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ISPRS Int. J. Geo-Inf., Volume 6, Issue 11 (November 2017)

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Research

Open AccessArticle Spatio-Temporal Variability in a Turbid and Dynamic Tidal Estuarine Environment (Tasmania, Australia): An Assessment of MODIS Band 1 Reflectance
ISPRS Int. J. Geo-Inf. 2017, 6(11), 320; doi:10.3390/ijgi6110320
Received: 12 August 2017 / Revised: 9 October 2017 / Accepted: 16 October 2017 / Published: 25 October 2017
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Abstract
Patterns of turbidity in estuarine environments are linked to hydrodynamic processes. However, the linkage between patterns and processes remains poorly resolved due to the scarcity of data needed to resolve fine scale highly dynamic processes in tidal estuaries. The application of remote sensing
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Patterns of turbidity in estuarine environments are linked to hydrodynamic processes. However, the linkage between patterns and processes remains poorly resolved due to the scarcity of data needed to resolve fine scale highly dynamic processes in tidal estuaries. The application of remote sensing technology to monitor dynamic coastal areas such as estuaries offers important advantages in this regard, by providing synoptic maps of larger, constantly changing regions over consistent periods. In situ turbidity measurements were correlated against the Moderate Resolution Imaging Spectrometer Terra sensor 250 m surface reflectance product, in order to assess this product for examining the complex estuarine waters of the Tamar estuary (Australia). Satellite images were averaged to examine spatial, seasonal and annual patterns of turbidity. Relationships between in situ measurements of turbidity and reflectance is positively correlated and improves with increased tidal height, a decreased overpass-in situ gap, and one day after a rainfall event. Spatial and seasonal patterns that appear in seasonal and annual MODIS averages, highlighting the usefulness of satellite imagery for resource managers to manage sedimentation issues in a degraded estuary. Full article
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Open AccessArticle Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes
ISPRS Int. J. Geo-Inf. 2017, 6(11), 321; doi:10.3390/ijgi6110321
Received: 17 August 2017 / Revised: 6 October 2017 / Accepted: 24 October 2017 / Published: 26 October 2017
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Abstract
Good characterization of traffic interactions among urban roads can facilitate traffic-related applications, such as traffic control and short-term forecasting. Most studies measure the traffic interaction between two roads by their topological distance or the correlation between their traffic variables. However, the distance-based methods
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Good characterization of traffic interactions among urban roads can facilitate traffic-related applications, such as traffic control and short-term forecasting. Most studies measure the traffic interaction between two roads by their topological distance or the correlation between their traffic variables. However, the distance-based methods neglect the spatial heterogeneity of roads’ traffic interactions, while the correlation-based methods cannot capture the non-linear dependency between two roads’ traffic variables. In this paper, we propose a novel approach called Road2Vec to quantify the implicit traffic interactions among roads based on large-scale taxi operating route data using a Word2Vec model from the natural language processing (NLP) field. First, the analogy between transportation elements (i.e., road segment, travel route) and NLP terms (i.e., word, document) is established. Second, the real-valued vectors for road segments are trained from massive travel routes using the Word2Vec model. Third, the traffic interaction between any pair of roads is measured by the cosine similarity of their vectors. A case study on short-term traffic forecasting is conducted with artificial neural network (ANN) and support vector machine (SVM) algorithms to validate the advantages of the presented method. The results show that the forecasting achieves a higher accuracy with the support of the Road2Vec method than with the topological distance and traffic correlation based methods. We argue that the Road2Vec method can be effectively utilized for quantifying complex traffic interactions among roads and capturing underlying heterogeneous and non-linear properties. Full article
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Open AccessArticle The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
ISPRS Int. J. Geo-Inf. 2017, 6(11), 322; doi:10.3390/ijgi6110322
Received: 11 August 2017 / Revised: 12 October 2017 / Accepted: 24 October 2017 / Published: 26 October 2017
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Abstract
This paper addresses the problem of processing range monitoring queries, each of which continuously retrieves moving objects that are currently located within a given query range. In particular, this paper focuses on processing range monitoring queries in the road network, where movements of
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This paper addresses the problem of processing range monitoring queries, each of which continuously retrieves moving objects that are currently located within a given query range. In particular, this paper focuses on processing range monitoring queries in the road network, where movements of the objects are constrained by a predefined set of paths. One of the most important challenges of processing range monitoring queries is how to minimize the wireless communication cost and the server computation cost, both of which are heavily dependent on the amount of location-update stream generated by moving objects. The traditional centralized methods for range monitoring queries assume that moving objects periodically send location-updates to the server. However, when the number of moving objects becomes increasingly large, such an assumption may no longer be acceptable because the amount of location-update stream becomes enormous. Recently, some distributed methods have been proposed, where moving objects utilize their available computational capabilities for sending location-updates to the server only when necessary. Unfortunately, the existing distributed methods only deal with the objects moving in Euclidean space, and thus they cannot be extended to processing range monitoring queries over the objects moving along the road network. In this paper, we propose the distributed method for processing range monitoring queries in the road network. To utilize the computational capabilities of moving objects, we introduce the concept of vicinity region. A vicinity region, assigned to each moving object o, makes o monitor whether or not it should be included in the results of nearby queries. The proposed method includes (i) a new spatial index structure, called the Segment-based Space Partitioning tree (SSP-tree) whose role is to efficiently search the appropriate vicinity regions for moving objects based on their heterogeneous computational capabilities and (ii) the details of the communication strategy between the server and moving objects, which significantly reduce the wireless communication cost as well as the server computation cost. Through simulations, we verify the effectiveness for processing range monitoring queries over a large number of moving objects (up to 100,000) in the road network (modeled as an undirected graph). Full article
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Open AccessArticle Globally Consistent Indoor Mapping via a Decoupling Rotation and Translation Algorithm Applied to RGB-D Camera Output
ISPRS Int. J. Geo-Inf. 2017, 6(11), 323; doi:10.3390/ijgi6110323
Received: 7 August 2017 / Revised: 16 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
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Abstract
This paper presents a novel RGB-D 3D reconstruction algorithm for the indoor environment. The method can produce globally-consistent 3D maps for potential GIS applications. As the consumer RGB-D camera provides a noisy depth image, the proposed algorithm decouples the rotation and translation for
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This paper presents a novel RGB-D 3D reconstruction algorithm for the indoor environment. The method can produce globally-consistent 3D maps for potential GIS applications. As the consumer RGB-D camera provides a noisy depth image, the proposed algorithm decouples the rotation and translation for a more robust camera pose estimation, which makes full use of the information, but also prevents inaccuracies caused by noisy depth measurements. The uncertainty in the image depth is not only related to the camera device, but also the environment; hence, a novel uncertainty model for depth measurements was developed using Gaussian mixture applied to multi-windows. The plane features in the indoor environment contain valuable information about the global structure, which can guide the convergence of camera pose solutions, and plane and feature point constraints are incorporated in the proposed optimization framework. The proposed method was validated using publicly-available RGB-D benchmarks and obtained good quality trajectory and 3D models, which are difficult for traditional 3D reconstruction algorithms. Full article
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Open AccessArticle Development of a Safety Index to Identify Differences in Safety Performance by Postal Delivery Motorcyclists Based either in Different Regional Post Offices or within the Same Regional Office
ISPRS Int. J. Geo-Inf. 2017, 6(11), 324; doi:10.3390/ijgi6110324
Received: 24 August 2017 / Revised: 12 October 2017 / Accepted: 25 October 2017 / Published: 27 October 2017
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Abstract
Postal motorcyclists who regularly conduct deliveries are particularly vulnerable to road accidents since they are exposed to traffic throughout their work day. To reduce accident rates, safety officers in each of the local delivery offices alert postmen of any hazardous conditions that may
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Postal motorcyclists who regularly conduct deliveries are particularly vulnerable to road accidents since they are exposed to traffic throughout their work day. To reduce accident rates, safety officers in each of the local delivery offices alert postmen of any hazardous conditions that may be conducive to accidents. Although some commercial postal organizations already use tracking technologies (e.g., GPS), Korea Post currently has no systematic way to collect their postmen’s driving behavior except by referring to each postman’s manually recorded daily mileage. In light of this, we developed a safety index (SI) for quantifying and analyzing individual postal motorcyclists’ safety performance based on their driving behavior and work environment. Each postman’s work environment varies from post office to post office and postman to postman depending on delivery conditions. After creating a GPS based system that can be installed on personal digital assistants (PDAs) that are already used by postmen throughout their shifts, we conducted two phases of field tests during a two-year period involving postmen working in different demographic areas. Using the collected field data, we validated our developed SI and analyzed whether there were any differences in the safety performance among postal motorcyclists working in different regional post offices or within the same regional post office. We found that the safety performance of postal motorcyclists working in different regional delivery offices varied depending on the regional characteristics of the local delivery office (e.g., densely distributed delivery points vs. loosely distributed delivery points). We also found that the safety performance of postal motorcyclists working in the same regional post office varied depending on the specific circumstances of each delivery area (e.g., short commuting routes of the postman responsible for downtown vs. long commuting routes of the postman responsible for a suburb). Full article
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Open AccessArticle “Voting with Their Feet”: Delineating the Sphere of Influence Using Social Media Data
ISPRS Int. J. Geo-Inf. 2017, 6(11), 325; doi:10.3390/ijgi6110325
Received: 28 July 2017 / Revised: 15 September 2017 / Accepted: 25 October 2017 / Published: 29 October 2017
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Abstract
Delineating regional boundaries for places has a long tradition in geography, urban analysis and regional planning. Its theoretical basis may be traced back to the central place theory. The normative approach, using spatial interaction models, has been used, and the empirical approach, using
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Delineating regional boundaries for places has a long tradition in geography, urban analysis and regional planning. Its theoretical basis may be traced back to the central place theory. The normative approach, using spatial interaction models, has been used, and the empirical approach, using commuting data, is also popular. While gathering commuting data using traditional methodologies (e.g., surveys) is costly, data capturing people’s locations and their thoughts, are widely available through social media platforms. This article demonstrates that Twitter data can be used to delineate boundaries among competing places. A generic approach based on the density of place names mentioned in geo-tagged tweets was proposed to reflect the sphere of influence or dominance of places. Locations with the same levels of influence from competing places constitute the boundaries delineating the regions dominated by the respective places. The method was tested to determine the boundaries between two metropolitan regions, two local cities, and two neighborhoods or communities. Results from these simple case studies demonstrated the validity of the general approach for evaluating existing place boundaries and determining boundaries if they have not been delineated. The method is applicable to different levels of the place hierarchy and has practical values for planning of places of different sizes. Full article
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Open AccessArticle An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold
ISPRS Int. J. Geo-Inf. 2017, 6(11), 326; doi:10.3390/ijgi6110326
Received: 11 September 2017 / Revised: 2 October 2017 / Accepted: 25 October 2017 / Published: 30 October 2017
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Abstract
The processing and analysis of trajectories are the core of many location-based applications and services, while trajectory similarity is an essential concept regularly used. To address the time-consuming problem of similarity query, an efficient algorithm based on Fréchet distance called Ordered Coverage Judge
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The processing and analysis of trajectories are the core of many location-based applications and services, while trajectory similarity is an essential concept regularly used. To address the time-consuming problem of similarity query, an efficient algorithm based on Fréchet distance called Ordered Coverage Judge (OCJ) is proposed, which could realize the filtering query with a given Fréchet distance threshold on large-scale trajectory datasets. The OCJ algorithm can obtain the result set quickly by a two-step operation containing morphological characteristic filtering and ordered coverage judgment. The algorithm is expedient to be implemented in parallel for further increases of speed. Demonstrated by experiments over real trajectory data in a multi-core hardware environment, the new algorithm shows favorable stability and scalability besides its higher efficiency in comparison with traditional serial algorithms and other Fréchet distance algorithms. Full article
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Open AccessArticle Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning
ISPRS Int. J. Geo-Inf. 2017, 6(11), 327; doi:10.3390/ijgi6110327
Received: 13 August 2017 / Revised: 28 September 2017 / Accepted: 24 October 2017 / Published: 30 October 2017
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Abstract
Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a
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Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data. Full article
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Open AccessArticle Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment
ISPRS Int. J. Geo-Inf. 2017, 6(11), 328; doi:10.3390/ijgi6110328
Received: 1 September 2017 / Revised: 30 September 2017 / Accepted: 24 October 2017 / Published: 30 October 2017
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Abstract
Structure from Motion with Multi-View Stereo photogrammetry (SfM-MVS) is increasingly used in geoscience investigations, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via Unmanned Aerial
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Structure from Motion with Multi-View Stereo photogrammetry (SfM-MVS) is increasingly used in geoscience investigations, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via Unmanned Aerial Vehicle, ‘UAV’) and ground-based (via handheld digital camera, ‘ground’) SfM-MVS in modelling hillslope gully systems in a dry-tropical savanna, and to assess the strengths and limitations of the approach at a hillslope scale and an individual gully scale. UAV surveys covered three separate hillslope gully systems (with areas of 0.412–0.715 km2), while ground surveys assessed individual gullies within the broader systems (with areas of 350–750 m2). SfM-MVS topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate that UAV SfM-MVS can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., approximately 0.1 m resolution with 0.4–1.2 m elevation error), while ground-based SfM-MVS is more capable of quantifying gully morphology (e.g., approximately 0.01 m resolution with 0.04–0.1 m elevation error). Despite difficulties in reconstructing vegetated surfaces, uncertainty as to optimal survey and processing designs, and high computational demands, this study has demonstrated great potential for SfM-MVS to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in savanna landscapes and elsewhere. Full article
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Open AccessArticle Simplifying GPS Trajectory Data with Enhanced Spatial-Temporal Constraints
ISPRS Int. J. Geo-Inf. 2017, 6(11), 329; doi:10.3390/ijgi6110329
Received: 27 July 2017 / Revised: 9 October 2017 / Accepted: 24 October 2017 / Published: 30 October 2017
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Abstract
Raw GPS trajectory data are often very large and use up excessive storage space. The efficiency and accuracy of activity patterns analysis or individual–environment interaction modeling using such data may be compromised due to data size and computational needs. Line generalization algorithms may
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Raw GPS trajectory data are often very large and use up excessive storage space. The efficiency and accuracy of activity patterns analysis or individual–environment interaction modeling using such data may be compromised due to data size and computational needs. Line generalization algorithms may be used to simplify GPS trajectories. However, traditional algorithms focus on geometric characteristics of linear features. Trajectory data may record information beyond location. Examples include time and elevation, and inferred information such as speed, transportation mode, and activities. Effective trajectory simplification should preserve these characteristics in addition to location and orientation of spatial-temporal movement. This paper proposes an Enhanced Douglas–Peucker (EDP) algorithm that implements a set of Enhanced Spatial-Temporal Constraints (ESTC) when simplifying trajectory data. These constraints ensure that the essential properties of a trajectory be preserved through preserving critical points. Further, this study argues that speed profile can uniquely identify a trajectory and thus it can be used to evaluate the effectiveness of a trajectory simplification. The proposed ESTC-EDP simplification method is applied to two examples of GPS trajectory. The results of trajectory simplification are reported and compared with that from traditional DP algorithm. The effectiveness of simplification is evaluated. Full article
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Open AccessArticle Aircraft Reconstruction in High-Resolution SAR Images Using Deep Shape Prior
ISPRS Int. J. Geo-Inf. 2017, 6(11), 330; doi:10.3390/ijgi6110330
Received: 8 September 2017 / Revised: 9 October 2017 / Accepted: 24 October 2017 / Published: 31 October 2017
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Abstract
Contour shapes are important features. An accurate contour shape of a target can provide important prior information for applications, such as target recognition, which can improve the accuracy of target interpretation. In this paper, a synthetic aperture radar (SAR) target reconstruction method is
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Contour shapes are important features. An accurate contour shape of a target can provide important prior information for applications, such as target recognition, which can improve the accuracy of target interpretation. In this paper, a synthetic aperture radar (SAR) target reconstruction method is proposed, which can be used to reconstruct the target by using shape priors to perform an accurate extraction of the contour shape feature. The method is divided into two stages. In the deep shape prior extraction stage, a generative deep learning modelling method is used to obtain deep shape priors. In the reconstruction stage, a novel coarse-to-fine pose estimation method combined with an optimization algorithm is proposed, which integrates deep shape priors into the process of reconstruction. Specifically, to address the issue of object rotation, candidate poses are obtained using the coarse pose estimation method, which avoids an exhaustive search of each pose. In addition, an energy function composed of a scattering term and shape term to combine the fine pose estimation, is constructed and optimized via an iterative optimization algorithm to achieve the goal of object reconstruction. To the best of our knowledge, this is the first time shape priors have been used to extract shape features of SAR targets. Experiments are conducted on a data set acquired by TerraSAR-X images and the results demonstrate the high accuracy and robustness of the proposed method. Full article
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Open AccessArticle A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China
ISPRS Int. J. Geo-Inf. 2017, 6(11), 331; doi:10.3390/ijgi6110331
Received: 13 August 2017 / Revised: 1 October 2017 / Accepted: 26 October 2017 / Published: 31 October 2017
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Abstract
Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification
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Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification (FBA-DTC) method for mapping LULC based on spectral and topographic information. Landsat 5 TM and Land 8 OLI images were employed, and the technique was applied to the coastal city of Xiamen, China. The method integrates multi-spectral features such as SAVI (soil adjustment vegetation index), NDWI (normalized water index), MNDBaI (modified normalized difference barren index), BI (brightness index), and WI (wetness index), with topographic features including DEM and slope. In addition, the new approach distinguishes between fallow land and cropland, and separates high-rise buildings from beaches and water bodies. Several of the FBA-DTC parameters (or rules) from 1997 to 2015 remained constant (i.e., DEM and slope), whereas others changed slightly. WI was negatively related to percent area of beach land, and BI was negatively related to percent area of arable land. The FBA-DTC method had an average user’s accuracy (UA) of 91.36% for built-up land, an average overall accuracy (OA) of 92.13%, and a Kappa coefficient (KC) of 0.90 for the period from 2003 to 2015, representing respective increases of 15.87%, 10.17%, and 0.13, compared with values calculated using maximum likelihood classification (MLC). Over the past 12 years, built-up land increased from 23.67% to 43.17% owing to occupation of coastal reclamation, arable land, and forest land. The FBA-DTC method presented here is a valuable technique for evaluating urban growth and changes in LULC classification for coastal cities. Full article
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Open AccessArticle Bibliometric Analysis of Global Remote Sensing Research during 2010–2015
ISPRS Int. J. Geo-Inf. 2017, 6(11), 332; doi:10.3390/ijgi6110332
Received: 30 August 2017 / Revised: 18 October 2017 / Accepted: 26 October 2017 / Published: 1 November 2017
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Abstract
Bibliometric analysis based on the Science Citation Index Expanded published by Thomson Scientific was carried out to identify the research status and future trends of remote sensing (RS) during 2010–2015. The analysis revealed the institutional, national, spatio-temporal, and categorical patterns in remote sensing
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Bibliometric analysis based on the Science Citation Index Expanded published by Thomson Scientific was carried out to identify the research status and future trends of remote sensing (RS) during 2010–2015. The analysis revealed the institutional, national, spatio-temporal, and categorical patterns in remote sensing research both from the WP (whole publications) viewpoint and the HCP (highly-cited publications) viewpoint. Statistical analysis results showed that remote sensing research almost doubled during 2010–2015. Environmental sciences comprised the most attractive subject category among remote sensing research. The International Journal of Remote Sensing was the most productive journal, and Remote Sensing of Environment published the most HCP among the 31 distributed journals. The productive ranking of countries was led by the U.S. both from the WP viewpoint and the HCP viewpoint, and CAS (Chinese Academy of Sciences) was the most productive institute both from the WP viewpoint and the HCP viewpoint with lower CPP (average number of citations per paper). Keyword analysis illustrated that model and algorithm research were the key points in RS during 2010–2015. RS data including Moderate-Resolution Imaging Spectroradiometer (MODIS), Landsat, synthetic aperture radar (SAR), and LiDAR (light detection and ranging) were the most frequently adopted, but the data usage of UAVs (unmanned aerial vehicles) and small satellites will be promoted in the future. With the development of data acquisition abilities, big data issues will become the challenges and hotspots of RS research, and new algorithms will continue to emerge. Full article
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Open AccessArticle Surveillance Video Synopsis in GIS
ISPRS Int. J. Geo-Inf. 2017, 6(11), 333; doi:10.3390/ijgi6110333
Received: 14 August 2017 / Revised: 23 October 2017 / Accepted: 26 October 2017 / Published: 31 October 2017
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Abstract
Surveillance videos contain a considerable amount of data, wherein interesting information to the user is sparsely distributed. Researchers construct video synopsis that contain key information extracted from a surveillance video for efficient browsing and analysis. Geospatial–temporal information of a surveillance video plays an
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Surveillance videos contain a considerable amount of data, wherein interesting information to the user is sparsely distributed. Researchers construct video synopsis that contain key information extracted from a surveillance video for efficient browsing and analysis. Geospatial–temporal information of a surveillance video plays an important role in the efficient description of video content. Meanwhile, current approaches of video synopsis lack the introduction and analysis of geospatial-temporal information. Owing to the preceding problems mentioned, this paper proposes an approach called “surveillance video synopsis in GIS”. Based on an integration model of video moving objects and GIS, the virtual visual field and the expression model of the moving object are constructed by spatially locating and clustering the trajectory of the moving object. The subgraphs of the moving object are reconstructed frame by frame in a virtual scene. Results show that the approach described in this paper comprehensively analyzed and created fusion expression patterns between video dynamic information and geospatial–temporal information in GIS and reduced the playback time of video content. Full article
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Open AccessArticle Detection of Moving Ships in Sequences of Remote Sensing Images
ISPRS Int. J. Geo-Inf. 2017, 6(11), 334; doi:10.3390/ijgi6110334
Received: 25 August 2017 / Revised: 27 September 2017 / Accepted: 30 October 2017 / Published: 1 November 2017
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Abstract
High-speed agile remote sensing satellites have the ability to capture multiple sequences of images. However, the frame rate is lower and the baseline between each image is much longer than normal image sequences. As a result, the edges and shadows in each image
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High-speed agile remote sensing satellites have the ability to capture multiple sequences of images. However, the frame rate is lower and the baseline between each image is much longer than normal image sequences. As a result, the edges and shadows in each image in the sequence vary considerably. Therefore, more requirements are placed on the target detection algorithm. Aiming at the characteristics of multi-view image sequences, we propose an approach to detect moving ships on the water surface. Based on marker controlled watershed segmentation, we use the extracted foreground and background images to segment moving ships, and we obtain the complete shape and texture information of the ships. The inter-frame difference algorithm is applied to extract the foreground object information, while Otsu’s algorithm is used to extract the image background. The foreground and background information is fused to solve the problem of interference with object detection caused by long imaging baseline. The experimental results show that the proposed method is effective for moving ship detection. Full article
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Open AccessArticle Spatial Analysis of Linear Structures in the Exploration of Groundwater
ISPRS Int. J. Geo-Inf. 2017, 6(11), 335; doi:10.3390/ijgi6110335
Received: 21 September 2017 / Revised: 18 October 2017 / Accepted: 25 October 2017 / Published: 2 November 2017
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Abstract
The analysis of linear structures on major geological formations plays a crucial role in resource exploration in the Inner Niger Delta. Highlighting and mapping of the large lithological units were carried out using image fusion, spectral bands (RGB coding), Principal Component Analysis (PCA),
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The analysis of linear structures on major geological formations plays a crucial role in resource exploration in the Inner Niger Delta. Highlighting and mapping of the large lithological units were carried out using image fusion, spectral bands (RGB coding), Principal Component Analysis (PCA), and band ratio methods. The automatic extraction method of linear structures has permitted the obtaining of a structural map with 82,659 linear structures, distributed on different stratigraphic stages. The intensity study shows an accentuation in density over 12.52% of the total area, containing 22.02% of the linear structures. The density and nodes (intersections of fractures) formed by the linear structures on the different lithologies allowed to observe the behavior of the region’s aquifers in the exploration of subsoil resources. The central density, in relation to the hydrographic network of the lowlands, shows the conditioning of the flow and retention of groundwater in the region, and in-depth fluids. The node areas and high-density linear structures, have shown an ability to have rejections in deep (pores) that favor the formation of structural traps for oil resources. Full article
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Open AccessArticle Mixture Statistical Distribution Based Multiple Component Model for Target Detection in High Resolution SAR Imagery
ISPRS Int. J. Geo-Inf. 2017, 6(11), 336; doi:10.3390/ijgi6110336
Received: 24 August 2017 / Revised: 29 September 2017 / Accepted: 30 October 2017 / Published: 2 November 2017
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Abstract
This paper proposes an innovative Mixture Statistical Distribution Based Multiple Component (MSDMC) model for target detection in high spatial resolution Synthetic Aperture Radar (SAR) images. Traditional detection algorithms usually ignore the spatial relationship among the target’s components. In the presented method, however, both
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This paper proposes an innovative Mixture Statistical Distribution Based Multiple Component (MSDMC) model for target detection in high spatial resolution Synthetic Aperture Radar (SAR) images. Traditional detection algorithms usually ignore the spatial relationship among the target’s components. In the presented method, however, both the structural information and the statistical distribution are considered to better recognize the target. Firstly, the method based on compressed sensing reconstruction is used to recover the SAR image. Then, the multiple component model composed of a root filter and some corresponding part filters is applied to describe the structural information of the target. In the following step, mixture statistical distributions are utilised to discriminate the target from the background, and the Method of Logarithmic Cumulants (MoLC) based Expectation Maximization (EM) approach is adopted to estimate the parameters of the mixture statistical distribution model, which will be finally merged into the proposed MSDMC framework together with the multiple component model. In the experiment, the aeroplanes and the electrical power towers in TerraSAR-X SAR images are detected at three spatial resolutions. The results indicate that the presented MSDMC Model has potential for improving the detection performance compared with the state-of-the-art SAR target detection methods. Full article
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Open AccessArticle Near-Real-Time OGC Catalogue Service for Geoscience Big Data
ISPRS Int. J. Geo-Inf. 2017, 6(11), 337; doi:10.3390/ijgi6110337
Received: 3 July 2017 / Revised: 26 October 2017 / Accepted: 26 October 2017 / Published: 2 November 2017
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Abstract
Geoscience data are typically big data, and they are distributed in various agencies and individuals worldwide. Efficient data sharing and interoperability are important for managing and applying geoscience data. The OGC (Open Geospatial Consortium) Catalogue Service for the Web (CSW) is an open
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Geoscience data are typically big data, and they are distributed in various agencies and individuals worldwide. Efficient data sharing and interoperability are important for managing and applying geoscience data. The OGC (Open Geospatial Consortium) Catalogue Service for the Web (CSW) is an open interoperability standard for supporting the discovery of geospatial data. In the past, regular OGC catalogue services have been studied, but few studies have discussed a near-real-time OGC catalogue service for geoscience big data. A near-real-time OGC catalogue service requires frequent updates of a metadata repository in a short time. When dealing with massive amounts of geoscience data, this comprises an extremely challenging issue. Discovering these data via an OGC catalogue service in near real-time is desirable. In this study, we focus on how the near-real-time OGC catalogue service is realized through several lightweight data structures, algorithms, and tools. We propose a framework of a near-real-time OGC catalogue service and discuss each element of the framework to which more attention should be paid when dealing with the massive amounts of real-time data, followed by a review of several methods that need to be considered in a near-real-time OGC CSW service. A case study on providing an OGC catalogue service to Unidata real-time data is presented to demonstrate how specific methods are utilized to deal with real-time data. The goal of this paper is to fill the gap in knowledge regarding an OGC catalogue service for geoscience big data, and it has realistic significance in facilitating a near-real-time OGC catalogue service. Full article
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Open AccessArticle An Index Based on Joint Density of Corners and Line Segments for Built-Up Area Detection from High Resolution Satellite Imagery
ISPRS Int. J. Geo-Inf. 2017, 6(11), 338; doi:10.3390/ijgi6110338
Received: 25 September 2017 / Revised: 13 October 2017 / Accepted: 1 November 2017 / Published: 2 November 2017
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Abstract
Detection of built-up areas from Very High Spatial Resolution (VHSR) remote sensing images is a critical step in urbanization monitoring. This paper presents a method for extracting built-up areas from VHSR remote sensing imagery by using feature-level-based fusion of right angle corners, right
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Detection of built-up areas from Very High Spatial Resolution (VHSR) remote sensing images is a critical step in urbanization monitoring. This paper presents a method for extracting built-up areas from VHSR remote sensing imagery by using feature-level-based fusion of right angle corners, right angle sides and road marks. This method has six main steps. First, line segments are detected. Second, the Harris corner points are detected. Third, the right-angle corners and right-angle sides are determined by cross-verification of the above detected Harris corners and line segments. Fourth, the potential road marks are detected by the template matching method. Fifth, a built-up index image is constructed. Finally, the built-up areas are extracted through a binary thresholding of the above index image. Three satellite images with wide coverage are employed for evaluating the above proposed method. The experimental results suggest that the proposed method outperforms the classic PanTex method. On average, the completeness and the quality of the proposed method are respectively 17.94% and 13.33% better than those of the PanTex method, while there is no great difference between the two methods on the correctness. Full article
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Open AccessArticle Exploring Spatiotemporal Patterns of Long-Distance Taxi Rides in Shanghai
ISPRS Int. J. Geo-Inf. 2017, 6(11), 339; doi:10.3390/ijgi6110339
Received: 31 August 2017 / Revised: 2 October 2017 / Accepted: 26 October 2017 / Published: 3 November 2017
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Abstract
Floating Car Data (FCD) has been analyzed for various purposes in past years. However, limited research about the behaviors of taking long-distance taxi rides has been made available. In this paper, we used data from over 12,000 taxis during a six-month period in
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Floating Car Data (FCD) has been analyzed for various purposes in past years. However, limited research about the behaviors of taking long-distance taxi rides has been made available. In this paper, we used data from over 12,000 taxis during a six-month period in Shanghai to analyze the spatiotemporal patterns of long-distance taxi trips. We investigated these spatiotemporal patterns by comparing them with metro usage in Shanghai, in order to determine the extent and how the suburban trains divert the passenger flow from taxis. The results identified 12 pick-up and six drop-off hotspots in Shanghai. Overall, the pick-up locations were relatively more concentrated than the drop-off locations. Temporal patterns were also revealed. Passengers on long-distance taxi rides were observed to avoid the rush hours on the street as their first priority and tried to avoid the inconvenience of interchanges on the metro lines as their second priority. Full article
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Open AccessArticle Contributors’ Withdrawal from Online Collaborative Communities: The Case of OpenStreetMap
ISPRS Int. J. Geo-Inf. 2017, 6(11), 340; doi:10.3390/ijgi6110340
Received: 1 September 2017 / Revised: 20 October 2017 / Accepted: 2 November 2017 / Published: 4 November 2017
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Abstract
Online collaborative communities are now ubiquitous. Identifying the nature of the events that drive contributors to withdraw from a project is of prime importance to ensure the sustainability of those communities. Previous studies used ad hoc criteria to identify withdrawn contributors, preventing comparisons
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Online collaborative communities are now ubiquitous. Identifying the nature of the events that drive contributors to withdraw from a project is of prime importance to ensure the sustainability of those communities. Previous studies used ad hoc criteria to identify withdrawn contributors, preventing comparisons between results and introducing interpretation biases. This paper compares different methods to identify withdrawn contributors, proposing a probabilistic approach. Withdrawals from the OpenStreetMap (OSM) community are investigated using time series and survival analyses. Survival analysis revealed that participants’ withdrawal pattern compares with the life cycles studied in reliability engineering. For OSM contributors, this life cycle would translate into three phases: “evaluation,” “engagement” and “detachment.” Time series analysis, when compared with the different events that may have affected the motivation of OSM participants over time, showed that an internal conflict about a license change was related to largest bursts of withdrawals in the history of the OSM project. This paper not only illustrates a formal approach to assess withdrawals from online communities, but also sheds new light on contributors’ behavior, their life cycle, and events that may affect the length of their participation in such project. Full article
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Open AccessArticle Understanding the Functionality of Human Activity Hotspots from Their Scaling Pattern Using Trajectory Data
ISPRS Int. J. Geo-Inf. 2017, 6(11), 341; doi:10.3390/ijgi6110341
Received: 2 September 2017 / Revised: 26 October 2017 / Accepted: 2 November 2017 / Published: 5 November 2017
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Abstract
Human activity hotspots are the clusters of activity locations in space and time, and a better understanding of their functionality would be useful for urban land use planning and transportation. In this article, using trajectory data, we aim to infer the functionality of
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Human activity hotspots are the clusters of activity locations in space and time, and a better understanding of their functionality would be useful for urban land use planning and transportation. In this article, using trajectory data, we aim to infer the functionality of human activity hotspots from their scaling pattern in a reliable way. Specifically, a large number of stopping locations are extracted from trajectory data, which are then aggregated into activity hotspots. Activity hotspots are found to display scaling patterns in terms of the sublinear scaling relationships between the number of stopping locations and the number of points of interest (POIs), which indicates economies of scale of human interactions with urban land use. Importantly, this scaling pattern remains stable over time. This finding inspires us to devise an allometric ruler to identify the activity hotspots, whose functionality could be reliably estimated using the stopping locations. Thereafter, a novel Bayesian inference model is proposed to infer their urban functionality, which examines the spatial and temporal information of stopping locations covering 75 days. Experimental results suggest that the functionality of identified activity hotspots are reliably inferred by stopping locations, such as the railway station. Full article
(This article belongs to the Special Issue Place-Based Research in GIScience and Geoinformatics)
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Open AccessArticle Beyond Maximum Independent Set: An Extended Integer Programming Formulation for Point Labeling
ISPRS Int. J. Geo-Inf. 2017, 6(11), 342; doi:10.3390/ijgi6110342
Received: 6 September 2017 / Revised: 20 October 2017 / Accepted: 25 October 2017 / Published: 6 November 2017
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Abstract
Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in a map and for each feature a set of label candidates, a common problem is to select an independent set of
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Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in a map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label–label intersections) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, the influences of labels on other labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate both for randomly generated point sets and an existing data set of cities. Moreover, a relaxation of our ILP allows for a simple and efficient heuristic, which yielded near-optimal solutions for our instances. Full article
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Open AccessArticle Hybrid Spatial Data Model for Indoor Space: Combined Topology and Grid
ISPRS Int. J. Geo-Inf. 2017, 6(11), 343; doi:10.3390/ijgi6110343
Received: 28 August 2017 / Revised: 3 October 2017 / Accepted: 3 November 2017 / Published: 6 November 2017
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Abstract
The construction and application of an indoor spatial data model is an important prerequisite to meet the requirements of diversified indoor spatial location services. The traditional indoor spatial topology model focuses on the construction of topology information. It has high path analysis and
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The construction and application of an indoor spatial data model is an important prerequisite to meet the requirements of diversified indoor spatial location services. The traditional indoor spatial topology model focuses on the construction of topology information. It has high path analysis and query efficiency, but ignores the spatial location information. The grid model retains the plane position information by grid, but increases the data volume and complexity of the model and reduces the efficiency of the model analysis. This paper presents a hybrid model for interior space based on topology and grid. Based on the spatial meshing and spatial division of the interior space, the model retains the position information and topological connectivity information of the interior space by establishing the connection or affiliation between the grid subspace and the topological subspace. The model improves the speed of interior spatial analysis and solves the problem of the topology information and location information updates not being synchronized. In this study, the A* shortest path query efficiency of typical daily indoor activities under the grid model and the hybrid model were compared for the indoor plane of an apartment and a shopping mall. The results obtained show that the hybrid model is 43% higher than the A* algorithm of the grid model as a result of the existence of topology communication information. This paper provides a useful idea for the establishment of a highly efficient and highly available interior spatial data model. Full article
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Open AccessArticle Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares
ISPRS Int. J. Geo-Inf. 2017, 6(11), 344; doi:10.3390/ijgi6110344
Received: 19 September 2017 / Revised: 26 October 2017 / Accepted: 3 November 2017 / Published: 6 November 2017
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Abstract
As a widely used classifier, sparse representation classification (SRC) has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the
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As a widely used classifier, sparse representation classification (SRC) has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., 2 -norm) can be used to regularize the coding coefficients, except for the sparsity 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS) for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1) the coefficient-level regularization strategy, and (2) the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework. Full article
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Open AccessArticle Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr
ISPRS Int. J. Geo-Inf. 2017, 6(11), 345; doi:10.3390/ijgi6110345
Received: 9 September 2017 / Revised: 4 October 2017 / Accepted: 2 November 2017 / Published: 7 November 2017
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Abstract
With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged
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With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platforms like Flickr have provided a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists and then extends the concept of motifs from topological spaces, to temporal spaces and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns. Full article
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Open AccessArticle Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM
ISPRS Int. J. Geo-Inf. 2017, 6(11), 346; doi:10.3390/ijgi6110346
Received: 22 August 2017 / Revised: 26 October 2017 / Accepted: 2 November 2017 / Published: 7 November 2017
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Abstract
Coral walls protect vegetation gardens from strong winds that sweep across Xiji Island, Taiwan Strait for half the year. Topographic parameters based on light detection and ranging (LiDAR)-based high-resolution digital elevation model (DEM) provide obvious correspondence with the expected form of landscape features.
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Coral walls protect vegetation gardens from strong winds that sweep across Xiji Island, Taiwan Strait for half the year. Topographic parameters based on light detection and ranging (LiDAR)-based high-resolution digital elevation model (DEM) provide obvious correspondence with the expected form of landscape features. The information on slope, curvature, and openness can help identify the location of landscape features. This study applied the automatic landscape line detection to extract historic vegetable garden wall lines from a LiDAR-derived DEM. The three rapid processes used in this study included the derivation of topographic parameters, line extraction, and aggregation. The rules were extracted from a decision tree to check the line detection from multiple topographic parameters. Results show that wall line detection with multiple topographic parameter images is an alternative means of obtaining essential historic wall feature information. Multiple topographic parameters are highly related to low wall feature identification. Furthermore, the accuracy of wall feature detection is 74% compared with manual interpretation. Thus, this study provides rapid wall detection systems with multiple topographic parameters for further historic landscape management. Full article
(This article belongs to the Special Issue Historic Settlement and Landscape Analysis)
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Open AccessArticle Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China
ISPRS Int. J. Geo-Inf. 2017, 6(11), 347; doi:10.3390/ijgi6110347
Received: 21 August 2017 / Revised: 31 October 2017 / Accepted: 3 November 2017 / Published: 7 November 2017
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Abstract
A landslide susceptibility map plays an essential role in urban and rural planning. The main purpose of this study is to establish a variable-weighted linear combination model (VWLC) and assess its potential for landslide susceptibility mapping. Firstly, different objective methods are employed for
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A landslide susceptibility map plays an essential role in urban and rural planning. The main purpose of this study is to establish a variable-weighted linear combination model (VWLC) and assess its potential for landslide susceptibility mapping. Firstly, different objective methods are employed for data processing rather than the frequently-used subjective judgments: K-means clustering is used for classification; binarization is introduced to determine buffer length thresholds for locational elements (road, river, and fault); landslide area density is adopted as the contribution index; and a correlation analysis is conducted for suitable factor selection. Secondly, considering the dimension changes of the preference matrix varying with the different locations of the mapping cells, the variable weights of each optimal factor are determined based on the improved analytic hierarchy process (AHP). On this basis, the VWLC model is established and applied to regional landslide susceptibility mapping for the Shennongjia Forestry District, China, where shallow landslides frequently occur. The obtained map is then compared with a map using the traditional WLC, and the results of the comparison show that VWLC is more reasonable, with a higher accuracy, and can be used anywhere that has the same or similar geological and topographical conditions. Full article
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Open AccessArticle A Novel Approach to Semantic Similarity Measurement Based on a Weighted Concept Lattice: Exemplifying Geo-Information
ISPRS Int. J. Geo-Inf. 2017, 6(11), 348; doi:10.3390/ijgi6110348
Received: 7 August 2017 / Revised: 30 October 2017 / Accepted: 3 November 2017 / Published: 7 November 2017
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Abstract
The measurement of semantic similarity has been widely recognized as having a fundamental and key role in information science and information systems. Although various models have been proposed to measure semantic similarity, these models are not able effectively to quantify the weights of
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The measurement of semantic similarity has been widely recognized as having a fundamental and key role in information science and information systems. Although various models have been proposed to measure semantic similarity, these models are not able effectively to quantify the weights of relevant factors that impact on the judgement of semantic similarity, such as the attributes of concepts, application context, and concept hierarchy. In this paper, we propose a novel approach that comprehensively considers the effects of various factors on semantic similarity judgment, which we name semantic similarity measurement based on a weighted concept lattice (SSMWCL). A feature model and network model are integrated together in SSMWCL. Based on the feature model, the combined weight of each attribute of the concepts is calculated by merging its information entropy and inclusion-degree importance in a specific application context. By establishing the weighted concept lattice, the relative hierarchical depths of concepts for comparison are computed according to the principle of the network model. The integration of feature model and network model enables SSMWCL to take account of differences in concepts more comprehensively in semantic similarity measurement. Additionally, a workflow of SSMWCL is designed to demonstrate these procedures and a case study of geo-information is conducted to assess the approach. Full article
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Open AccessArticle A Post-Rectification Approach of Depth Images of Kinect v2 for 3D Reconstruction of Indoor Scenes
ISPRS Int. J. Geo-Inf. 2017, 6(11), 349; doi:10.3390/ijgi6110349
Received: 22 August 2017 / Revised: 17 October 2017 / Accepted: 2 November 2017 / Published: 13 November 2017
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Abstract
3D reconstruction of indoor scenes is a hot research topic in computer vision. Reconstructing fast, low-cost, and accurate dense 3D maps of indoor scenes have applications in indoor robot positioning, navigation, and semantic mapping. In other studies, the Microsoft Kinect for Windows v2
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3D reconstruction of indoor scenes is a hot research topic in computer vision. Reconstructing fast, low-cost, and accurate dense 3D maps of indoor scenes have applications in indoor robot positioning, navigation, and semantic mapping. In other studies, the Microsoft Kinect for Windows v2 (Kinect v2) is utilized to complete this task, however, the accuracy and precision of depth information and the accuracy of correspondence between the RGB and depth (RGB-D) images still remain to be improved. In this paper, we propose a post-rectification approach of the depth images to improve the accuracy and precision of depth information. Firstly, we calibrate the Kinect v2 with a planar checkerboard pattern. Secondly, we propose a post-rectification approach of the depth images according to the reflectivity-related depth error. Finally, we conduct tests to evaluate this post-rectification approach from the perspectives of accuracy and precision. In order to validate the effect of our post-rectification approach, we apply it to RGB-D simultaneous localization and mapping (SLAM) in an indoor environment. Experimental results show that once our post-rectification approach is employed, the RGB-D SLAM system can perform a more accurate and better visual effect 3D reconstruction of indoor scenes than other state-of-the-art methods. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
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Open AccessArticle WebGIS and Geospatial Technologies for Landscape Education on Personalized Learning Contexts
ISPRS Int. J. Geo-Inf. 2017, 6(11), 350; doi:10.3390/ijgi6110350
Received: 24 August 2017 / Revised: 29 October 2017 / Accepted: 3 November 2017 / Published: 8 November 2017
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Abstract
The value of landscape, as part of collective heritage, can be acquired by geographic information systems (GIS) due to the multilayer approach of the spatial configuration. Proficiency in geospatial technologies to collect, process, analyze, interpret, visualize, and communicate geographic information is being increased
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The value of landscape, as part of collective heritage, can be acquired by geographic information systems (GIS) due to the multilayer approach of the spatial configuration. Proficiency in geospatial technologies to collect, process, analyze, interpret, visualize, and communicate geographic information is being increased by undergraduate and graduate students but, in particular, by those who are training to become geography teachers at the secondary education level. Some teaching experiences, using personalized learning, distance learning methodology, and GIS, focused on education aims to integrate students and enhance their understanding of the landscape are shown. Opportunities offered by WebGIS will be described, through quantitative tools and techniques that will allow this modality of learning and improve its effectiveness. Results of this research show that students, through geospatial technologies, learn the landscape as a diversity of elements, but also the complexity of physical and human factors involved. Several conclusions will be highlighted: (i) the contribution of geospatial training to education on the landscape and for sustainable development; (ii) spatial analysis as a means of skills acquisition regarding measures for landscape conservation; and (iii) expanding and applying acquired knowledge to other geographic spaces. Full article
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Open AccessArticle Working with Open BIM Standards to Source Legal Spaces for a 3D Cadastre
ISPRS Int. J. Geo-Inf. 2017, 6(11), 351; doi:10.3390/ijgi6110351
Received: 1 September 2017 / Revised: 1 October 2017 / Accepted: 16 October 2017 / Published: 7 November 2017
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Abstract
Much work has already been done on how a 3D Cadastre should best be developed. An inclusive information model, the Land Administration Model (LADM ISO 19152) has been developed to provide an international framework for how this can best be done. This conceptual
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Much work has already been done on how a 3D Cadastre should best be developed. An inclusive information model, the Land Administration Model (LADM ISO 19152) has been developed to provide an international framework for how this can best be done. This conceptual model does not prescribe the technical data format. One existing source from which data could be obtained is 3D Building Information Models (BIMs), or, more specifically in this context, BIMs in the form of one of buildingSMART’s open standards: the Industry Foundation Classes (IFC). The research followed a standard BIM methodology of first defining the requirements through the use of the Information Delivery Manual (IDM ISO29481) and then translating the process described in the IDM into technical requirements using a Model View Definition (MVD), a practice to coordinate upfront the multidisciplinary stakeholders of a construction project. The proposed process model illustrated how the time it takes to register 3D spatial units in a Land Registry could substantially be reduced compared to the first 3D registration in the Netherlands. The modelling of an MVD or a subset of the IFC data model helped enable the creation and exchange of boundary representations of topological objects capable of being combined into a 3D legal space overview map. Full article
(This article belongs to the Special Issue Research and Development Progress in 3D Cadastral Systems)
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Open AccessArticle GIS-Based Evaluation of Spatial Interactions by Geographic Disproportionality of Industrial Diversity
ISPRS Int. J. Geo-Inf. 2017, 6(11), 352; doi:10.3390/ijgi6110352
Received: 12 September 2017 / Revised: 31 October 2017 / Accepted: 6 November 2017 / Published: 8 November 2017
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Abstract
Diversity of regional industry is regarded as a key factor for regional development, as it has a positive relationship with economic stability, which attracts population. This paper focuses on how the spatial imbalance of industrial diversity contributes to the population change caused by
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Diversity of regional industry is regarded as a key factor for regional development, as it has a positive relationship with economic stability, which attracts population. This paper focuses on how the spatial imbalance of industrial diversity contributes to the population change caused by inter-regional migration. This paper introduces a spatial interaction model for the Geographic Information System (GIS)-based simulation of the spatial interactions to evaluate the demographic attraction force. The proposed model adopts the notions of gravity, entropy, and virtual work. An industrial classification by profit level is introduced and its diversity is quantified with the entropy of information theory. The introduced model is applied to the cases of 207 regions in South Korea. Spatial interactions are simulated with an optimized model and their resultant forces, the demographic attraction forces, are compared with observed net migration for verification. The results show that the evaluated attraction forces from industrial diversity have a very significant, positive, and moderate relationship with net migration, while other conventional factors of industry, population, economy, and the job market do not. This paper concludes that the geographical quality of industrial diversity has positive and significant effects on population change by migration. Full article
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Open AccessArticle Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain
ISPRS Int. J. Geo-Inf. 2017, 6(11), 353; doi:10.3390/ijgi6110353
Received: 4 September 2017 / Revised: 2 November 2017 / Accepted: 4 November 2017 / Published: 10 November 2017
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Abstract
In this study, we showed that the multi-angle satellite remote sensing product, MISR L2 Land Surface (MIL2ASLS), which has a scale of 1.1 km, could be suitable for improving land-cover studies. Using seven images from this product, captured by the multi-angle imaging spectroradiometer
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In this study, we showed that the multi-angle satellite remote sensing product, MISR L2 Land Surface (MIL2ASLS), which has a scale of 1.1 km, could be suitable for improving land-cover studies. Using seven images from this product, captured by the multi-angle imaging spectroradiometer sensor (MISR), we explored the values reached by the three parameters (ρ0, Θ, and k) of the Rahman–Pinty–Verstraete model, which was modified by Martonchick (MRPV). Thereafter, we compared the values and behaviors shown in seven Co-ordination of Information on the Environment (CORINE) land cover categories, in the red and near infrared (NIR) bands, over the seven MISR orbits captured in 2006 for Mainland Spain. Furthermore, we used Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) ancillary data and the illumination angles from the same pixels, which made up the images. These ancillary data were also provided by the MISR products. An inferential statistic test was performed to evaluate the relationship between each parameter–band combination, and the land cover in every MISR orbit used. The results suggested that the ρ0 parameters of this product seemed to be the most related to photosynthetic activity, and it should be comparable with the widely-used NDVI. On the other hand, the k and Θ parameter values were not related, or at least not entirely related, to the phenology of land coverage. These seemed to be more influenced by the anisotropy behavior of the studied land cover pixels. Additionally, we observed, by constructing analysis of variance, how the mean of each MRPV parameter–band differed statistically (p < 0.01) by land covers and orbits. This study suggested that the MISR MRPV model parameter data product has great potential to be used to improve land cover applications. Full article
Open AccessArticle Web-Scale Normalization of Geospatial Metadata Based on Semantics-Aware Data Sources
ISPRS Int. J. Geo-Inf. 2017, 6(11), 354; doi:10.3390/ijgi6110354
Received: 29 September 2017 / Revised: 27 October 2017 / Accepted: 2 November 2017 / Published: 13 November 2017
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Abstract
Geospatial metadata are largely denormalized inasmuch as resource descriptions typically accommodate property values as plain text. Hence, it is not possible to bring multiple references to the same entity (say, a keyword from a controlled vocabulary) under the same umbrella. This practice is
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Geospatial metadata are largely denormalized inasmuch as resource descriptions typically accommodate property values as plain text. Hence, it is not possible to bring multiple references to the same entity (say, a keyword from a controlled vocabulary) under the same umbrella. This practice is ultimately the main source for the heterogeneities in metadata descriptions by which geospatial discovery is hampered. In this paper, we elaborate on ex-post semantic augmentation of metadata, a technique generally referred to as semantic lift, which complements our previous research on semantic characterization of metadata via transparent association of uniform resource identifiers with metadata items at editing time. The latter is accomplished by means of a template-based metadata editor that can be tailored to any XML-based metadata schema. By repurposing the template language previously defined for metadata editing, we broaden the expressiveness of the former and integrate heterogeneous, XML-based resource descriptions in our semantics-aware metadata management workflow. URI-based indirection in metadata provision not only entails normalization of individual information items and allows one to overcome the aforementioned heterogeneities, but also elicits decentralized, multi-tenanted management of metadata. Full article
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Open AccessArticle Conceptual Design of a Mobile Application for Geography Fieldwork Learning
ISPRS Int. J. Geo-Inf. 2017, 6(11), 355; doi:10.3390/ijgi6110355
Received: 30 August 2017 / Revised: 3 October 2017 / Accepted: 3 November 2017 / Published: 9 November 2017
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Abstract
The use of mobile applications on smartphones has a vast potential to support learning in the field. However, all learning technologies should be properly designed. To this end, we adopt User-Centered Design (UCD) to design a mobile application, called GeoFARA (Geography Fieldwork Augmented
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The use of mobile applications on smartphones has a vast potential to support learning in the field. However, all learning technologies should be properly designed. To this end, we adopt User-Centered Design (UCD) to design a mobile application, called GeoFARA (Geography Fieldwork Augmented Reality Application), for university geography fieldwork. This paper is about the conceptual design of GeoFARA based on its use and user requirements. The paper first establishes a review of selected existing mobile AR applications for outdoor use, in order to identify the innovative aspects and the improvements of GeoFARA. Thereafter, we present the results of use and user requirements derived from (1) an online survey of the current use of tools in undergraduate geography fieldwork, (2) a field experiment in which the use of paper maps and a mobile mapping tool were compared, (3) investigations during a human geography fieldwork, (4) post-fieldwork surveys among undergraduates from two universities, (5) our use case, and (6) a use scenario. Based on these requirements, a conceptual design of GeoFARA is provided in terms of technical specifications, main contents, functionalities, as well as user interactions and interfaces. This conceptual design will guide the future prototype development of GeoFARA. Full article
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Open AccessArticle WLAN Fingerprint Indoor Positioning Strategy Based on Implicit Crowdsourcing and Semi-Supervised Learning
ISPRS Int. J. Geo-Inf. 2017, 6(11), 356; doi:10.3390/ijgi6110356
Received: 11 September 2017 / Revised: 31 October 2017 / Accepted: 3 November 2017 / Published: 9 November 2017
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Abstract
Wireless local area network (WLAN) fingerprint positioning is an indoor localization technique with high accuracy and low hardware requirements. However, collecting received signal strength (RSS) samples for the fingerprint database is time-consuming and labor-intensive, hindering the use of this technique. The popular crowdsourcing
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Wireless local area network (WLAN) fingerprint positioning is an indoor localization technique with high accuracy and low hardware requirements. However, collecting received signal strength (RSS) samples for the fingerprint database is time-consuming and labor-intensive, hindering the use of this technique. The popular crowdsourcing sampling technique has been introduced to reduce the workload of sample collection, but has two challenges: one is the heterogeneity of devices, which can significantly affect the positioning accuracy; the other is the requirement of users’ intervention in traditional crowdsourcing, which reduces the practicality of the system. In response to these challenges, we have proposed a new WLAN indoor positioning strategy, which incorporates a new preprocessing method for RSS samples, the implicit crowdsourcing sampling technique, and a semi-supervised learning algorithm. First, implicit crowdsourcing does not require users’ intervention. The acquisition program silently collects unlabeled samples, the RSS samples, without information about the position. Secondly, to cope with the heterogeneity of devices, the preprocessing method maps all the RSS values of samples to a uniform range and discretizes them. Finally, by using a large number of unlabeled samples with some labeled samples, Co-Forest, the introduced semi-supervised learning algorithm, creates and repeatedly refines a random forest ensemble classifier that performs well for location estimation. The results of experiments conducted in a real indoor environment show that the proposed strategy reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. Full article
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Open AccessFeature PaperArticle Visualization of Features in 3D Terrain
ISPRS Int. J. Geo-Inf. 2017, 6(11), 357; doi:10.3390/ijgi6110357
Received: 29 September 2017 / Revised: 31 October 2017 / Accepted: 3 November 2017 / Published: 14 November 2017
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Abstract
In 3D terrain analysis, topographical characteristics, such as mountains or valleys, and geo-spatial data characteristics, such as specific weather conditions or objects of interest, are important features. Visual representations of these features are essential in many application fields, e.g., aviation, meteorology, or geo-science.
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In 3D terrain analysis, topographical characteristics, such as mountains or valleys, and geo-spatial data characteristics, such as specific weather conditions or objects of interest, are important features. Visual representations of these features are essential in many application fields, e.g., aviation, meteorology, or geo-science. However, creating suitable representations is challenging. On the one hand, conveying the topography of terrain models is difficult, due to data complexity and computational costs. On the other hand, depicting further geo-spatial data increases the intricacy of the image and can lead to visual clutter. Moreover, perceptional issues within the 3D presentation, such as distance recognition, play a significant role as well. In this paper, we address the question of how features in the terrain can be visualized appropriately. We discuss various design options to facilitate the awareness of global and local features; that is, the coarse spatial distribution of characteristics and the fine-granular details. To improve spatial perception of the 3D environment, we propose suitable depth cues. Finally, we demonstrate the feasibility of our approach by a sophisticated framework called TedaVis that unifies the proposed concepts and facilitates designing visual terrain representations tailored to user requirements. Full article
(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)
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Open AccessArticle Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data
ISPRS Int. J. Geo-Inf. 2017, 6(11), 358; doi:10.3390/ijgi6110358
Received: 9 October 2017 / Revised: 9 November 2017 / Accepted: 13 November 2017 / Published: 15 November 2017
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Abstract
The housing market in Chinese metropolises have become inflated significantly over the last decade. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can
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The housing market in Chinese metropolises have become inflated significantly over the last decade. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can be dictated by the amenity value in the proximity of communities, such as accessibility to business centers and transportation hubs. In the past, scholars have employed the hedonic pricing model to quantify the amenity value in relation to structural, locational, and environmental variables. These studies, however, are limited by two methodological obstacles that are relatively difficult to overcome. The first pertains to difficulty of data collection in regions where geospatial datasets are strictly controlled and limited. The second refers to the spatial autocorrelation effect inherent in the hedonic analysis. Using Beijing, China as a case study, we addressed these two issues by (1) collecting residential housing and urban amenity data in terms of Points of Interest (POIs) through web-crawling on open access platforms; and (2) eliminating the spatial autocorrelation effect using the Eigenvector Spatial Filtering (ESF) method. The results showed that the effects of nearby amenities on housing prices are mixed. In other words, while proximity to certain amenities, such as convenient parking, was positively correlated with housing prices, other amenity variables, such as supermarkets, showed negative correlations. This mixed finding is further discussed in relation to community planning strategies in Beijing. This paper provides an example of employing open access datasets to analyze the determinants of housing prices. Results derived from the model can offer insights into the reasons for housing segmentation in Chinese cities, eventually helping to formulate effective urban planning strategies and equitable housing policies. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle A New Recursive Filtering Method of Terrestrial Laser Scanning Data to Preserve Ground Surface Information in Steep-Slope Areas
ISPRS Int. J. Geo-Inf. 2017, 6(11), 359; doi:10.3390/ijgi6110359 (registering DOI)
Received: 10 October 2017 / Revised: 3 November 2017 / Accepted: 13 November 2017 / Published: 15 November 2017
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Abstract
Landslides are one of the critical natural hazards that cause human, infrastructure, and economic losses. Risk of catastrophic losses due to landslides is significant given sprawled urban development near steep slopes and the increasing proximity of large populations to hilly areas. For reducing
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Landslides are one of the critical natural hazards that cause human, infrastructure, and economic losses. Risk of catastrophic losses due to landslides is significant given sprawled urban development near steep slopes and the increasing proximity of large populations to hilly areas. For reducing these losses, a high-resolution digital terrain model (DTM) is an essential piece of data for a qualitative or a quantitative investigation of slopes that may lead to landslides. Data acquired by a terrestrial laser scanning (TLS), called a point cloud, has been widely used to generate a DTM, since a TLS is appropriate for detecting small- to large-scale ground features on steep slopes. For an accurate DTM, TLS data should be filtered to remove non-ground points, but most current algorithms for extracting ground points from a point cloud have been developed for airborne laser scanning (ALS) data and not TLS data. Moreover, it is a challenging task to generate an accurate DTM from a steep-slope area by using existing algorithms. For these reasons, we developed an algorithm to automatically extract only ground points from the point clouds of steep terrains. Our methodology is focused on TLS datasets and utilizes the adaptive principal component analysis–triangular irregular network (PCA-TIN) approach. Our method was applied to two test areas and the results showed that the algorithm can cope well with steep slopes, giving an accurate surface model compared to conventional algorithms. Total accuracy values of the generated DTMs in the form of root mean squared errors are 1.84 cm and 2.13 cm over the areas of 5252 m2 and 1378 m2, respectively. The slope-based adaptive PCA-TIN method demonstrates great potential for TLS-derived DTM construction in steep-slope landscapes. Full article
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Open AccessArticle Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters
ISPRS Int. J. Geo-Inf. 2017, 6(11), 360; doi:10.3390/ijgi6110360
Received: 13 October 2017 / Revised: 9 November 2017 / Accepted: 14 November 2017 / Published: 15 November 2017
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Abstract
Coastal waters are one of the most vulnerable resources that require effective monitoring programs. One of the key factors for effective coastal monitoring is the use of remote sensing technologies that significantly capture the spatiotemporal variability of coastal waters. Optical properties of coastal
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Coastal waters are one of the most vulnerable resources that require effective monitoring programs. One of the key factors for effective coastal monitoring is the use of remote sensing technologies that significantly capture the spatiotemporal variability of coastal waters. Optical properties of coastal waters are strongly linked to components, such as colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), and suspended solids (SS) concentrations, which are essential for the survival of a coastal ecosystem and usually independent of each other. Thus, developing effective remote sensing models to estimate these important water components based on optical properties of coastal waters is mandatory for a successful coastal monitoring program. This study attempted to evaluate the performance of empirical predictive models (EPM) and neural networks (NN)-based algorithms to estimate Chl-a and SS concentrations, in the coastal area of Hong Kong. Remotely-sensed data over a 13-year period was used to develop regional and local models to estimate Chl-a and SS over the entire Hong Kong waters and for each water class within the study area, respectively. The accuracy of regional models derived from EPM and NN in estimating Chl-a and SS was 83%, 93%, 78%, and 97%, respectively, whereas the accuracy of local models in estimating Chl-a and SS ranged from 60–94% and 81–94%, respectively. Both the regional and local NN models exhibited a higher performance than those models derived from empirical analysis. Thus, this study suggests using machine learning methods (i.e., NN) for the more accurate and efficient routine monitoring of coastal water quality parameters (i.e., Chl-a and SS concentrations) over the complex coastal area of Hong Kong and other similar coastal environments. Full article
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Open AccessArticle Ground Deformation Detection Using China’s ZY-3 Stereo Imagery in an Opencast Mining Area
ISPRS Int. J. Geo-Inf. 2017, 6(11), 361; doi:10.3390/ijgi6110361
Received: 25 September 2017 / Revised: 6 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract
Detection and extraction of mining-induced ground deformation can be used to understand the deformation process and space distribution and to estimate the deformation laws and trends. This study focuses on the application of ground deformation detection and extraction combined with digital surface model
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Detection and extraction of mining-induced ground deformation can be used to understand the deformation process and space distribution and to estimate the deformation laws and trends. This study focuses on the application of ground deformation detection and extraction combined with digital surface model (DSM), derived from China’s ZiYuan-3 (ZY-3) satellite stereo imagery and the advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM) data. A district covering 200 km2 around the west open-pit mine in Fushun of Liaoning Province, a city located in Northeast China, is chosen as the study area. Regional overall deformation, typical region deformation, and topographical profile deformation are extracted to analyze the distribution and the link between the regional ground deformations. The results show that the mean elevation has already increased by 3.12 m from 2010 to 2015; 71.18% of this area is deformed, and 22.72% of this area has an elevation variation of more than 10 m. Four districts of rising elevation and three districts of descending elevation are extracted. They are deformed with distinct elevation and volume changes. The total area with distinct rising elevation (>15 m) is about 8.44 km2, and the change in volume is 2.47 × 108 m3. However, the total area with distinct descending elevation (<−10 m) is about 6.12 km2, and the change in volume is 2.01 × 108 m3. Moreover, the deformation in the local mining area has expanded to the surrounding areas. Experiments in the mining area demonstrate that ground deformation, especially acute deformation such as large fractures or landslides, can be monitored using DSMs derived from ZY-3 satellite stereo images. Full article
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Open AccessArticle A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks
ISPRS Int. J. Geo-Inf. 2017, 6(11), 362; doi:10.3390/ijgi6110362
Received: 2 September 2017 / Revised: 20 October 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract
Traffic incidents have a broad negative impact on both traffic systems and the quality of social activities; thus, analyzing and predicting the influence of traffic incidents dynamically is necessary. However, the traditional geographic information system for transportation (GIS-T) mostly presents fundamental data and
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Traffic incidents have a broad negative impact on both traffic systems and the quality of social activities; thus, analyzing and predicting the influence of traffic incidents dynamically is necessary. However, the traditional geographic information system for transportation (GIS-T) mostly presents fundamental data and static analysis, and transportation models focus predominantly on some typical road structures. Therefore, it is important to integrate transportation models with the spatiotemporal analysis techniques of GIS to address the dynamic process of traffic incidents. This paper presents a dynamic spatiotemporal analysis model to predict the influence of traffic incidents with the assistance of a GIS database and road network data. The model leverages a physical traffic shockwave model, and different superposition situations of shockwaves are proposed for both straight roads and road networks. Two typical cases were selected to verify the proposed model and were tested with the car-following model and real-world monitoring data. The results showed that the proposed model could successfully predict traffic effects with over 60% accuracy in both cases, and required less computational resources than the car-following model. Compared to other methods, the proposed model required fewer dynamic parameters and could be implemented on a wider set of road hierarchies. Full article
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Open AccessArticle A Hybrid Parallel Spatial Interpolation Algorithm for Massive LiDAR Point Clouds on Heterogeneous CPU-GPU Systems
ISPRS Int. J. Geo-Inf. 2017, 6(11), 363; doi:10.3390/ijgi6110363
Received: 30 September 2017 / Revised: 8 November 2017 / Accepted: 14 November 2017 / Published: 16 November 2017
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Abstract
Nowadays, heterogeneous CPU-GPU systems have become ubiquitous, but current parallel spatial interpolation (SI) algorithms exploit only one type of processing unit, and thus result in a waste of parallel resources. To address this problem, a hybrid parallel SI algorithm based on a thin
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Nowadays, heterogeneous CPU-GPU systems have become ubiquitous, but current parallel spatial interpolation (SI) algorithms exploit only one type of processing unit, and thus result in a waste of parallel resources. To address this problem, a hybrid parallel SI algorithm based on a thin plate spline is proposed to integrate both the CPU and GPU to further accelerate the processing of massive LiDAR point clouds. A simple yet powerful parallel framework is designed to enable simultaneous CPU-GPU interpolation, and a fast online training method is then presented to estimate the optimal decomposition granularity so that both types of processing units can run at maximum speed. Based on the optimal granularity, massive point clouds are continuously partitioned into a collection of discrete blocks in a data processing flow. A heterogeneous dynamic scheduler based on the greedy policy is also proposed to achieve better workload balancing. Experimental results demonstrate that the computing power of the CPU and GPU is fully utilized under conditions of optimal granularity, and the hybrid parallel SI algorithm achieves a significant performance boost when compared with the CPU-only and GPU-only algorithms. For example, the hybrid algorithm achieved a speedup of 20.2 on one of the experimental point clouds, while the corresponding speedups of using a CPU or a GPU alone were 8.7 and 12.6, respectively. The interpolation time was reduced by about 12% when using the proposed scheduler, in comparison with other common scheduling strategies. Full article
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Open AccessArticle Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval
ISPRS Int. J. Geo-Inf. 2017, 6(11), 364; doi:10.3390/ijgi6110364
Received: 12 September 2017 / Revised: 6 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract
Driven by the urgent demand of remote sensing big data management and knowledge discovery, large-scale remote sensing image retrieval (LSRSIR) has attracted more and more attention. As is well known, hashing learning has played an important role in coping with big data mining
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Driven by the urgent demand of remote sensing big data management and knowledge discovery, large-scale remote sensing image retrieval (LSRSIR) has attracted more and more attention. As is well known, hashing learning has played an important role in coping with big data mining problems. In the literature, several hashing learning methods have been proposed to address LSRSIR. Until now, existing LSRSIR methods take only one type of feature descriptor as the input of hashing learning methods and ignore the complementary effects of multiple features, which may represent remote sensing images from different aspects. Different from the existing LSRSIR methods, this paper proposes a flexible multiple-feature hashing learning framework for LSRSIR, which takes multiple complementary features as the input and learns the hybrid feature mapping function, which projects multiple features of the remote sensing image to the low-dimensional binary (i.e., compact) feature representation. Furthermore, the compact feature representations can be directly utilized in LSRSIR with the aid of the hamming distance metric. In order to show the superiority of the proposed multiple feature hashing learning method, we compare the proposed approach with the existing methods on two publicly available large-scale remote sensing image datasets. Extensive experiments demonstrate that the proposed approach can significantly outperform the state-of-the-art approaches. Full article
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Open AccessArticle An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method
ISPRS Int. J. Geo-Inf. 2017, 6(11), 365; doi:10.3390/ijgi6110365
Received: 5 July 2017 / Revised: 4 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract
The Mw 7.8 Gorkha earthquake of 25 April 2015 triggered thousands of landslides in the central part of the Nepal Himalayas. The main goal of this study was to generate an ensemble-based map of co-seismic landslide susceptibility in Sindhupalchowk District using model comparison
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The Mw 7.8 Gorkha earthquake of 25 April 2015 triggered thousands of landslides in the central part of the Nepal Himalayas. The main goal of this study was to generate an ensemble-based map of co-seismic landslide susceptibility in Sindhupalchowk District using model comparison and combination strands. A total of 2194 co-seismic landslides were identified and were randomly split into 1536 (~70%), to train data for establishing the model, and the remaining 658 (~30%) for the validation of the model. Frequency ratio, evidential belief function, and weight of evidence methods were applied and compared using 11 different causative factors (peak ground acceleration, epicenter proximity, fault proximity, geology, elevation, slope, plan curvature, internal relief, drainage proximity, stream power index, and topographic wetness index) to prepare the landslide susceptibility map. An ensemble of random forest was then used to overcome the various prediction limitations of the individual models. The success rates and prediction capabilities were critically compared using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). By synthesizing the results of the various models into a single score, the ensemble model improved accuracy and provided considerably more realistic prediction capacities (91%) than the frequency ratio (81.2%), evidential belief function (83.5%) methods, and weight of evidence (80.1%). Full article
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Open AccessArticle Accuracy Assessment of Landform Classification Approaches on Different Spatial Scales for the Iranian Loess Plateau
ISPRS Int. J. Geo-Inf. 2017, 6(11), 366; doi:10.3390/ijgi6110366
Received: 29 July 2017 / Revised: 10 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract
An accurate geomorphometric description of the Iranian loess plateau landscape will further enhance our understanding of recent and past geomorphological processes in this strongly dissected landscape. Therefore, four different input datasets for four landform classification methods were used in order to derive the
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An accurate geomorphometric description of the Iranian loess plateau landscape will further enhance our understanding of recent and past geomorphological processes in this strongly dissected landscape. Therefore, four different input datasets for four landform classification methods were used in order to derive the most accurate results in comparison to ground-truth data from a geomorphological field survey. The input datasets in 5 m and 10 m pixel resolution were derived from Pléiades stereo satellite imagery and the “Shuttle Radar Topography Mission” (SRTM), and “Advanced Spaceborne Thermal Emission and Reflection Radiometer” (ASTER GDEM) datasets with a spatial resolution of 30 m were additionally applied. The four classification approaches tested with this data include the stepwise approach after Dikau, the geomorphons, the topographical position index (TPI) and the object based approach. The results show that input datasets with higher spatial resolutions produced overall accuracies of greater than 70% for the TPI and geomorphons and greater than 60% for the other approaches. For the lower resolution datasets, only accuracies of about 40% were derived, 20–30% lower than for data derived from higher spatial resolutions. The results of the topographic position index and the geomorphons approach worked best for all selected input datasets. Full article
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Open AccessArticle Comparison and Evolution of Extreme Rainfall-Induced Landslides in Taiwan
ISPRS Int. J. Geo-Inf. 2017, 6(11), 367; doi:10.3390/ijgi6110367
Received: 12 September 2017 / Revised: 5 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract
This study analyzed the characteristics of, and locations prone to, extreme rainfall-induced landslides in three watersheds in Taiwan, as well as the long-term evolution of landslides in the Laonong River watershed (LRW), based on multiannual landslide inventories during 2003–2014. Extreme rainfall-induced landslides were
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This study analyzed the characteristics of, and locations prone to, extreme rainfall-induced landslides in three watersheds in Taiwan, as well as the long-term evolution of landslides in the Laonong River watershed (LRW), based on multiannual landslide inventories during 2003–2014. Extreme rainfall-induced landslides were centralized beside sinuous or meandering reaches, especially those with large sediment deposition. Landslide-prone strata during extreme rainfall events were sandstone and siltstone. Large-scale landslides were likely to occur when the maximum 6-h accumulated rainfall exceeded 420 mm. All of the large-scale landslides induced by short-duration and high-intensity rainfall developed from historical small-scale landslides beside the sinuous or meandering reaches or in the source area of rivers. However, most of the large-scale landslides induced by long-duration and high-intensity rainfall were new but were still located beside sinuous or meandering reaches or near the source. The frequency density of landslides under long-duration and high-intensity rainfall was larger by one order than those under short-duration rainfall, and the β values in the landslide frequency density-area analysis ranged from 1.22 to 1.348. The number of downslope landslides was three times larger than those of midslope and upslope landslides. The extreme rainfall-induced landslides occurred in the erosion gullies upstream of the watersheds, whereas those beside rivers were downstream. Analysis of the long-term evolution of landslides in the LRW showed that the geological setting, sinuousness of reaches, and sediment yield volume determined their location and evolution. Small-scale landslides constituted 71.9–96.2% of the total cases from 2003 to 2014, and were more easily induced after Typhoon Morakot (2009). The frequency density of landslides after Morakot was greater by one order than before, with 61% to 68% of total landslides located in the downslope. Small-scale landslides not beside the rivers disappeared within four years, whereas those beside rivers or located in the source areas either developed into large-scale landslides or slowly disappeared. Large-scale landslides caused by Morakot were either combined from several historical small-scale landslides in the river source areas or located beside the sinuous or meandering reaches. The probabilities of landslide recurrence in the LRW during the next 5, 10, and 20 years were determined to be 7.26%, 9.16%, and 10.48%, respectively, and those beside the rivers were 10.47%, 13.33%, and 15.41%, respectively. Full article
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Open AccessFeature PaperArticle Using Visual Exploratory Data Analysis to Facilitate Collaboration and Hypothesis Generation in Cross-Disciplinary Research
ISPRS Int. J. Geo-Inf. 2017, 6(11), 368; doi:10.3390/ijgi6110368
Received: 6 October 2017 / Revised: 11 November 2017 / Accepted: 15 November 2017 / Published: 16 November 2017
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Abstract
Massive open data resources are changing the way that people do science. To make use of those data resources, data science methods and technology can be leveraged by stakeholders of various disciplines. The objective of this paper is to present our experience of
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Massive open data resources are changing the way that people do science. To make use of those data resources, data science methods and technology can be leveraged by stakeholders of various disciplines. The objective of this paper is to present our experience of using visual exploratory data analysis as a method to facilitate collaboration and hypothesis generation in geoscience research. The research team consisted of both geoscientists and computer scientists. A use case-driven, iterative approach was applied to create a collaborative and communicative environment. Through several rounds of use case analysis and technological development, a data visualization pilot system was created for studying the co-relationships between chemical elements and mineral species. The exploratory data analyses conducted in those use case studies led to several research hypotheses for future work. This research illustrates the usefulness of exploratory data analysis for hypothesis generation in a data science process. Although the presented project is in geoscience, the discussed method and experience can also be translated into other disciplines. Full article
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Open AccessFeature PaperArticle Mapping and Analyzing Stream Network Changes in Watonwan River Watershed, Minnesota, USA
ISPRS Int. J. Geo-Inf. 2017, 6(11), 369; doi:10.3390/ijgi6110369 (registering DOI)
Received: 30 September 2017 / Revised: 31 October 2017 / Accepted: 13 November 2017 / Published: 17 November 2017
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Abstract
Much of the Watonwan River tributary system to the upper Mississippi River basin (UMR), and the fluvial systems to which it drains, are listed as impaired under the United States Environmental Protection Agency Clean Water Act303(d) and/or by the Minnesota Pollution Control Agency.
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Much of the Watonwan River tributary system to the upper Mississippi River basin (UMR), and the fluvial systems to which it drains, are listed as impaired under the United States Environmental Protection Agency Clean Water Act303(d) and/or by the Minnesota Pollution Control Agency. In addition, eutrophic conditions and excessive sedimentation rates exist in Lake Pepin, a riverine lake to which the UMR drains. Thus, understanding the hydrogeomorphic change throughout the UMR is vital in order to establish appropriate efforts to mitigate environmental hazards downstream. This study attempts to evaluate hydrogeomorphic change at the watershed scale in the Watonwan River watershed between 1855 and the near present. Historical plat maps, digital elevation models (DEMs), aerial images, soil/topographic characteristics, land-use change, and field surveys are analyzed. Surficial hydrologic features digitized from historical plat maps are compared with contemporary stream networks extracted from high-resolution DEMs. Scale effects are investigated using multi-resolution (1 m, 3 m, 8.5 m, and 30 m) DEMs, with 8.5 m DEMs being ideal for watershed scale analysis, and 1–3 m DEMs being ideal for subwatershed analysis. There has been a substantial hydrogeomorphic change in the watershed since 1855, but most significantly, we interpret that the highest rates of erosion occur in the eastern watershed, where knickzone propagation has produced substantial relief. Full article
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Open AccessArticle An Integrated Spatial Clustering Analysis Method for Identifying Urban Fire Risk Locations in a Network-Constrained Environment: A Case Study in Nanjing, China
ISPRS Int. J. Geo-Inf. 2017, 6(11), 370; doi:10.3390/ijgi6110370 (registering DOI)
Received: 23 September 2017 / Revised: 10 November 2017 / Accepted: 15 November 2017 / Published: 17 November 2017
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Abstract
The spatial distribution of urban geographical events is largely constrained by the road network, and research on spatial clusters of fire accidents at the city level plays a crucial role in emergency rescue and urban planning. For example, by knowing where and when
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The spatial distribution of urban geographical events is largely constrained by the road network, and research on spatial clusters of fire accidents at the city level plays a crucial role in emergency rescue and urban planning. For example, by knowing where and when fire accidents usually occur, fire enforcement can conduct more efficient aid measures and planning department can work out more reasonable layout optimization of fire stations. This article proposed an integrated method by combining weighted network-constrained kernel density estimation (NKDE) and network-constrained local Moran’s I (ILINCS) to detect spatial cluster pattern and identify higher-risk locations of fire accidents. The proposed NKDE-ILINCS weighted a set of crucial non-spatial attributes of point events and links, and considered the impact factors of road traffic states, intersection roads and fire severity in NKDE to reflect real urban environment. This method was tested using the fire data in 2015 in Nanjing, China. The results demonstrated that the method was appropriate to detect network-constrained fire cluster patterns and identify high–high road segments. Besides, the first 14 higher-risk road segments in Nanjing are listed. These findings of this case study enhance our knowledge to more accurately observe where fire accidents usually occur and provide a reference for fire departments to improve emergency rescue effectiveness. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle Deriving Ephemeral Gullies from VHR Image in Loess Hilly Areas through Directional Edge Detection
ISPRS Int. J. Geo-Inf. 2017, 6(11), 371; doi:10.3390/ijgi6110371 (registering DOI)
Received: 6 September 2017 / Revised: 13 November 2017 / Accepted: 15 November 2017 / Published: 18 November 2017
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Abstract
Monitoring ephemeral gullies facilitates water planning and soil conservation. Artificial interpretation based on high spatial resolution images is the main method for monitoring ephemeral gullies in large areas; however, this method is time consuming. In this study, a semiautomatic method for extracting ephemeral
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Monitoring ephemeral gullies facilitates water planning and soil conservation. Artificial interpretation based on high spatial resolution images is the main method for monitoring ephemeral gullies in large areas; however, this method is time consuming. In this study, a semiautomatic method for extracting ephemeral gullies in loess hilly areas based on directional edge detection is proposed. First, the area where ephemeral gullies developed was extracted because the weak trace of ephemeral gullies in images can hardly be detected by most image detectors, which avoided the noise from other large gullies. Second, a Canny edge detector was employed to extract all edges in the image. Then, those edges along the direction where ephemeral gullies developed were searched and coded as candidate ephemeral gullies. Finally, the ephemeral gullies were identified through filtering of pseudo-gullies by setting the appropriate length threshold. Experiments in three loess hilly areas showed that accuracy ranged from 38.18% to 85.05%, completeness ranged from 82.35% to 92.86%, and quality ranged from 35.29% to 79.82%. The quality of the remote sensing images highly affected the results. The accuracy was significantly improved when the image was used with less grass and shrubs. The length threshold in directional searching also affected the accuracy. A small threshold resulted in additional noise and disconnected gullies, whereas a large threshold disregarded the short gullies. A reasonable threshold can be obtained through the index of quality. The threshold also exhibits a strong relationship with the average length of ephemeral gullies, and this relationship can help obtain the optimum threshold in the hilly area of the Northern Loess Plateau of China. Full article
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Open AccessArticle A Representation Method for Complex Road Networks in Virtual Geographic Environments
ISPRS Int. J. Geo-Inf. 2017, 6(11), 372; doi:10.3390/ijgi6110372 (registering DOI)
Received: 29 August 2017 / Revised: 14 November 2017 / Accepted: 15 November 2017 / Published: 18 November 2017
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Abstract
Road networks are important for modelling the urban geographic environment. It is necessary to determine the spatial relationships of road intersections when using maps to help researchers conduct virtual urban geographic experiments (because a road intersection might occur as a connected cross or
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Road networks are important for modelling the urban geographic environment. It is necessary to determine the spatial relationships of road intersections when using maps to help researchers conduct virtual urban geographic experiments (because a road intersection might occur as a connected cross or as an unconnected bridge overpass). Based on the concept of using different map layers to organize the render order of each road segment, three methods (manual, semi-automatic and mask-based automatic) are available to help map designers arrange the rendering order. However, significant efforts are still needed, and rendering efficiency remains problematic with these methods. This paper considers the Discrete, Crossing, Overpass, Underpass, Conjunction, Up-overlap and Down-overlap spatial relationships of road intersections. An automatic method is proposed to represent these spatial relationships when drawing road networks on a map. The data-layer organization method (reflecting road grade and elevation-level information) and the symbol-layer decomposition method (reflecting road covering order in the vertical direction) are designed to determine the rendering order of each road element when rendering a map. In addition, an “auxiliary-drawing-action” (for drawing road segments belonging to different grades and elevations) is proposed to adjust the rendering sequences automatically. Two experiments are conducted to demonstrate the feasibility and efficiency of the method, and the results demonstrate that it can effectively handle spatial relationships of road networks in map representations. Using the proposed method, the difficulty of rendering complex road networks can be reduced. Full article
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