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ISPRS Int. J. Geo-Inf., Volume 8, Issue 7 (July 2019)

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Open AccessArticle
VS30 Seismic Microzoning Based on a Geomorphology Map: Experimental Case Study of Chiang Mai, Chiang Rai, and Lamphun, Thailand
ISPRS Int. J. Geo-Inf. 2019, 8(7), 309; https://doi.org/10.3390/ijgi8070309
Received: 13 May 2019 / Revised: 8 July 2019 / Accepted: 13 July 2019 / Published: 18 July 2019
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Abstract
Thailand is not known to be an earthquake-prone country; however, in 2014, an unexpected moderate earthquake caused severe damage to infrastructure and resulted in public panic. This event caught public attention and raised awareness of national seismic disaster management. However, the expertise and [...] Read more.
Thailand is not known to be an earthquake-prone country; however, in 2014, an unexpected moderate earthquake caused severe damage to infrastructure and resulted in public panic. This event caught public attention and raised awareness of national seismic disaster management. However, the expertise and primary data required for implementation of seismic disaster management are insufficient, including data on soil character which are used in amplification analyses for further ground motion prediction evaluations. Therefore, in this study, soil characterization was performed to understand the seismic responses of soil rigidity. The final output is presented in a seismic microzoning map. A geomorphology map was selected as the base map for the analysis. The geomorphology units were assigned with a time-averaged shear wave velocity of 30 m (VS30), which was collected by the spatial autocorrelation (SPAC) method of microtremor array measurements. The VS30 values were obtained from the phase velocity of the Rayleigh wave corresponding to a 40 m wavelength (C(40)). From the point feature, the VS30 values were transformed into polygonal features based on the geomorphological characteristics. Additionally, the automated geomorphology classification was explored in this study. Then, the seismic microzones were compared with the locations of major damage from the 2014 records for validation. The results from this study include geomorphological classification and seismic microzoning. The results suggest that the geomorphology units obtained from a pixel-based classification can be recommended for use in seismic microzoning. For seismic microzoning, the results show mainly stiff soil and soft rocks in the study area, and these geomorphological units have relatively high amplifications. The results of this study provide a valuable base map for further disaster management analyses. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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Open AccessArticle
Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data
ISPRS Int. J. Geo-Inf. 2019, 8(7), 308; https://doi.org/10.3390/ijgi8070308
Received: 30 May 2019 / Revised: 4 July 2019 / Accepted: 12 July 2019 / Published: 17 July 2019
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Abstract
Anomalous urban mobility pattern refers to abnormal human mobility flow in a city. Anomalous urban mobility pattern detection is important in the study of urban mobility. In this paper, a framework is proposed to identify anomalous urban mobility patterns based on taxi GPS [...] Read more.
Anomalous urban mobility pattern refers to abnormal human mobility flow in a city. Anomalous urban mobility pattern detection is important in the study of urban mobility. In this paper, a framework is proposed to identify anomalous urban mobility patterns based on taxi GPS trajectories and Point of Interest (POI) data. In the framework, functional regions are first generated based on the distribution of POIs by the DBSCAN clustering algorithm. A Weighted Term Frequency-Inverse Document Frequency (WTF-IDF) method is proposed to identify function values in each region. Then, the Origin-Destination (OD) of trips between functional regions is extracted from GPS trajectories to detect anomalous urban mobility patterns. Mobility vectors are established for each time interval based on the OD of trips and are classified into clusters by the mean shift algorithm. Abnormal urban mobility patterns are identified by processing the mobility vectors. A case study in the city of Wuhan, China, is conducted; the experimental results show that the proposed method can effectively identify daily and hourly anomalous urban mobility patterns. Full article
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Open AccessArticle
Monitoring Ground Instabilities Using SAR Satellite Data: A Practical Approach
ISPRS Int. J. Geo-Inf. 2019, 8(7), 307; https://doi.org/10.3390/ijgi8070307
Received: 17 May 2019 / Revised: 25 June 2019 / Accepted: 12 July 2019 / Published: 17 July 2019
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Abstract
Satellite interferometric data are widely exploited for ground motion monitoring thanks to their wide area coverage, cost efficiency and non-invasiveness. The launch of the Sentinel-1 constellation opened new horizons for interferometric applications, allowing the scientists to rethink the way in which these data [...] Read more.
Satellite interferometric data are widely exploited for ground motion monitoring thanks to their wide area coverage, cost efficiency and non-invasiveness. The launch of the Sentinel-1 constellation opened new horizons for interferometric applications, allowing the scientists to rethink the way in which these data are delivered, passing from a static view of the territory to a continuous streaming of ground motion measurements from space. Tuscany Region is the first worldwide example of a regional scale monitoring system based on satellite interferometric data. The processing chain here exploited combines a multi-interferometric approach with a time-series data mining algorithm aimed at recognizing benchmarks with significant trend variations. The system is capable of detecting the temporal changes of a wide variety of phenomena such as slow-moving landslides and subsidence, producing a high amount of data to be interpreted in a short time. Bulletins and reports are derived to the hydrogeological risk management actors at regional scale. The final output of the project is a list of potentially hazardous and accelerating phenomena that are verified on site by field campaign by completing a sheet survey in order to qualitatively estimate the risk and to suggest short-term actions to be taken by local entities. Two case studies, one related to landslides and one to subsidence, are proposed to highlight the potential of the monitoring system to early detect anomalous ground changes. Both examples represent a successful implementation of satellite interferometric data as monitoring and risk management tools, raising the awareness of local and regional authorities to geohazards. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Modeling the Vagueness of Areal Geographic Objects: A Categorization System
ISPRS Int. J. Geo-Inf. 2019, 8(7), 306; https://doi.org/10.3390/ijgi8070306
Received: 10 June 2019 / Revised: 24 June 2019 / Accepted: 12 July 2019 / Published: 17 July 2019
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Abstract
Modeling vague objects with indeterminate boundaries has drawn much attention in geographic information science. Because fields and objects are two perspectives in modeling geographic phenomena, this paper investigates the characteristics of vague regions from the perspective of the field/object dichotomy. Based on the [...] Read more.
Modeling vague objects with indeterminate boundaries has drawn much attention in geographic information science. Because fields and objects are two perspectives in modeling geographic phenomena, this paper investigates the characteristics of vague regions from the perspective of the field/object dichotomy. Based on the assumption that a vague object can be viewed as the conceptualization of a field, we defined five categories of vague objects: direct field-cutting objects, focal operation-based field-cutting objects, element-clustering objects, object-referenced objects, and dynamic boundary objects. We then established a categorization system to formalize the semantic differences between vague objects using the fuzzy set theory. The proposed framework provides valuable input for the conceptualization, interpretation, and modeling of vague geographical objects. Full article
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Open AccessArticle
Assessing Spatial Information Themes in the Spatial Information Infrastructure for Participatory Urban Planning Monitoring: Indonesian Cities
ISPRS Int. J. Geo-Inf. 2019, 8(7), 305; https://doi.org/10.3390/ijgi8070305
Received: 22 May 2019 / Revised: 3 July 2019 / Accepted: 12 July 2019 / Published: 17 July 2019
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Abstract
Most urban planning monitoring activities were designed to monitor implementation of aggregated sectors from different initiatives into practical and measurable indicators. Today, cities utilize spatial information in monitoring and evaluating urban planning implementation for not only national or local goals but also for [...] Read more.
Most urban planning monitoring activities were designed to monitor implementation of aggregated sectors from different initiatives into practical and measurable indicators. Today, cities utilize spatial information in monitoring and evaluating urban planning implementation for not only national or local goals but also for the 2030 Agenda of Sustainable Development Goals (SDGs). Modern cities adopt Participatory Geographic Information System (PGIS) initiative for their urban planning monitoring. Cities provide spatial information and online tools for citizens to participate. However, the selection of spatial information services for participants is made from producers’ perception and often disregards requirements from the regulation, functionalities, and broader user’s perception. By providing appropriate spatial information, the quality of participatory urban monitoring can be improved. This study presents a method for selecting appropriate spatial information for urban planning monitoring. It considers regulation, urban planning, and spatial science theories, as well as citizens’ requirements, to support participatory urban planning monitoring as a way to ensure the success of providing near real-time urban information to planners and decision-makers. Full article
(This article belongs to the Special Issue Algorithms and Techniques in Urban Monitoring)
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Open AccessArticle
A New Algorithms of Stroke Generation Considering Geometric and Structural Properties of Road Network
ISPRS Int. J. Geo-Inf. 2019, 8(7), 304; https://doi.org/10.3390/ijgi8070304
Received: 6 May 2019 / Revised: 28 June 2019 / Accepted: 13 July 2019 / Published: 16 July 2019
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Abstract
Strokes are considered an elementary unit of road networks and have been widely used in their analysis and application. However, most conventional stroke generation methods are based solely on a fixed angle threshold, which ignores road networks’ geometric and structural properties. To remedy [...] Read more.
Strokes are considered an elementary unit of road networks and have been widely used in their analysis and application. However, most conventional stroke generation methods are based solely on a fixed angle threshold, which ignores road networks’ geometric and structural properties. To remedy this, this paper proposes an algorithm for generating strokes that takes into account these additional geometric and structural road network properties and that reduces the impact of stroke generation on road network quality. To this end, we introduce a model of feature-based information entropy and then utilize this model to calculate road networks’ information volume and both the elemental and neighborhood level. To make our experimental results more objective, we use the Douglas-Peucker algorithm to simplify the information change curve and to obtain the optimal angle threshold range for generating strokes for different road network structures. Finally, we apply this model to three different road networks, and the optimal threshold ranges are 54°–63° (Chicago), 61°–63° (Moscow), 45°–48° (Monaco). And taking Monaco as an example, this paper conducts stroke selection experiments. The results demonstrate that our proposed algorithm has better connectivity and wider coverage than those based on a common angle threshold (60°). Full article
(This article belongs to the Special Issue Map Generalization)
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Open AccessArticle
Skeleton Line Extraction Method in Areas with Dense Junctions Considering Stroke Features
ISPRS Int. J. Geo-Inf. 2019, 8(7), 303; https://doi.org/10.3390/ijgi8070303
Received: 1 June 2019 / Revised: 24 June 2019 / Accepted: 12 July 2019 / Published: 16 July 2019
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Abstract
Extraction of the skeleton line of complex polygons is difficult, and a hot topic in map generalization study. Due to the irregularity and complexity of junctions, it is difficult for traditional methods to maintain main structure and extension characteristics when dealing with dense [...] Read more.
Extraction of the skeleton line of complex polygons is difficult, and a hot topic in map generalization study. Due to the irregularity and complexity of junctions, it is difficult for traditional methods to maintain main structure and extension characteristics when dealing with dense junction areas, so a skeleton line extraction method considering stroke features has been proposed in this paper. Firstly, we put forward a long-edge adaptive node densification algorithm, which is used to construct boundary-constrained Delaunay triangulation to uniformly divide the polygon and extract the initial skeleton line. Secondly, we defined the triangles with three adjacent triangles (Type III) as the basic unit of junctions, then obtained the segmented areas with dense junctions on the basis of local width characteristics and correlation relationships of each Type III triangle. Finally, we concatenated the segments into strokes and corrected the initial skeleton lines based on the extension direction features of each stroke. The actual water network data of Jiangsu Province in China were used to verify the method. Experimental results show that the proposed method can better identify the areas with dense junctions and that the extracted skeleton line is naturally smooth and well-connected, which accurately reflects the main structure and extension characteristics of these areas. Full article
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Open AccessArticle
Areal Interpolation Using Parcel and Census Data in Highly Developed Urban Environments
ISPRS Int. J. Geo-Inf. 2019, 8(7), 302; https://doi.org/10.3390/ijgi8070302
Received: 28 May 2019 / Revised: 20 June 2019 / Accepted: 12 July 2019 / Published: 16 July 2019
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Abstract
Areal interpolation is routinely used when spatial data are unavailable at desired geographical units. While many methods are available, few of them were developed specifically for and tested in highly developed urban cores. Even fewer studied subpopulation or population characteristics. This paper explores [...] Read more.
Areal interpolation is routinely used when spatial data are unavailable at desired geographical units. While many methods are available, few of them were developed specifically for and tested in highly developed urban cores. Even fewer studied subpopulation or population characteristics. This paper explores both issues using parcel map and decennial census data as ancillary information. Using census blocks as intermediate zones, the method first disaggregates source-zone data to intermediate zones, then disaggregates data to parcel level in intermediate zones intersecting target zones, and finally aggregates intermediate-zone and parcel-level estimates to obtain target-zone estimates. Compared to areal weighting and residential proportion, the proposed method is significantly more accurate. All three methods perform the best on population count, and worst on spatially clustered subpopulations such as black/African American population. Quotient variables are more difficult to interpolate than count variables. The research demonstrates the utility of parcel and decennial census data for areal interpolation in highly developed urban cores, and calls for future research on subpopulation and population characteristics. Full article
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Open AccessArticle
HBIM Modeling from the Surface Mesh and Its Extended Capability of Knowledge Representation
ISPRS Int. J. Geo-Inf. 2019, 8(7), 301; https://doi.org/10.3390/ijgi8070301
Received: 7 July 2019 / Accepted: 12 July 2019 / Published: 15 July 2019
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Abstract
Built heritage has been documented by reality-based modeling for geometric description and by ontology for knowledge management. The current challenge still involves the extraction of geometric primitives and the establishment of their connection to heterogeneous knowledge. As a recently developed 3D information modeling [...] Read more.
Built heritage has been documented by reality-based modeling for geometric description and by ontology for knowledge management. The current challenge still involves the extraction of geometric primitives and the establishment of their connection to heterogeneous knowledge. As a recently developed 3D information modeling environment, building information modeling (BIM) entails both graphical and non-graphical aspects of the entire building, which has been increasingly applied to heritage documentation and generates a new issue of heritage/historic BIM (HBIM). However, HBIM needs to additionally deal with the heterogeneity of geometric shape and semantic knowledge of the heritage object. This paper developed a new mesh-to-HBIM modeling workflow and an integrated BIM management system to connect HBIM elements and historical knowledge. Using the St-Pierre-le-Jeune Church, Strasbourg, France as a case study, this project employs Autodesk Revit as a BIM environment and Dynamo, a built-in visual programming tool of Revit, to extend the new HBIM functions. The mesh-to-HBIM process segments the surface mesh, thickens the triangle mesh to 3D volume, and transfers the primitives to BIM elements. The obtained HBIM is then converted to the ontology model to enrich the heterogeneous knowledge. Finally, HBIM geometric elements and ontology semantic knowledge is joined in a unified BIM environment. By extending the capability of the BIM platform, the HBIM modeling process can be conducted in a time-saving way, and the obtained HBIM is a semantic model with object-oriented knowledge. Full article
(This article belongs to the Special Issue BIM for Cultural Heritage (HBIM))
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Open AccessArticle
A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality
ISPRS Int. J. Geo-Inf. 2019, 8(7), 300; https://doi.org/10.3390/ijgi8070300
Received: 1 June 2019 / Revised: 11 July 2019 / Accepted: 12 July 2019 / Published: 15 July 2019
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Abstract
Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by [...] Read more.
Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project. The VGG16 has been used as the base model since it allows finetuning, and high performance could be achieved by selecting the best hyper-parameters. Although the training dataset was small, the proposed CNN architecture was found to be effective as it could identify the landslide photos with 94% precision. The accuracy of the results is sufficient for purpose and could even be improved further using a larger amount of training data, which is expected to be obtained with the help of volunteers. Full article
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Open AccessArticle
Collision Detection for UAVs Based on GeoSOT-3D Grids
ISPRS Int. J. Geo-Inf. 2019, 8(7), 299; https://doi.org/10.3390/ijgi8070299
Received: 7 May 2019 / Revised: 24 June 2019 / Accepted: 13 July 2019 / Published: 15 July 2019
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Abstract
The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation [...] Read more.
The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation increases exponentially with an increasing number of spatial entities; second, existing grid-based approaches are unsuitable for complicated scenarios with a large number of objects moving at high speeds. In the proposed model, we first identified UAVs and other spatial objects with GeoSOT-3D grids. Second, the nonrelational spatial database was initialized with a multitable strategy, and spatiotemporal data were inserted with the GeoSOT-3D grid codes as the primary key. Third, the collision detection procedure was transformed from a pairwise calculation to a multilevel query. Four simulation experiments were conducted to verify the feasibility and efficiency of the proposed collision detection model for UAVs in different environments. The results also indicated that 64 m GeoSOT-3D grids are the most suitable basic grid size, and the reduction in the time consumption compared with traditional methods reached approximately 50–80% in different scenarios. Full article
(This article belongs to the Special Issue Global Grid Systems)
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Open AccessArticle
City Maker: Reconstruction of Cities from OpenStreetMap Data for Environmental Visualization and Simulations
ISPRS Int. J. Geo-Inf. 2019, 8(7), 298; https://doi.org/10.3390/ijgi8070298
Received: 3 June 2019 / Revised: 3 July 2019 / Accepted: 12 July 2019 / Published: 15 July 2019
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Abstract
Recent innovations in 3D processing and availability of geospatial data have contributed largely to more comprehensive solutions to data visualization. As various data formats are utilized to describe the data, a combination of layers from different sources allow us to represent 3D urban [...] Read more.
Recent innovations in 3D processing and availability of geospatial data have contributed largely to more comprehensive solutions to data visualization. As various data formats are utilized to describe the data, a combination of layers from different sources allow us to represent 3D urban areas, contributing to ideas of emergency management and smart cities. This work focuses on 3D urban environment reconstruction using crowdsourced OpenStreetMap data. Once the data are extracted, the visualization pipeline draws features using coloring for added context. Moreover, by structuring the layers and entities through the addition of simulation parameters, the generated environment is made simulation ready for further use. Results show that urban areas can be properly visualized in 3D using OpenStreetMap data given data availability. The simulation-ready environment was tested using hypothetical flooding scenarios, which demonstrated that the added parameters can be utilized in environmental simulations. Furthermore, an efficient restructuring of data was implemented for viewing the city information once the data are parsed. Full article
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Open AccessArticle
A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China
ISPRS Int. J. Geo-Inf. 2019, 8(7), 297; https://doi.org/10.3390/ijgi8070297
Received: 11 June 2019 / Revised: 9 July 2019 / Accepted: 9 July 2019 / Published: 12 July 2019
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Abstract
Flash floods are one of the natural disasters that threaten the lives of many people all over the world every year. Flash floods are significantly affected by the intensification of extreme climate events and interactions with exposed and vulnerable socio-economic systems impede regional [...] Read more.
Flash floods are one of the natural disasters that threaten the lives of many people all over the world every year. Flash floods are significantly affected by the intensification of extreme climate events and interactions with exposed and vulnerable socio-economic systems impede regional development processes. Hence, it is important to estimate the loss due to flash floods before the disaster occurs. However, there are no comprehensive vulnerability assessment results for flash floods in China. Fortunately, the National Mountain Flood Disaster Investigation Project provided a foundation to develop this proposed assessment. In this study, an index system was established from the exposure and disaster reduction capability categories, and is based on analytic hierarchy process (AHP) methods. We evaluated flash flood vulnerability by adopting the support vector machine (SVM) model. Our results showed 439 counties with high and extremely high vulnerability (accounting for 10.5% of the land area and corresponding to approximately 100 million hectares (ha)), 571 counties with moderate vulnerability (accounting for 19.18% of the land area and corresponding to approximately 180 million ha), and 1128 counties with low and extremely low vulnerability (accounting for 39.43% of the land area and corresponding to approximately 370 million ha). The highly-vulnerable counties were mainly concentrated in the south and southeast regions of China, moderately-vulnerable counties were primarily concentrated in the central, northern, and southwestern regions of China, and low-vulnerability counties chiefly occurred in the northwest regions of China. Additionally, the results of the spatial autocorrelation suggested that the “High-High” values of spatial agglomeration areas mainly occurred in the Zhejiang, Fujian, Jiangxi, Hunan, Guangxi, Chongqing, and Beijing areas. On the basis of these results, our study can be used as a proposal for population and building distribution readjustments, and the management of flash floods in China. Full article
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Open AccessFeature PaperReview
Application of Remote Sensing to the Investigation of Rock Slopes: Experience Gained and Lessons Learned
ISPRS Int. J. Geo-Inf. 2019, 8(7), 296; https://doi.org/10.3390/ijgi8070296
Received: 10 May 2019 / Revised: 22 June 2019 / Accepted: 24 June 2019 / Published: 27 June 2019
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Abstract
The stability and deformation behavior of high rock slopes depends on many factors, including geological structures, lithology, geomorphic processes, stress distribution, and groundwater regime. A comprehensive mapping program is, therefore, required to investigate and assess the stability of high rock slopes. However, slope [...] Read more.
The stability and deformation behavior of high rock slopes depends on many factors, including geological structures, lithology, geomorphic processes, stress distribution, and groundwater regime. A comprehensive mapping program is, therefore, required to investigate and assess the stability of high rock slopes. However, slope steepness, rockfalls and ongoing instability, difficult terrain, and other safety concerns may prevent the collection of data by means of traditional field techniques. Therefore, remote sensing methods are often critical to perform an effective investigation. In this paper, we describe the application of field and remote sensing approaches for the characterization of rock slopes at various scale and distances. Based on over 15 years of the experience gained by the Engineering Geology and Resource Geotechnics Research Group at Simon Fraser University (Vancouver, Canada), we provide a summary of the potential applications, advantages, and limitations of varied remote sensing techniques for comprehensive characterization of rock slopes. We illustrate how remote sensing methods have been critical in performing rock slope investigations. However, we observe that traditional field methods still remain indispensable to collect important intact rock and discontinuity condition data. Full article
(This article belongs to the Special Issue Applications of Photogrammetry for Environmental Research)
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Open AccessArticle
Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories
ISPRS Int. J. Geo-Inf. 2019, 8(7), 295; https://doi.org/10.3390/ijgi8070295
Received: 17 May 2019 / Accepted: 24 June 2019 / Published: 27 June 2019
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Abstract
Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov [...] Read more.
Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis. Full article
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Open AccessArticle
Automatic Discovery of Railway Train Driving Modes Using Unsupervised Deep Learning
ISPRS Int. J. Geo-Inf. 2019, 8(7), 294; https://doi.org/10.3390/ijgi8070294
Received: 16 May 2019 / Revised: 23 June 2019 / Accepted: 24 June 2019 / Published: 27 June 2019
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Abstract
Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and gear changes can be good proxies in the [...] Read more.
Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and gear changes can be good proxies in the study of driving modes. However, in the absence of labeled data, discovering driving modes is challenging. In this paper, instead of classical models (railway-specified feature extraction and classical clustering), we used five deep unsupervised learning models to overcome this difficulty. In these models, adversarial autoencoders and stacked autoencoders are used as feature extractors, along with generative adversarial network-based and Kullback–Leibler (KL) divergence-based networks as clustering models. An experiment based on real and artificial datasets showed the following: (i) The proposed deep learning models outperform the classical models by 27.64% on average. (ii) Integrated trajectory data can improve the accuracy of unsupervised learning by approximately 13.78%. (iii) The different performance rankings of models based on indices with labeled data and indices without labeled data demonstrate the insufficiency of people’s understanding of the existing modes. This study also analyzes the relationship between the discovered modes and railway carrying capacity. Full article
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Open AccessReview
Automatic (Tactile) Map Generation—A Systematic Literature Review
ISPRS Int. J. Geo-Inf. 2019, 8(7), 293; https://doi.org/10.3390/ijgi8070293
Received: 15 May 2019 / Revised: 14 June 2019 / Accepted: 24 June 2019 / Published: 27 June 2019
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Abstract
This paper presents a systematic literature review that reflects the current state of research in the field of algorithms and models for map generalization, the existing solutions for automatic (tactile) map generation, as well as good practices for designing spatial databases for the [...] Read more.
This paper presents a systematic literature review that reflects the current state of research in the field of algorithms and models for map generalization, the existing solutions for automatic (tactile) map generation, as well as good practices for designing spatial databases for the purposes of automatic map development. A total number of over 500 primary studies were screened in order to identify the most relevant research on automatic (tactile) map generation from the last decade. The reviewed papers revealed many existing solutions in the field of automatic map production, as well as algorithms (e.g., Douglas–Peucker, Visvalingam–Whyatt) and models (e.g., GAEL, CartACom) for data generalization that might be used to transform traditional spatial data into the haptic form, suitable for blind and visually impaired people. However, it turns out that a comprehensive solution for automatic tactile map generation does not exist. Full article
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Open AccessArticle
The Role of African Emerging Space Agencies in Earth Observation Capacity Building for Facilitating the Implementation and Monitoring of the African Development Agenda: The Case of African Earth Observation Program
ISPRS Int. J. Geo-Inf. 2019, 8(7), 292; https://doi.org/10.3390/ijgi8070292
Received: 30 April 2019 / Revised: 12 June 2019 / Accepted: 15 June 2019 / Published: 27 June 2019
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Abstract
AU-Agenda 2063 was adopted at the 24th Ordinary Session of the African Heads of State and Government in 2015 as the blueprint for the future development of the continent. Built upon the continent’s past experiences, challenges, and successes, AU-Agenda 2063 comprehensively describes the [...] Read more.
AU-Agenda 2063 was adopted at the 24th Ordinary Session of the African Heads of State and Government in 2015 as the blueprint for the future development of the continent. Built upon the continent’s past experiences, challenges, and successes, AU-Agenda 2063 comprehensively describes the strategic path for Africa’s future development in the next 50 years. Thus, the monitoring of its implementation in various African states is critical for ensuring sustainable development and track progress. However, the higher cost of collecting data for accurately and reliably monitoring the implementation of Agenda 2063 may hinder the progress towards achieving these goals. Satellite Earth observation provides ample data, and thus has provided opportunities for the development of novel products and services with the potential to support implementation, monitoring and reporting for AU-Agenda 2063 development imperatives. However, it has been limitedly exploited in Africa, as evidenced by lower research outputs and investments. This calls for increased capacity building in the use of available EO data and products for various users including decision makers to advance national, regional and continental priorities. The use of such data products is often hampered by the capability to understand the products and thus their value for addressing socio-economic challenges. This paper discusses the potential of Earth observation capacity building for supporting the implementation, monitoring of, and reporting towards achieving AU-Agenda 2063 development imperatives. Specifically, this paper identifies existing capacity building resources, including the role of open and free Earth observation data, open-source software, and product dissemination platforms that can be leveraged for supporting national development, service delivery and the achievement of AU-Agenda 2063 targets. Furthermore, the paper recognizes the importance of bilateral and multilateral partnerships in leveraging existing know-how, technology and other resources for advancing strategic goals of African emerging space agencies and promoting sustainable development, with examples from South African National Space Agency (SANSA). Then, the challenges and opportunities for capacity building and the wide adoption of EO in Africa are discussed in the context of AU-Agenda 2063. The paper thus concludes that EO capacity building is essential to address the skills and data gaps and increase the use of EO-based solutions for decision making in various sectors, critical for achieving AU-A2063. Full article
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Open AccessArticle
Evaluating the Suitability of Urban Expansion Based on the Logic Minimum Cumulative Resistance Model: A Case Study from Leshan, China
ISPRS Int. J. Geo-Inf. 2019, 8(7), 291; https://doi.org/10.3390/ijgi8070291
Received: 19 May 2019 / Revised: 8 June 2019 / Accepted: 11 June 2019 / Published: 26 June 2019
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Abstract
This paper focuses on the suitability of urban expansion in mountain areas against the background of accelerated urban development. Urbanization is accompanied by conflict and intense transformations of various landscapes, and is accompanied by social, economic, and ecological impacts. Evaluating the suitability of [...] Read more.
This paper focuses on the suitability of urban expansion in mountain areas against the background of accelerated urban development. Urbanization is accompanied by conflict and intense transformations of various landscapes, and is accompanied by social, economic, and ecological impacts. Evaluating the suitability of urban expansion (UE) and determining an appropriate scale is vital to solving urban environmental issues and realizing sustainable urban development. In mountain areas, the natural and social environments are different from those in the plains; the former is characterized by fragile ecology and proneness to geological disasters. Therefore, when evaluating the expansion of a mountain city, more factors need to be considered. Moreover, we need to follow the principle of harmony between nature and society according to the characteristics of mountain cities. Thus, when we evaluate the expansion of a mountain city, the key procedure is to establish a scientific evaluation system and explore the relationship between each evaluation factor and the urban expansion process. Taking Leshan (LS), China—a typical mountain city in the upper Yangtze River which has undergone rapid growth—as a case study, the logic minimum cumulative resistance (LMCR) model was applied to evaluate the suitability of UE and to simulate its direction and scale. The results revealed that: An evaluation system of resistance factors (ESRFs) was established according to the principle of natural and social harmony; the logic resistance surface (LRS) scientifically integrated multiple resistance factors based on the ESRF and a logic regression analysis. LRS objectively and effectively reflected the contribution and impact of each resistance factor to urban expansion. We found that landscape, geological hazards and GDP have had a great impact on urban expansion in LS. The expansion space of the mountain city is limited; the area of suitable expansion is only 23.5%, while the area which is unsuitable for expansion is 39.3%. In addition, it was found that setting up ecological barriers is an effective way to control unreasonable urban expansion in mountain cities. There is an obvious scale (grid size) effect in the evaluation of urban expansion in mountain cities; an evaluation of the suitable scale yielded the result of 90 m × 90 m. On this scale, taking the central district as the center, the urban expansion process will extend to the neighboring towns of Mianzhu, Suji, Juzi and Mouzi. Urban expansion should be controlled in terms of scale, especially in mountain cities. The most suitable urban size of LS is 132 km2.This would allow for high connectivity of urban-rural areas with the occupation of relatively few green spaces. Full article
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Open AccessArticle
High-Performance Overlay Analysis of Massive Geographic Polygons That Considers Shape Complexity in a Cloud Environment
ISPRS Int. J. Geo-Inf. 2019, 8(7), 290; https://doi.org/10.3390/ijgi8070290
Received: 24 March 2019 / Revised: 19 June 2019 / Accepted: 24 June 2019 / Published: 26 June 2019
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Abstract
Overlay analysis is a common task in geographic computing that is widely used in geographic information systems, computer graphics, and computer science. With the breakthroughs in Earth observation technologies, particularly the emergence of high-resolution satellite remote-sensing technology, geographic data have demonstrated explosive growth. [...] Read more.
Overlay analysis is a common task in geographic computing that is widely used in geographic information systems, computer graphics, and computer science. With the breakthroughs in Earth observation technologies, particularly the emergence of high-resolution satellite remote-sensing technology, geographic data have demonstrated explosive growth. The overlay analysis of massive and complex geographic data has become a computationally intensive task. Distributed parallel processing in a cloud environment provides an efficient solution to this problem. The cloud computing paradigm represented by Spark has become the standard for massive data processing in the industry and academia due to its large-scale and low-latency characteristics. The cloud computing paradigm has attracted further attention for the purpose of solving the overlay analysis of massive data. These studies mainly focus on how to implement parallel overlay analysis in a cloud computing paradigm but pay less attention to the impact of spatial data graphics complexity on parallel computing efficiency, especially the data skew caused by the difference in the graphic complexity. Geographic polygons often have complex graphical structures, such as many vertices, composite structures including holes and islands. When the Spark paradigm is used to solve the overlay analysis of massive geographic polygons, its calculation efficiency is closely related to factors such as data organization and algorithm design. Considering the influence of the shape complexity of polygons on the performance of overlay analysis, we design and implement a parallel processing algorithm based on the Spark paradigm in this paper. Based on the analysis of the shape complexity of polygons, the overlay analysis speed is improved via reasonable data partition, distributed spatial index, a minimum boundary rectangular filter and other optimization processes, and the high speed and parallel efficiency are maintained. Full article
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
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