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ISPRS Int. J. Geo-Inf., Volume 8, Issue 3 (March 2019)

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Open AccessArticle Location Privacy in the Wake of the GDPR
ISPRS Int. J. Geo-Inf. 2019, 8(3), 157; https://doi.org/10.3390/ijgi8030157 (registering DOI)
Received: 13 February 2019 / Revised: 13 March 2019 / Accepted: 15 March 2019 / Published: 22 March 2019
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Abstract
The General Data Protection Regulation (GDPR) protects the personal data of natural persons and at the same time allows the free movement of such data within the European Union (EU). Hailed as majestic by admirers and dismissed as protectionist by critics, the Regulation [...] Read more.
The General Data Protection Regulation (GDPR) protects the personal data of natural persons and at the same time allows the free movement of such data within the European Union (EU). Hailed as majestic by admirers and dismissed as protectionist by critics, the Regulation is expected to have a profound impact around the world, including in the African Union (AU). For European–African consortia conducting research that may affect the privacy of African citizens, the question is `how to protect personal data of data subjects while at the same time ensuring a just distribution of the benefits of a global digital ecosystem?’ We use location privacy as a point of departure, because information about an individual’s location is different from other kinds of personally identifiable information. We analyse privacy at two levels, individual and cultural. Our perspective is interdisciplinary: we draw from computer science to describe three scenarios of transformation of volunteered or observed information to inferred information about a natural person and from cultural theory to distinguish four privacy cultures emerging within the EU in the wake of GDPR. We highlight recent data protection legislation in the AU and discuss factors that may accelerate or inhibit the alignment of data protection legislation in the AU with the GDPR. Full article
Open AccessArticle Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?
ISPRS Int. J. Geo-Inf. 2019, 8(3), 156; https://doi.org/10.3390/ijgi8030156 (registering DOI)
Received: 30 September 2018 / Revised: 1 January 2019 / Accepted: 24 February 2019 / Published: 22 March 2019
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Abstract
Using two GEOBIA (Geographical Object Based Image Analysis) algorithms on a set of segmented images compared to grid partitioning at different scales, we show that statistical metrics related to both objects and sets of pixels are (more or less) subject to the Modifiable [...] Read more.
Using two GEOBIA (Geographical Object Based Image Analysis) algorithms on a set of segmented images compared to grid partitioning at different scales, we show that statistical metrics related to both objects and sets of pixels are (more or less) subject to the Modifiable Areal Unit Problem. Subsequently, even in a same spatial partition, there may be a bias in statistics describing the objects due to some size effect of the pixel samples. For instance, pixels homogeneity based on Grey Level Cooccurrence Matrices (GLCM), Landscape Shape Index, entropy, object compacity, perimeter/area ratio are studied according to scale. The approach consists in studying the behavior of a given statistical metrics through scales and to compare the results on several image segmentations, according to different partitioning processes, from GEOBIA (Baatz & Schäpe algorithm and Self Organizing Maps) or using reference grids. We finally discuss about the relationship between GEOBIA metrics and scale. By analysing object shape and pixels composition from different metrics points of views, we show that GEOBIA does not always mitigate the Modifiable Areal Unit Problem. Full article
(This article belongs to the Special Issue GEOBIA in a Changing World)
Open AccessArticle Airbnb Offer in Spain—Spatial Analysis of the Pattern and Determinants of Its Distribution
ISPRS Int. J. Geo-Inf. 2019, 8(3), 155; https://doi.org/10.3390/ijgi8030155 (registering DOI)
Received: 28 February 2019 / Revised: 12 March 2019 / Accepted: 15 March 2019 / Published: 22 March 2019
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Abstract
The rising number of homes and apartments rented out through Airbnb and similar peer-to-peer accommodation platforms cause concerns about the impact of such activity on the tourism sector and property market. To date, spatial analysis on peer-to-peer rental activity has been usually limited [...] Read more.
The rising number of homes and apartments rented out through Airbnb and similar peer-to-peer accommodation platforms cause concerns about the impact of such activity on the tourism sector and property market. To date, spatial analysis on peer-to-peer rental activity has been usually limited in scope to individual large cities. In this study, we take into account the whole territory of Spain, with special attention given to cities and regions with high tourist activity. We use a dataset of about 250 thousand Airbnb listings in Spain obtained from the Airbnb webpage, aggregate the numbers of these offers in 8124 municipalities and 79 tourist areas/sites, measure their concentration, spatial autocorrelation, and develop regression models to find the determinants of Airbnb rentals’ distribution. We conclude that apart from largest cities, Airbnb is active in holiday destinations of Spain, where it often serves as an intermediary for the rental of second or investment homes and apartments. The location of Airbnb listings is mostly determined by the supply of empty or secondary dwellings, distribution of traditional tourism accommodation, coastal location, and the level of internationalization of tourism demand. Full article
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Open AccessArticle Geographical Area Network—Structural Health Monitoring Utility Computing Model
ISPRS Int. J. Geo-Inf. 2019, 8(3), 154; https://doi.org/10.3390/ijgi8030154
Received: 22 November 2018 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 21 March 2019
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Abstract
In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural [...] Read more.
In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM. Full article
Open AccessArticle HiXDraw: An Improved XDraw Algorithm Free of Chunk Distortion
ISPRS Int. J. Geo-Inf. 2019, 8(3), 153; https://doi.org/10.3390/ijgi8030153
Received: 7 February 2019 / Revised: 3 March 2019 / Accepted: 10 March 2019 / Published: 21 March 2019
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Abstract
Viewshed analysis is of great interest to location optimization, environmental planning, ecology and tourism. There have been plenty of viewshed analysis methods which are generally time-consuming and among these methods, the XDraw algorithm is one of the fastest algorithms and has been widely [...] Read more.
Viewshed analysis is of great interest to location optimization, environmental planning, ecology and tourism. There have been plenty of viewshed analysis methods which are generally time-consuming and among these methods, the XDraw algorithm is one of the fastest algorithms and has been widely adopted in various applications. Unfortunately, XDraw suffers from chunk distortion which greatly lowers the accuracy, which limits the application of XDraw to a certain extent. Previous works failed to remove chunk distortion because they are unaware of the underlying contribution relationship. In this paper, we propose HiXDraw—an improved XDraw algorithm free of chunk distortion. We first uncover the causation of chunk distortion from an innovative contributing perspective. Instead of recording LOS (line-of-sight) height, we use a new auxiliary grid to preserve contributing points. By preventing improper terrain data from contributing to determining the visibility, we significantly improve the accuracy of the outcome viewshed. The experimental results reveal that the error rate largely decreases by 65%. Given the same computing time, HiXDraw is more accurate than previous improvements in XDraw. To validate the removal of chunk distortion, we also present a pillar experiment. Full article
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Open AccessArticle Making the Invisible Visible—Strategies for Visualizing Underground Infrastructures in Immersive Environments
ISPRS Int. J. Geo-Inf. 2019, 8(3), 152; https://doi.org/10.3390/ijgi8030152
Received: 13 February 2019 / Revised: 5 March 2019 / Accepted: 10 March 2019 / Published: 20 March 2019
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Abstract
Visualization of underground infrastructure in an interactive 3D immersive environment is extremely important for efficient management of city’s infrastructure. This paper describes different geometric modelling approaches to illustrate appropriate visualization of such data. A multimodal prototype has been developed by exploiting different algorithms [...] Read more.
Visualization of underground infrastructure in an interactive 3D immersive environment is extremely important for efficient management of city’s infrastructure. This paper describes different geometric modelling approaches to illustrate appropriate visualization of such data. A multimodal prototype has been developed by exploiting different algorithms to render these invisible underground objects as part of an urban model. This prototype has been integrated in an immersive geographic information system (GIS), named MultiVis, for handheld iOS and Android devices. As a part of the study, three distinct strategies have been tested; the first is based on the use of transparencies to convey a sense of depth, the second relies on an image-space superposition of “ditches” on top of the rendered frame and the third is a world-space deformation of the elevation model that exposes the underground elements. Furthermore, a comparative user experience analysis of different techniques aimed to the geometrically accurate visualisation of utility networks and other underground facilities are performed and evaluated. It includes a set of user evaluations for different parameters of these techniques, which gives us an insight on how the proposed methods affect the experience and usability for technical and non-technical users. Full article
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Open AccessArticle Finding Visible kNN Objects in the Presence of Obstacles within the User’s View Field
ISPRS Int. J. Geo-Inf. 2019, 8(3), 151; https://doi.org/10.3390/ijgi8030151
Received: 8 February 2019 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 20 March 2019
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Abstract
In many spatial applications, users are only interested in data objects that are visible to them. Hence, finding visible data objects is an important operation in these real-world spatial applications. This study addressed a new type of spatial query, the View field-aware Visible [...] Read more.
In many spatial applications, users are only interested in data objects that are visible to them. Hence, finding visible data objects is an important operation in these real-world spatial applications. This study addressed a new type of spatial query, the View field-aware Visible k Nearest Neighbor (V2-kNN) query. Given the location of a user and his/her view field, a V2-kNN query finds data object p so that p is the nearest neighbor of and visible to the user, where visible means the data object is (1) not hidden by obstacles and (2) inside the view field of the user. Previous works on visible NN queries considered only one of these two factors, but not both. To the best of our knowledge, this work is the first to consider both the effect of obstacles and the restriction of the view field in finding the solutions. To support efficient processing of V2-kNN queries, a grid structure is used to index data objects and obstacles. Pruning heuristics are also designed so that only data objects and obstacles relevant to the final query result are accessed. A comprehensive experimental evaluation using both real and synthetic datasets is performed to verify the effectiveness of the proposed algorithms. Full article
(This article belongs to the Special Issue Spatial Databases: Design, Management, and Knowledge Discovery)
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Open AccessArticle Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China
ISPRS Int. J. Geo-Inf. 2019, 8(3), 150; https://doi.org/10.3390/ijgi8030150
Received: 29 January 2019 / Revised: 12 March 2019 / Accepted: 15 March 2019 / Published: 20 March 2019
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Abstract
Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and [...] Read more.
Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively. Full article
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Open AccessArticle Spatial Learning with Orientation Maps: The Influence of Different Environmental Features on Spatial Knowledge Acquisition
ISPRS Int. J. Geo-Inf. 2019, 8(3), 149; https://doi.org/10.3390/ijgi8030149
Received: 21 February 2019 / Revised: 14 March 2019 / Accepted: 15 March 2019 / Published: 20 March 2019
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Abstract
The prevalent use of GPS-based navigation systems impairs peoples’ ability to orient themselves. This paper investigates whether wayfinding maps that accentuate different types of environmental features support peoples’ spatial learning. A virtual-reality driving simulator was used to investigate spatial knowledge acquisition in assisted [...] Read more.
The prevalent use of GPS-based navigation systems impairs peoples’ ability to orient themselves. This paper investigates whether wayfinding maps that accentuate different types of environmental features support peoples’ spatial learning. A virtual-reality driving simulator was used to investigate spatial knowledge acquisition in assisted wayfinding tasks. Two main conditions of wayfinding maps were tested against a base condition: (i) highlighting local features, i.e., landmarks, along the route and at decision points; and (ii) highlighting structural features that provide global orientation. The results show that accentuating local features supports peoples’ acquisition of route knowledge, whereas accentuating global features supports peoples’ acquisition of survey knowledge. The results contribute to the general understanding of spatial knowledge acquisition in assisted wayfinding tasks. Future navigation systems could enhance spatial knowledge by providing visual navigation support incorporating not only landmarks but structural features in wayfinding maps. Full article
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Open AccessArticle Shallow Landslide Susceptibility Mapping in Sochi Ski-Jump Area Using GIS and Numerical Modelling
ISPRS Int. J. Geo-Inf. 2019, 8(3), 148; https://doi.org/10.3390/ijgi8030148
Received: 30 January 2019 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 19 March 2019
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Abstract
The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the [...] Read more.
The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the ski-jump complex area. This study aims to conduct a seismic slope instability map of the area. A slope map was derived from a digital elevation model (DEM) and calculated using ArcGIS. The numerical modelling of slope stability with various slope angles was conducted using Geostudio. The Spencer method was applied to calculate the slope safety factors (Fs). The pseudostatic analysis was used to compute Fs considering seismic effect. A good correlation between Fs and slope angle was found. Combining these data, sets slope instability maps were achieved. Newmark displacement maps were also drawn according to empirical regression equations. The result shows that the static safety factor map corresponds to the existing slope instability locations in a shallow landslide inventory map. The seismic safety factor maps and Newmark displacement maps may be applied to predict potential landslides of the study area in the case of earthquake occurrence. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
Open AccessArticle Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam
ISPRS Int. J. Geo-Inf. 2019, 8(3), 147; https://doi.org/10.3390/ijgi8030147
Received: 13 February 2019 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 19 March 2019
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Abstract
Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions. This study was conducted in a hilly region of Central Vietnam with the following objectives: (i) to [...] Read more.
Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions. This study was conducted in a hilly region of Central Vietnam with the following objectives: (i) to evaluate the best environmental variables to estimate soil organic carbon (SOC), total nitrogen (TN), and soil reaction (pH) with a regression kriging (RK) model, and (ii) to compare the accuracy of the ordinary kriging (OK) and RK methods. SOC, TN, and soil pH data were measured at 155 locations within the research area with a sampling grid of 2 km × 2 km for a soil layer from 0 to 30 cm depth. From these samples, 117 were used for interpolation, and the 38 randomly remaining samples were used for evaluating accuracy. The chosen environmental variables are land use type (LUT), topographic wetness index (TWI), and transformed soil adjusted vegetation index (TSAVI). The results indicate that the LUT variable is more effective than TWI and TSAVI for determining TN and pH when using the RK method, with a variance of 7.00% and 18.40%, respectively. In contrast, a combination of the LUT and TWI variables is the best for SOC mapping with the RK method, with a variance of 14.98%. The OK method seemed more accurate than the RK method for SOC mapping by 3.33% and for TN mapping by 10% but the RK method was found more precise than the OK method for soil pH mapping by 1.81%. Further selection of auxiliary variables and higher sampling density should be considered to improve the accuracy of the RK method. Full article
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Open AccessArticle A Universal Generating Algorithm of the Polyhedral Discrete Grid Based on Unit Duplication
ISPRS Int. J. Geo-Inf. 2019, 8(3), 146; https://doi.org/10.3390/ijgi8030146
Received: 4 February 2019 / Revised: 6 March 2019 / Accepted: 15 March 2019 / Published: 19 March 2019
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Abstract
Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region [...] Read more.
Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region restriction”. First, the grid coordinate system and the corresponding spatial rectangular coordinate system are established to determine the rectangular coordinates of any grid cell node. Then, the type of the subdivision grid system to be calculated is determined to identify the three key factors affecting the grid types, which are the position of the starting point, the length of the starting edge, and the direction of the starting edge. On this basis, the effective boundary of a multiscale grid can be determined and the grid coordinates of a multiscale grid can be obtained. A one-to-one correspondence between the multiscale grids and subdivision types can be established. Through the appropriate rotation, translation and scaling of the multiscale grid, the node coordinates of a single triangular grid system are calculated, and the relationships between the nodes of different levels are established. Finally, this paper takes a hexagonal grid as an example to carry out the experiment verifications by converting a single triangular grid system (plane) directly to a single triangular grid with a positive icosahedral surface to generate a positive icosahedral surface grid. The experimental results show that the algorithm has good universality and can generate the multiscale grid of an arbitrary grid configuration by adjusting the corresponding starting transformation parameters. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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Open AccessArticle Instability Index Derived from a Landslide Inventory for Watershed Stability Assessment and Mapping
ISPRS Int. J. Geo-Inf. 2019, 8(3), 145; https://doi.org/10.3390/ijgi8030145
Received: 2 February 2019 / Revised: 13 March 2019 / Accepted: 15 March 2019 / Published: 19 March 2019
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Abstract
Watersheds represent natural units of social–ecological systems and affect crop productivity. Extreme weather events accelerate the natural erosion process by triggering more landslides in watersheds. To achieve the land degradation neutrality set up by the UN’s Sustainable Development Goals, it is necessary to [...] Read more.
Watersheds represent natural units of social–ecological systems and affect crop productivity. Extreme weather events accelerate the natural erosion process by triggering more landslides in watersheds. To achieve the land degradation neutrality set up by the UN’s Sustainable Development Goals, it is necessary to assess and map spatiotemporal landslides in watersheds. This paper proposes an innovative approach to calculating the instability index by preparing an annual landslide inventory, determining the optimum sub-watershed, compensating for shadow effects on the time series of the landslide area ratio, and classifying the standard deviations to different levels of instability. Taking the Qingquan watershed as an example, the instability index calculated for 22 sub-watersheds makes it possible to identify hot spots that are prone to collapse. This new index can also be used to evaluate the effectiveness of watershed management before and after completion of a specific engineering project, as well as to update the latest upriver situation to evaluate current management practices and develop strategies for future planning. Based on this new approach, the Soil and Water Conservation Bureau of Taiwan assesses the stability of 28 watersheds, and the results are made available on the Big Geospatial Information System. Full article
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Open AccessArticle Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS
ISPRS Int. J. Geo-Inf. 2019, 8(3), 144; https://doi.org/10.3390/ijgi8030144
Received: 5 February 2019 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 19 March 2019
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Abstract
The rapid increase in applications of Light Detection and Ranging (LiDAR) scanners, followed by the development of various methods that are dedicated for survey data processing, visualization, and dissemination constituted the need of new open standards for storage and online distribution of collected [...] Read more.
The rapid increase in applications of Light Detection and Ranging (LiDAR) scanners, followed by the development of various methods that are dedicated for survey data processing, visualization, and dissemination constituted the need of new open standards for storage and online distribution of collected three-dimensional data. However, over a decade of research in the area has resulted in a number of incompatible solutions that offer their own ways of disseminating results of LiDAR surveys (be it point clouds or reconstructed three-dimensional (3D) models) over the web. The article presents a unified system for remote processing, storage, visualization, and dissemination of 3D LiDAR survey data, including 3D model reconstruction. It is built with the use of open source technologies and employs open standards, such as 3D Tiles, LASer (LAS), and Object (OBJ) for data distribution. The system has been deployed for automatic organization, processing, and dissemination of LiDAR surveys that were performed in the city of Gdansk. The performance of the system has been measured using a selection of LiDAR datasets of various sizes. The system has shown to considerably simplify the process of data organization and integration, while also delivering tools for easy discovery, inspection, and acquisition of desired datasets. Full article
(This article belongs to the Special Issue Multidimensional and Multiscale GIS)
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Open AccessArticle Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences
ISPRS Int. J. Geo-Inf. 2019, 8(3), 143; https://doi.org/10.3390/ijgi8030143
Received: 21 February 2019 / Revised: 11 March 2019 / Accepted: 15 March 2019 / Published: 18 March 2019
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Abstract
In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, [...] Read more.
In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, along with historical lightning-caused fire occurrences during the 2005–2016 period, derived from a Moderate Resolution Imaging Spectroradiometer. First, we calculated the normalized difference water index (NDWI) as an indicator of vegetation/fuel water content over the six natural subregions of interest. Then, we generated the subregion-specific annual dynamic median NDWI during the 2005–2012 period, which was assembled into a distinct pattern every year. We plotted the historical lightning-caused fires onto the generated patterns, and used the concept of cumulative frequency to model lightning-caused fire occurrences. Then, we applied this concept to model the cumulative frequencies of lightning-caused fires using the median NDWI values in each natural subregion. By finding the best subregion-specific function (i.e., R2 values over 0.98 for each subregion), we evaluated their performance using an independent subregion-specific lightning-caused fire dataset acquired during the 2013–2016 period. Our analyses revealed strong relationships (i.e., R2 values in the range of 0.92 to 0.98) between the observed and modeled cumulative frequencies of lightning-caused fires at the natural subregion level throughout the validation years. Finally, our results demonstrate the applicability of the proposed method in modeling lightning-caused fire occurrences over forested regions. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Open AccessArticle A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data
ISPRS Int. J. Geo-Inf. 2019, 8(3), 142; https://doi.org/10.3390/ijgi8030142
Received: 29 December 2018 / Revised: 28 February 2019 / Accepted: 11 March 2019 / Published: 14 March 2019
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Abstract
With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very [...] Read more.
With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods. Full article
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
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Open AccessArticle Information Exchange between GIS and Geospatial ITS Databases Based on a Generic Model
ISPRS Int. J. Geo-Inf. 2019, 8(3), 141; https://doi.org/10.3390/ijgi8030141
Received: 31 January 2019 / Revised: 5 March 2019 / Accepted: 10 March 2019 / Published: 14 March 2019
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Abstract
This study aims to improve interoperability between Geographic Information Systems (GIS) and geospatial databases for Intelligent Transport Systems (ITS). Road authorities maintain authoritative information for legal and safe navigation in GIS databases. This information needs to be shared with ITS databases for route [...] Read more.
This study aims to improve interoperability between Geographic Information Systems (GIS) and geospatial databases for Intelligent Transport Systems (ITS). Road authorities maintain authoritative information for legal and safe navigation in GIS databases. This information needs to be shared with ITS databases for route planning and navigation, and for use in combination with local knowledge from vehicle sensors. Current solutions for modelling and exchanging geospatial information in the domains of GIS and ITS have been studied and evaluated. Limitations have been pointed out related to usability in the GIS domain and flexibility for representing an evolving real world. A prototype for an improved information exchange model has been developed, based on ISO/TC 211 standards, Model Driven Architecture (MDA), and concepts from the studied solutions. The prototype contains generic models for feature catalogues and features, with implementation schemas in the Geography Markup Language (GML). Results from a case study indicated that the models could be implemented with feature catalogues from the ITS standard ISO 14825 Geographic Data Files (GDF) and the INSPIRE Transport Networks specification. The prototype can be a candidate solution for improved information exchange from GIS databases to ITS databases that are based on the Navigation Data Standard. Full article
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Open AccessArticle Large-Scale Station-Level Crowd Flow Forecast with ST-Unet
ISPRS Int. J. Geo-Inf. 2019, 8(3), 140; https://doi.org/10.3390/ijgi8030140
Received: 24 January 2019 / Revised: 7 March 2019 / Accepted: 11 March 2019 / Published: 13 March 2019
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Abstract
High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public [...] Read more.
High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods
ISPRS Int. J. Geo-Inf. 2019, 8(3), 139; https://doi.org/10.3390/ijgi8030139
Received: 30 January 2019 / Revised: 3 March 2019 / Accepted: 11 March 2019 / Published: 13 March 2019
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Abstract
Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats [...] Read more.
Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method. Full article
(This article belongs to the Special Issue Algorithms and Techniques in Urban Monitoring)
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Open AccessArticle Integrating Spatial and Non-Spatial Dimensions to Measure Urban Fire Service Access
ISPRS Int. J. Geo-Inf. 2019, 8(3), 138; https://doi.org/10.3390/ijgi8030138
Received: 7 February 2019 / Revised: 4 March 2019 / Accepted: 11 March 2019 / Published: 13 March 2019
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Abstract
Assessing the access to fire service at an urban scale involves accounting for geographical impedance, demand, and supply, thus both spatial and non-spatial dimensions must be taken into account. Therefore, in this paper, an optimized two-step floating catchment area (F-2SFCA) method is proposed [...] Read more.
Assessing the access to fire service at an urban scale involves accounting for geographical impedance, demand, and supply, thus both spatial and non-spatial dimensions must be taken into account. Therefore, in this paper, an optimized two-step floating catchment area (F-2SFCA) method is proposed for measuring urban fire service access, which incorporates the effects of both spatial and non-spatial factors into fire service access. The proposed model is conducted in a case study to assess the fire service accessibility of Nanjing City, China, and then compares its differences and strengths to the existing 2SFCA (two-step floating catchment area) methods. The experimental results demonstrate that the proposed method effectively quantifies the actual fire service needs and reflects a more realistic spatial pattern of accessibility (i.e., high accessibility level corresponded to a low fire service needs). In addition, we teste the relationship between service accessibility and the facility busyness using the inverted 2SFCA method. The empirical findings indicate that the weighted average accessibility obtained by F-2SFCA is reciprocal to facility busyness across the study area (based on a 5-min catchment), and fits an obvious nonlinear correlation with the high R-square values. The above results further prove the effectiveness and accuracy of the proposed method in characterizing the accessibility of fire services. Full article
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Open AccessArticle Evacuation Priority Method in Tsunami Hazard Based on DMSP/OLS Population Mapping in the Pearl River Estuary, China
ISPRS Int. J. Geo-Inf. 2019, 8(3), 137; https://doi.org/10.3390/ijgi8030137
Received: 9 January 2019 / Revised: 26 February 2019 / Accepted: 4 March 2019 / Published: 9 March 2019
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Abstract
Evacuation plans are critical in case of natural disaster to save people’s lives. The priority of population evacuation on coastal areas could be useful to reduce the death toll in case of tsunami hazard. In this study, the population density remote sensing mapping [...] Read more.
Evacuation plans are critical in case of natural disaster to save people’s lives. The priority of population evacuation on coastal areas could be useful to reduce the death toll in case of tsunami hazard. In this study, the population density remote sensing mapping approach was developed using population records in 2013 and Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) night-time light (NTL) image of the same year for defining the coastal densest resident areas in Pearl River Estuary (PRE), China. Two pixel-based saturation correction methods were evaluated for application of population density mapping to enhance DMSP/OLS NTL image. The Vegetation Adjusted NTL Urban Index (VANUI) correction method (R2 (original/corrected): 0.504, Std. error: 0.0069) was found to be the better-fit correction method of NTL image saturation for the study area compared to Human Settlement Index (HSI) correction method (R2 (original/corrected): 0.219, Std. error: 0.1676). The study also gained a better dynamic range of HSI correction (0~25 vs. 0.1~5.07) compared to the previous one [27]. The town-level’s population NTL simulation model is built (R2 = 0.43, N = 47) for the first time in PRE with mean relative error (MSE) of 32% (N = 24, town level), On the other side, the tsunami hazard map was produced based on numerical modeling of potential tsunami wave height and velocity, combining with the river net system, elevation, slope, and vegetation cover factors. Both results were combined to produce an evacuation map in PRE. The simulation of tsunami exposure on density of population showed that the highest evacuation priority was found to be in most of Zhuhai city area and the coastal area of Shenzhen City under wave height of nine meters, while lowest evacuation priority was defined in Panyu and Nansha Districts of Guangzhou City, eastern and western parts of Zhongshan City, and northeast and northwest parts of Dongguan City. The method of tsunami risk simulation and the result of mapped tsunami exposure are of significance for direction to tsunami disaster-risk reduction or evacuation traffic arrangement in PRE or other coastal areas in the world. Full article
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Open AccessArticle A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages
ISPRS Int. J. Geo-Inf. 2019, 8(3), 136; https://doi.org/10.3390/ijgi8030136
Received: 8 February 2019 / Revised: 3 March 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
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Abstract
The effectiveness of disaster response depends on the correctness and timeliness of data regarding the location and the impact of the event. These two issues are critical when the data come from citizens’ tweets, since the automatic classification of disaster-related tweets suffers from [...] Read more.
The effectiveness of disaster response depends on the correctness and timeliness of data regarding the location and the impact of the event. These two issues are critical when the data come from citizens’ tweets, since the automatic classification of disaster-related tweets suffers from many shortcomings. In this paper, we explore an approach based on participatory sensing (i.e., a subset of mobile crowdsourcing that emphasizes the active and intentional participation of citizens to collect data from the place where they live or work). We operate with the hypothesis of a “friendly world”, that is by assuming that after a calamitous event, in the survivors prevails the feeling of helping those who suffer. The extraction, from the Twitter repository, of the few tweets relevant to the event of interest has a long processing time. With the aggravating circumstance in the phase that follows a severe earthquake, the elaboration of tweets clashes with the need to act promptly. Our proposal allows a huge reduction of the processing time. This goal is reached by introducing a service and a mobile app, the latter is an intermediate tool between Twitter and the citizens, suitable to assist them to write structured messages that act as surrogates of tweets. The article describes the architecture of the software service and the steps involved in the retrieval, from the Twitter server, of the messages coming from citizens living in the places hit by the earthquake; moreover, it details the storage of those messages into a geographical database and their processing using SQL. Full article
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Open AccessArticle Accurate Reconstruction of the LoD3 Building Model by Integrating Multi-Source Point Clouds and Oblique Remote Sensing Imagery
ISPRS Int. J. Geo-Inf. 2019, 8(3), 135; https://doi.org/10.3390/ijgi8030135
Received: 1 February 2019 / Revised: 2 March 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
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Abstract
3D urban building models, which provide 3D information services for urban planning, management and operational decision-making, are essential for constructing digital cities. Unfortunately, the existing reconstruction approaches for LoD3 building models are insufficient in model details and are associated with a heavy workload, [...] Read more.
3D urban building models, which provide 3D information services for urban planning, management and operational decision-making, are essential for constructing digital cities. Unfortunately, the existing reconstruction approaches for LoD3 building models are insufficient in model details and are associated with a heavy workload, and accordingly they could not satisfy urgent requirements of realistic applications. In this paper, we propose an accurate LoD3 building reconstruction method by integrating multi-source laser point clouds and oblique remote sensing imagery. By combing high-precision plane features extracted from point clouds and accurate boundary constraint features from oblique images, the building mainframe model, which provides an accurate reference for further editing, is quickly and automatically constructed. Experimental results show that the proposed reconstruction method outperforms existing manual and automatic reconstruction methods using both point clouds and oblique images in terms of reconstruction efficiency and spatial accuracy. Full article
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Open AccessArticle From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data
ISPRS Int. J. Geo-Inf. 2019, 8(3), 134; https://doi.org/10.3390/ijgi8030134
Received: 29 January 2019 / Revised: 23 February 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
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Abstract
The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users’ motion traces to construct a behavioral [...] Read more.
The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users’ motion traces to construct a behavioral representation of places based on how people move between them, ignoring geographical coordinates and spatial proximity. Inspired by natural language processing techniques, we generate and explore vector representations of locations, traces and visitors, obtained through an unsupervised machine learning approach, which we generically named motion-to-vector (Mot2vec), trained on large-scale mobility data. The algorithm consists of two steps, the trajectory pre-processing and the Word2vec-based model building. First, mobility traces are converted into sequences of locations that unfold in fixed time steps; then, a Skip-gram Word2vec model is used to construct the location embeddings. Trace and visitor embeddings are finally created combining the location vectors belonging to each trace or visitor. Mot2vec provides a meaningful representation of locations, based on the motion behavior of users, defining a direct way of comparing locations’ connectivity and providing analogous similarity distributions for places of the same type. In addition, it defines a metric of similarity for traces and visitors beyond their spatial proximity and identifies common motion behaviors between different categories of people. Full article
(This article belongs to the Special Issue Spatial Data Science)
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Open AccessArticle Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method
ISPRS Int. J. Geo-Inf. 2019, 8(3), 133; https://doi.org/10.3390/ijgi8030133
Received: 10 January 2019 / Revised: 27 February 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
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Abstract
In recent years, various types of terrorist attacks have occurred which have caused worldwide catastrophes. The ability to proactively detect and even predict a potential terrorist risk is critically important for government agencies to react in a timely manner. In this study, a [...] Read more.
In recent years, various types of terrorist attacks have occurred which have caused worldwide catastrophes. The ability to proactively detect and even predict a potential terrorist risk is critically important for government agencies to react in a timely manner. In this study, a method of geospatial statistics was used to analyse the spatio-temporal evolution of terrorist attacks on the Indochina Peninsula. The machine learning random forest (RF) method was adopted to predict the potential risk of terrorist attacks on the Indochina Peninsula on a spatial scale with 15 driving factors. The RF model performed well with AUC values of 0.839 [95% confidence interval of 0.833–0.844]. The map of the potential distribution of terrorist attack risk was obtained with a 0.05×0.05-degree (approximately 5×5 km) resolution. The results indicate that Thailand is the most dangerous area for terrorist attacks, especially southern Thailand, Bangkok and its surrounding cities. Middle Cambodia and the northern and southern parts of Myanmar are also high-risk areas. Other areas are relatively low risk. This study provides the hotspots for terrorist attacks on a more fine-grained geographical unit. Meanwhile, it shows that machine learning algorithms (e.g., RF) combined with GIS have great potential for simulating the risk of terrorist attacks. Full article
(This article belongs to the Special Issue GIS for Safety & Security Management)
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Open AccessArticle Combining Object-Based Image Analysis with Topographic Data for Landform Mapping: A Case Study in the Semi-Arid Chaco Ecosystem, Argentina
ISPRS Int. J. Geo-Inf. 2019, 8(3), 132; https://doi.org/10.3390/ijgi8030132
Received: 15 December 2018 / Revised: 1 March 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
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Abstract
This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. [...] Read more.
This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. The research was conducted in two stages. The first stage focused on basic-spectral landform classifications where both pixel- and object-based image analyses were tested with five classification algorithms: Mahalanobis Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM) and Decision Tree (DT). The results obtained indicate that object-based analyses clearly outperform pixel-based classifications, with an increase in accuracy of up to 35%. The second stage focused on advanced object-based derived variables with topographic ancillary data classifications. The combinations of variables were tested in order to obtain the most accurate map of landforms based on the most successful classifiers identified in the previous stage (ML, SVM and DT). The results indicate that DT is the most accurate classifier, exhibiting the highest overall accuracies with values greater than 72% in both the winter and summer images. Future work could combine both, the most appropriate methodologies and combinations of variables obtained in this study, with physico-chemical variables sampled to improve the classification of landforms and even of types of soil. Full article
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Open AccessArticle A QGIS Tool for Automatically Identifying Asbestos Roofing
ISPRS Int. J. Geo-Inf. 2019, 8(3), 131; https://doi.org/10.3390/ijgi8030131
Received: 25 January 2019 / Revised: 10 February 2019 / Accepted: 24 February 2019 / Published: 6 March 2019
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Abstract
Exposure to asbestos fibers implies a long-term risk for human health; therefore, the development of information systems that are able to detect the extent and status of asbestos over a certain territory has become a priority. This work presents a tool (based on [...] Read more.
Exposure to asbestos fibers implies a long-term risk for human health; therefore, the development of information systems that are able to detect the extent and status of asbestos over a certain territory has become a priority. This work presents a tool (based on the geographic information system open source software, QGIS) that is conceived for automatically identifying buildings with asbestos roofing. The area under investigation is the metropolitan area around Prato (Italy). The performance analysis of this system was carried out by classifying images that were acquired by the WorldView-3 sensor. These images are available at a low cost when compared with those obtained by means of aerial surveys, and they provide adequate resolution levels for roofing classification. The tool, a QGIS plugin, has shown fairly good performance in identifying asbestos roofing, with some false negatives and some false positives when applying a per-pixel classification. A performance improvement is obtainable when considering the percentage of asbestos pixels that are contained in each roof of the analyzed image. This value is also available with the plugin. In the future, this tool should make it possible to monitor the asbestos roof removal process over time in the area of interest, in accordance with other image data that give evidence of such removals. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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Open AccessArticle NoiseModelling: An Open Source GIS Based Tool to Produce Environmental Noise Maps
ISPRS Int. J. Geo-Inf. 2019, 8(3), 130; https://doi.org/10.3390/ijgi8030130
Received: 8 February 2019 / Revised: 23 February 2019 / Accepted: 26 February 2019 / Published: 4 March 2019
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Abstract
The urbanisation phenomenon and related cities expansion and transport networks entail preventing the increase of population exposed to environmental pollution. Regarding noise exposure, the Environmental Noise Directive demands on main metropolis to produce noise maps. While based on standard methods, these latter are [...] Read more.
The urbanisation phenomenon and related cities expansion and transport networks entail preventing the increase of population exposed to environmental pollution. Regarding noise exposure, the Environmental Noise Directive demands on main metropolis to produce noise maps. While based on standard methods, these latter are usually generated by proprietary software and require numerous input data concerning, for example, the buildings, land use, transportation network and traffic. The present work describes an open source implementation of a noise mapping tool fully implemented in a Geographic Information System compliant with the Open Geospatial Consortium standards. This integration makes easier at once the formatting and harvesting of noise model input data, cartographic rendering and output data linkage with population data. An application is given for a French city, which consists in estimating the impact of road traffic-related scenarios in terms of population exposure to noise levels in relation to both a threshold value and level classes. Full article
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Open AccessArticle Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk
ISPRS Int. J. Geo-Inf. 2019, 8(3), 129; https://doi.org/10.3390/ijgi8030129
Received: 1 October 2018 / Revised: 19 February 2019 / Accepted: 23 February 2019 / Published: 4 March 2019
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Abstract
Recognition and characterization of built-up areas in the Siberian sub-Arctic urban territories of Yakutsk are dependent on two main factors: (1) the season (snow and ice from October to the end of April, the flooding period in May, and the summertime), which influences [...] Read more.
Recognition and characterization of built-up areas in the Siberian sub-Arctic urban territories of Yakutsk are dependent on two main factors: (1) the season (snow and ice from October to the end of April, the flooding period in May, and the summertime), which influences the accuracy of urban object detection, and (2) the urban structure, which influences the morphological recognition and characterization of built-up areas. In this study, high repetitiveness remote sensing Sentinel-2A and SPOT 6 high-resolution satellite images were combined to characterize and detect urban built-up areas over the city of Yakutsk. High temporal resolution of Sentinel-2A allows land use change detection and metric spatial resolution of SPOT 6 allows the characterization of built-up areas’ socioeconomic functions and uses. Full article
(This article belongs to the Special Issue GEOBIA in a Changing World)
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Open AccessArticle Bangkok CCTV Image through a Road Environment Extraction System Using Multi-Label Convolutional Neural Network Classification
ISPRS Int. J. Geo-Inf. 2019, 8(3), 128; https://doi.org/10.3390/ijgi8030128
Received: 30 January 2019 / Accepted: 24 February 2019 / Published: 4 March 2019
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Abstract
Information regarding the conditions of roads is a safety concern when driving. In Bangkok, public weather sensors such as weather stations and rain sensors are insufficiently available to provide such information. On the other hand, a number of existing CCTV cameras have been [...] Read more.
Information regarding the conditions of roads is a safety concern when driving. In Bangkok, public weather sensors such as weather stations and rain sensors are insufficiently available to provide such information. On the other hand, a number of existing CCTV cameras have been deployed recently in various places for surveillance and traffic monitoring. Instead of deploying new sensors designed specifically for monitoring road conditions, images and location information from existing cameras can be used to obtain precise environmental information. Therefore, we propose a road environment extraction framework that covers different situations, such as raining and non-raining scenes, daylight and night-time scenes, crowded and non-crowded traffic, and wet and dry roads. The framework is based on CCTV images from a Bangkok metropolitan dataset, provided by the Bangkok Metropolitan Administration. To obtain information from CCTV image sequences, multi-label classification was considered by applying a convolutional neural network. We also compared various models, including transfer learning techniques, and developed new models in order to obtain optimum results in terms of performance and efficiency. By adding dropout and batch normalization techniques, our model could acceptably perform classification with only a few convolutional layers. Our evaluation showed a Hamming loss and exact match ratio of 0.039 and 0.84, respectively. Finally, a road environment monitoring system was implemented to test the proposed framework. Full article
(This article belongs to the Special Issue Innovative Sensing - From Sensors to Methods and Applications)
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