Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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16 pages, 42937 KiB  
Article
A Smooth Transition Algorithm for Adjacent Panoramic Viewpoints Using Matched Delaunay Triangular Patches
by Pengcheng Zhao, Qingwu Hu, Zhixiong Tang and Mingyao Ai
ISPRS Int. J. Geo-Inf. 2020, 9(10), 596; https://doi.org/10.3390/ijgi9100596 - 10 Oct 2020
Cited by 3 | Viewed by 3404
Abstract
The unnatural panoramic image transition between two adjacent viewpoints reduces the immersion and interactive experiences of 360° panoramic walkthrough systems. In this paper, a dynamic panoramic image rendering and smooth transition algorithm for adjacent viewpoints is proposed. First, the feature points of adjacent [...] Read more.
The unnatural panoramic image transition between two adjacent viewpoints reduces the immersion and interactive experiences of 360° panoramic walkthrough systems. In this paper, a dynamic panoramic image rendering and smooth transition algorithm for adjacent viewpoints is proposed. First, the feature points of adjacent view images are extracted, a robust matching algorithm is used to establish adjacent point pairs, and the matching triangles are formed by using the homonymous points. Then, a dynamic transition model is formed by the simultaneous linear transitions of shape and texture for each control triangle. Finally, the smooth transition between adjacent viewpoints is implemented by overlaying the dynamic transition model with the 360° panoramic walkthrough scene. Experimental results show that this method has obvious advantages in visual representation with distinct visual movement. It can realize the smooth transition between two indoor panoramic stations with arbitrary station spacing, and its execution efficiency is up to 50 frames per second. It effectively enhances the interactivity and immersion of 360° panoramic walkthrough systems. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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15 pages, 4010 KiB  
Technical Note
PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy
by Ding Ma, Zhigang Zhao, Ye Zheng, Renzhong Guo and Wei Zhu
ISPRS Int. J. Geo-Inf. 2020, 9(10), 594; https://doi.org/10.3390/ijgi9100594 - 10 Oct 2020
Cited by 5 | Viewed by 2999
Abstract
Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. [...] Read more.
Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. A geographic feature can be deemed as a fractal given the perspective of scaling, as its rough, irregular, and unsmooth shape inherently holds a striking scaling hierarchy of far more small elements than large ones. The pattern of far more small things than large ones is a de facto heavy tailed distribution. In this paper, we apply the scaling hierarchy for map generalization to polygonal features. To do this, we firstly revisit the scaling hierarchy of a classic fractal: the Koch Snowflake. We then review previous work that used the Douglas–Peuker algorithm, which identifies characteristic points on a line to derive three types of measures that are long-tailed distributed: the baseline length (d), the perpendicular distance to the baseline (x), and the area formed by x and d (area). More importantly, we extend the usage of the three measures to other most popular cartographical generalization methods; i.e., the bend simplify method, Visvalingam–Whyatt method, and hierarchical decomposition method, each of which decomposes any polygon into a set of bends, triangles, or convex hulls as basic geometric units for simplification. The different levels of details of the polygon can then be derived by recursively selecting the head part of geometric units and omitting the tail part using head/tail breaks, which is a new classification scheme for data with a heavy-tailed distribution. Since there are currently few tools with which to readily conduct the polygon simplification from such a fractal perspective, we have developed PolySimp, a tool that integrates the mentioned four algorithms for polygon simplification based on its underlying scaling hierarchy. The British coastline was selected to demonstrate the tool’s usefulness. The developed tool can be expected to showcase the applicability of fractal way of thinking and contribute to the development of map generalization. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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34 pages, 9315 KiB  
Article
A Comparative Study of Several Metaheuristic Algorithms to Optimize Monetary Incentive in Ridesharing Systems
by Fu-Shiung Hsieh
ISPRS Int. J. Geo-Inf. 2020, 9(10), 590; https://doi.org/10.3390/ijgi9100590 - 08 Oct 2020
Cited by 25 | Viewed by 2688
Abstract
The strong demand on human mobility leads to excessive numbers of cars and raises the problems of serious traffic congestion, large amounts of greenhouse gas emissions, air pollution and insufficient parking space in cities. Although ridesharing is a potential transport mode to solve [...] Read more.
The strong demand on human mobility leads to excessive numbers of cars and raises the problems of serious traffic congestion, large amounts of greenhouse gas emissions, air pollution and insufficient parking space in cities. Although ridesharing is a potential transport mode to solve the above problems through car-sharing, it is still not widely adopted. Most studies consider non-monetary incentive performance indices such as travel distance and successful matches in ridesharing systems. These performance indices fail to provide a strong incentive for ridesharing. The goal of this paper is to address this issue by proposing a monetary incentive performance indicator to improve the incentives for ridesharing. The objectives are to improve the incentive for ridesharing through a monetary incentive optimization problem formulation, development of a solution methodology and comparison of different solution algorithms. A non-linear integer programming optimization problem is formulated to optimize monetary incentive in ridesharing systems. Several discrete metaheuristic algorithms are developed to cope with computational complexity for solving the above problem. These include several discrete variants of particle swarm optimization algorithms, differential evolution algorithms and the firefly algorithm. The effectiveness of applying the above algorithms to solve the monetary incentive optimization problem is compared based on experimental results. Full article
(This article belongs to the Special Issue GIS in Sustainable Transportation)
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30 pages, 20417 KiB  
Article
A Built Heritage Information System Based on Point Cloud Data: HIS-PC
by Florent Poux, Roland Billen, Jean-Paul Kasprzyk, Pierre-Henri Lefebvre and Pierre Hallot
ISPRS Int. J. Geo-Inf. 2020, 9(10), 588; https://doi.org/10.3390/ijgi9100588 - 07 Oct 2020
Cited by 16 | Viewed by 3899
Abstract
The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling, archaeological or heritage information systems now tend to propose a common framework where all the materials [...] Read more.
The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling, archaeological or heritage information systems now tend to propose a common framework where all the materials are managed from a central database and visualised through a 3D representation. In this research, we offer the development of a built heritage information system prototype based on a high-resolution 3D point cloud data set. The particularity of the approach is to consider a user-centred development methodology while avoiding meshing/down-sampling operations. The proposed system is initiated by a close collaboration between multi-modal users (managers, visitors, curators) and a development team (designers, developers, architects). The developed heritage information system permits the management of spatial and temporal information, including a wide range of semantics using relational along with NoSQL databases. The semantics used to describe the artifacts are subject to conceptual modelling. Finally, the system proposes a bi-directional communication with a 3D interface able to stream massive point clouds, which is a big step forward to provide a comprehensive site representation for stakeholders while minimising modelling costs. Full article
(This article belongs to the Special Issue BIM for Cultural Heritage (HBIM))
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15 pages, 2567 KiB  
Article
Evaluation of the Space Syntax Measures Affecting Pedestrian Density through Ordinal Logistic Regression Analysis
by Özge Öztürk Hacar, Fatih Gülgen and Serdar Bilgi
ISPRS Int. J. Geo-Inf. 2020, 9(10), 589; https://doi.org/10.3390/ijgi9100589 - 07 Oct 2020
Cited by 6 | Viewed by 3610
Abstract
This paper examines the relationship between pedestrian density and space syntax measures in a university campus using ordinal logistic regression analysis. The pedestrian density assumed as the dependent variable of regression analysis was categorised in low, medium, and high classes by using Jenks [...] Read more.
This paper examines the relationship between pedestrian density and space syntax measures in a university campus using ordinal logistic regression analysis. The pedestrian density assumed as the dependent variable of regression analysis was categorised in low, medium, and high classes by using Jenks natural break classification. The data elements of groups were derived from pedestrian counts performed in 22 gates 132 times. The counting period grouped in nominal categories was assumed as an independent variable. Another independent was one of the 15 derived measures of axial analysis and visual graphic analysis. The statistically significant model results indicated that the integration of axial analysis was the most reasonable measure that explained the pedestrian density. Then, the changes in integration values of current and master plan datasets were analysed using paired sample t-test. The calculated p-value of t-test proved that the master plan would change the campus morphology for pedestrians. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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27 pages, 4612 KiB  
Article
Supporting Policy Design for the Diffusion of Cleaner Technologies: A Spatial Empirical Agent-Based Model
by Caterina Caprioli, Marta Bottero and Elena De Angelis
ISPRS Int. J. Geo-Inf. 2020, 9(10), 581; https://doi.org/10.3390/ijgi9100581 - 01 Oct 2020
Cited by 10 | Viewed by 2819
Abstract
Renewable energy resources and energy-efficient technologies, as well as building retrofitting, are only some of the possible strategies that can achieve more sustainable cities and reduce greenhouse gas emissions. Subsidies and incentives are often provided by governments to increase the number of people [...] Read more.
Renewable energy resources and energy-efficient technologies, as well as building retrofitting, are only some of the possible strategies that can achieve more sustainable cities and reduce greenhouse gas emissions. Subsidies and incentives are often provided by governments to increase the number of people adopting these sustainable energy efficiency actions. However, actual sales of green products are currently not as high as would be desired. The present paper applies a hybrid agent-based model (ABM) integrated with a Geographic Information System (GIS) to simulate a complex socio-economic-architectural adaptive system to study the temporal diffusion and the willingness of inhabitants to adopt photovoltaic (PV) systems. The San Salvario neighborhood in Turin (Italy) is used as an exemplary case study for testing consumer behavior associated with this technology, integrating social network theories, opinion formation dynamics and an adaptation of the theory of planned behavior (TPB). Data/characteristics for both buildings and people are explicitly spatialized with the level of detail at the block scale. Particular attention is given to the comparison of the policy mix for supporting decision-makers and policymakers in the definition of the most efficient strategies for achieving a long-term vision of sustainable development. Both variables and outcomes accuracy of the model are validated with historical real-world data. Full article
(This article belongs to the Special Issue Geo-Information Science in Planning and Development of Smart Cities)
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18 pages, 9387 KiB  
Article
Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm
by Nikiforos Samarinas, Nikolaos Tziolas and George Zalidis
ISPRS Int. J. Geo-Inf. 2020, 9(10), 576; https://doi.org/10.3390/ijgi9100576 - 30 Sep 2020
Cited by 5 | Viewed by 2660
Abstract
The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not [...] Read more.
The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale. Full article
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17 pages, 8014 KiB  
Article
Quantitative Evaluation of Spatial Differentiation for Public Open Spaces in Urban Built-Up Areas by Assessing SDG 11.7: A Case of Deqing County
by Qiang Chen, Mingyi Du, Qianhao Cheng and Changfeng Jing
ISPRS Int. J. Geo-Inf. 2020, 9(10), 575; https://doi.org/10.3390/ijgi9100575 - 30 Sep 2020
Cited by 11 | Viewed by 2591
Abstract
Urban public open spaces refer to open space between architectural structures in a city or urban agglomeration that is open for urban residents to conduct public exchanges and hold various activities. Sustainable Development Goal (SDG) 11.7 in the 2030 UN Agenda for Sustainable [...] Read more.
Urban public open spaces refer to open space between architectural structures in a city or urban agglomeration that is open for urban residents to conduct public exchanges and hold various activities. Sustainable Development Goal (SDG) 11.7 in the 2030 UN Agenda for Sustainable Development clearly states that the distribution characteristics of public open spaces are important indicators to measure the sustainable development of urban ecological society. In 2018, in order to implement the sustainable development agenda, China offered the example of Deqing to the world. Therefore, taking Deqing as an example, this paper uses geographic statistics and spatial analysis methods to quantitatively evaluate and visualize public open spaces in the built area in 2016 and analyzes the spatial pattern and relationship of the population. The results show that the public open spaces in the built-up area of Deqing have typical global and local spatial autocorrelation. The spatial pattern shows obvious differences in different parts of the built area and attributes of public open spaces. According to the results of correlation analysis, it can be seen that the decentralized characteristics of public open spaces have a significant relationship with the population agglomeration, and this correlation is also related to the types of public open spaces. The assessment results by SDG 11.7.1 indicate that the public open spaces in the built-up area of Deqing conform to the living needs of residents on the whole and have a humanized space design and good accessibility. However, the per capita public open spaces of towns and villages outside the built area are relatively low, and there is an imbalance in public open spaces. Therefore, more attention should be paid to constructing urban public open spaces fairly. Full article
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26 pages, 44915 KiB  
Article
Emergency Department Overcrowding: A Retrospective Spatial Analysis and the Geocoding of Accesses. A Pilot Study in Rome
by Cristiano Pesaresi, Giuseppe Migliara, Davide Pavia and Corrado De Vito
ISPRS Int. J. Geo-Inf. 2020, 9(10), 579; https://doi.org/10.3390/ijgi9100579 - 30 Sep 2020
Cited by 6 | Viewed by 4665
Abstract
The overcrowding of first aid facilities creates considerable hardship and problems which have repercussions on patients’ wellbeing, the time needed for a diagnosis, and on the quality of the assistance. The basic objective of this contribution, based on the data collected by the [...] Read more.
The overcrowding of first aid facilities creates considerable hardship and problems which have repercussions on patients’ wellbeing, the time needed for a diagnosis, and on the quality of the assistance. The basic objective of this contribution, based on the data collected by the Hospital Policlinico Umberto I in Rome (Lazio region, Italy), is to carry out a territorial screening of the municipality using GIS applications and spatial analyses aimed at reducing—in terms of triage—code white (inappropriate) attendances, after having identified the areas of greatest provenance of improperly used emergency room access. Working in a GIS environment and using functions for geocoding, we have tested an experimental model aimed at giving a close-up geographical-sanitary look at the situation: recognizing the territorial sectors in Rome which contribute to amplifying the Policlinico Umberto I emergency room overcrowding; leading up to an improvement of the situation; promoting greater awareness and knowledge of the services available on the territory, a closer relationship between patient and regular doctor (general practitioner, GP) or Local Healthcare Unit and a more efficient functioning of the emergency room. In particular, we have elaborated a “source” map from which derive all the others and it is a dot map on which all the codes white have been geolocalized on a satellite image through geocoding. We have produced three sets made up of three digital cartographic elaborations each, constructed on the census sections, the census areas and the sub-municipal areas, according to data aggregation, for absolute and relative values, and using different templates. Finally, following the same methodology and steps, we elaborated another dot map about all the codes red to provide another kind of information and input for social utility. In the near future, this system could be tested on a platform that spatially analyzes the emergency department (ED) accesses in near-real-time in order to facilitate the identification of critical territorial issues and intervene in a shorter time to regulate the influx of patients to the ED. Full article
(This article belongs to the Special Issue Geo-Information Science in Planning and Development of Smart Cities)
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20 pages, 2407 KiB  
Article
Evaluating the Performance of Three Popular Web Mapping Libraries: A Case Study Using Argentina’s Life Quality Index
by Alejandro Zunino, Guillermo Velázquez, Juan Pablo Celemín, Cristian Mateos, Matías Hirsch and Juan Manuel Rodriguez
ISPRS Int. J. Geo-Inf. 2020, 9(10), 563; https://doi.org/10.3390/ijgi9100563 - 29 Sep 2020
Cited by 6 | Viewed by 5339
Abstract
Recent Web technologies such as HTML5, JavaScript, and WebGL have enabled powerful and highly dynamic Web mapping applications executing on standard Web browsers. Despite the complexity for developing such applications has been greatly reduced by Web mapping libraries, developers face many choices to [...] Read more.
Recent Web technologies such as HTML5, JavaScript, and WebGL have enabled powerful and highly dynamic Web mapping applications executing on standard Web browsers. Despite the complexity for developing such applications has been greatly reduced by Web mapping libraries, developers face many choices to achieve optimal performance and network usage. This scenario is even more complex when considering different representations of geographical data (raster, raw data or vector) and variety of devices (tablets, smartphones, and personal computers). This paper compares the performance and network usage of three popular JavaScript Web mapping libraries for implementing a Web map using different representations for geodata, and executing on different devices. In the experiments, Mapbox GL JS achieved the best overall performance on mid and high end devices for displaying raster or vector maps, while OpenLayers was the best for raster maps on all devices. Vector-based maps are a safe bet for new Web maps, since performance is on par with raster maps on mid-end smartphones, with significant less network bandwidth requirements. Full article
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20 pages, 8298 KiB  
Article
A Method for 3D Reconstruction of the Ming and Qing Official-Style Roof Using a Decorative Components Template Library
by Pengpeng Huo, Miaole Hou, Youqiang Dong, Aiqun Li, Yuhang Ji and Songnian Li
ISPRS Int. J. Geo-Inf. 2020, 9(10), 570; https://doi.org/10.3390/ijgi9100570 - 29 Sep 2020
Cited by 10 | Viewed by 3866
Abstract
The ancient roof decorative components of the official-style architectures from the Ming and Qing dynasties in China hold both physical and symbolic significance. These roof structures are the essential objects in three-dimensional (3D) modeling of ancient architectures for traditional Chinese cultural preservation. Although [...] Read more.
The ancient roof decorative components of the official-style architectures from the Ming and Qing dynasties in China hold both physical and symbolic significance. These roof structures are the essential objects in three-dimensional (3D) modeling of ancient architectures for traditional Chinese cultural preservation. Although ancient architectures can be surveyed by a 3D laser scanner, the complex geometry and diverse pattern of their roof decorative components make the 3D point cloud reconstruction challenging, or at some points, nearly impossible in a fully automated manner. In this paper, we propose a method to ensure that the 3D shape of each roof decorative component is accurately modeled. First, we establish a decorative components template library (or “template library” in short hereafter), which is the first of its kind for the roofs of Ming and Qing official-style architectures. The process of establishing the decorative components template library begins with a remote collection of survey data using a terrestrial laser scanner and digital camera. The next stage involves the design and construction of different 3D decorative components in the template library with reference to the manuscripts written in the Ming and Qing dynasties’ architectural pattern books. With the point cloud data collected on any Ming and Qing official-style architecture, we further propose a geo-registration mechanism to search for an optimal fitting of the decorative components from the template library on the collected point cloud automatically. Based on the experimental results, the accuracy of point cloud registration yields less than 0.02 m, which meets the accuracy of the 3D model at LoD 300 level. Time consumption is less than 5s and stable, for large volume computing capacity has good robustness. The proposed strategy provides a new way for the 3D modeling of large and clustered historical architectures, particularly with complex structures. Full article
(This article belongs to the Special Issue BIM for Cultural Heritage (HBIM))
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22 pages, 3891 KiB  
Article
Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform
by Elgar Barboza Castillo, Efrain Y. Turpo Cayo, Cláudia Maria de Almeida, Rolando Salas López, Nilton B. Rojas Briceño, Jhonsy Omar Silva López, Miguel Ángel Barrena Gurbillón, Manuel Oliva and Raul Espinoza-Villar
ISPRS Int. J. Geo-Inf. 2020, 9(10), 564; https://doi.org/10.3390/ijgi9100564 - 29 Sep 2020
Cited by 38 | Viewed by 7257
Abstract
During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study [...] Read more.
During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively. Full article
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31 pages, 18687 KiB  
Article
An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping
by Omid Ghorbanzadeh, Khalil Didehban, Hamid Rasouli, Khalil Valizadeh Kamran, Bakhtiar Feizizadeh and Thomas Blaschke
ISPRS Int. J. Geo-Inf. 2020, 9(10), 561; https://doi.org/10.3390/ijgi9100561 - 27 Sep 2020
Cited by 13 | Viewed by 3701
Abstract
In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering [...] Read more.
In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor. Full article
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
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23 pages, 49398 KiB  
Article
Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning
by Nida Qayyum, Sajid Ghuffar, Hafiz Mughees Ahmad, Adeel Yousaf and Imran Shahid
ISPRS Int. J. Geo-Inf. 2020, 9(10), 560; https://doi.org/10.3390/ijgi9100560 - 25 Sep 2020
Cited by 44 | Viewed by 6941
Abstract
Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability [...] Read more.
Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring. Full article
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23 pages, 4038 KiB  
Article
Visit Probability in Space–Time Prisms Based on Binomial Random Walk
by Deepak Elias and Bart Kuijpers
ISPRS Int. J. Geo-Inf. 2020, 9(9), 555; https://doi.org/10.3390/ijgi9090555 - 18 Sep 2020
Cited by 1 | Viewed by 2301
Abstract
Space–time prisms are used to model the uncertainty of space–time locations of moving objects between (for instance, GPS-measured) sample points. However, not all space–time points in a prism are equally likely and we propose a simple, formal model for the so-called “visit probability” [...] Read more.
Space–time prisms are used to model the uncertainty of space–time locations of moving objects between (for instance, GPS-measured) sample points. However, not all space–time points in a prism are equally likely and we propose a simple, formal model for the so-called “visit probability” of space–time points within prisms. The proposed mathematical framework is based on a binomial random walk within one- and two-dimensional space–time prisms. Without making any assumptions on the random walks (we do not impose any distribution nor introduce any bias towards the second anchor point), we arrive at the conclusion that binomial random walk-based visit probability in space–time prisms corresponds to a hypergeometric distribution. Full article
(This article belongs to the Special Issue Human Dynamics Research in the Age of Smart and Intelligent Systems)
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44 pages, 14019 KiB  
Article
The City of Tomorrow from… the Data of Today
by Giorgio Agugiaro, Francisco Gabriel García González and Roberto Cavallo
ISPRS Int. J. Geo-Inf. 2020, 9(9), 554; https://doi.org/10.3390/ijgi9090554 - 16 Sep 2020
Cited by 8 | Viewed by 4980
Abstract
In urban planning, a common unit of measure for housing density is the number of households per hectare. However, the actual size of the physical space occupied by a household, i.e., a dwelling, is seldom considered, neither in 2D nor in 3D. This [...] Read more.
In urban planning, a common unit of measure for housing density is the number of households per hectare. However, the actual size of the physical space occupied by a household, i.e., a dwelling, is seldom considered, neither in 2D nor in 3D. This article proposes a methodology to estimate the average size of a dwelling in existing urban areas from available open data, and to use it as one of the design parameters for new urban-development projects. The proposed unit of measure, called “living space”, includes outdoor and indoor spaces. The idea is to quantitatively analyze the city of today to help design the city of tomorrow. First, the “typical”-dwelling size and a series of Key Performance Indicators are computed for all neighborhoods from a semantic 3D city model and other spatial and non-spatial datasets. A limited number of neighborhoods is selected based on their similarities with the envisioned development plan. The size of the living space of the selected neighborhoods is successively used as a design parameter to support the computer-assisted generation of several design proposals. Each proposal can be exported, shared, and visualized online. As a test case, a to-be-planned neighborhood in Amsterdam, called “Sloterdijk One”, has been chosen. Full article
(This article belongs to the Special Issue The Applications of 3D-City Models in Urban Studies)
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15 pages, 1623 KiB  
Review
GIS-Based Emotional Computing: A Review of Quantitative Approaches to Measure the Emotion Layer of Human–Environment Relationships
by Yingjing Huang, Teng Fei, Mei-Po Kwan, Yuhao Kang, Jun Li, Yizhuo Li, Xiang Li and Meng Bian
ISPRS Int. J. Geo-Inf. 2020, 9(9), 551; https://doi.org/10.3390/ijgi9090551 - 15 Sep 2020
Cited by 22 | Viewed by 5544
Abstract
In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human–environment relationship is proposed for enriching traditional methods [...] Read more.
In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human–environment relationship is proposed for enriching traditional methods of various related disciplines such as urban planning. This paper proposes the geographic information system (GIS)-based emotional computing concept, which is a novel framework for applying GIS methods to collective human emotion. The methodology presented in this paper consists of three key steps: (1) collecting georeferenced data containing emotion and environment information such as social media and official sites, (2) detecting emotions using AI-based emotional computing technics such as natural language processing (NLP) and computer vision (CV), and (3) visualizing and analyzing the spatiotemporal patterns with GIS tools. This methodology is a great synergy of multidisciplinary cutting-edge techniques, such as GIScience, sociology, and computer science. Moreover, it can effectively and deeply explore the connection between people and their surroundings with the help of GIS methods. Generally, the framework provides a standard workflow to calculate and analyze the new information layer for researchers, in which a measured human-centric perspective onto the environment is possible. Full article
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19 pages, 7973 KiB  
Article
Social Sensing for Urban Land Use Identification
by Adindha Surya Anugraha, Hone-Jay Chu and Muhammad Zeeshan Ali
ISPRS Int. J. Geo-Inf. 2020, 9(9), 550; https://doi.org/10.3390/ijgi9090550 - 15 Sep 2020
Cited by 9 | Viewed by 3256
Abstract
The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is [...] Read more.
The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses. Full article
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24 pages, 24446 KiB  
Article
Drift Invariant Metric Quality Control of Construction Sites Using BIM and Point Cloud Data
by Maarten Bassier, Stan Vincke, Heinder De Winter and Maarten Vergauwen
ISPRS Int. J. Geo-Inf. 2020, 9(9), 545; https://doi.org/10.3390/ijgi9090545 - 14 Sep 2020
Cited by 18 | Viewed by 3422
Abstract
Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by [...] Read more.
Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by high failure costs which are often caused by erroneously constructed structural objects. With the upcoming use of periodic remote sensing during the different phases of the building process, new possibilities arise to advance from a selective quality analysis to an in-depth assessment of the full construction site. In this work, a novel methodology is presented to rapidly evaluate a large number of built objects on a construction site. Given a point cloud and a set of as-design BIM elements, our method evaluates the deviations between both datasets and computes the positioning errors of each object. Unlike the current state of the art, our method computes the error vectors regardless of drift, noise, clutter and (geo)referencing errors, leading to a better detection rate. The main contributions are the efficient matching of both datasets, the drift invariant metric evaluation and the intuitive visualisation of the results. The proposed analysis facilitates the identification of construction errors early on in the process, hence significantly lowering the failure costs. The application is embedded in native BIM software and visualises the objects by a simple color code, providing an intuitive indicator for the positioning accuracy of the built objects. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and Modelling)
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21 pages, 9436 KiB  
Article
Identification and Geographic Distribution of Accommodation and Catering Centers
by Ze Han and Wei Song
ISPRS Int. J. Geo-Inf. 2020, 9(9), 546; https://doi.org/10.3390/ijgi9090546 - 14 Sep 2020
Cited by 12 | Viewed by 3320
Abstract
As the most important manifestation of the activities of the life service industry, the reasonable layout of spatial agglomeration and dispersion of the accommodation and catering industry plays an important role in guiding the spatial structure of the urban industry and population. Applying [...] Read more.
As the most important manifestation of the activities of the life service industry, the reasonable layout of spatial agglomeration and dispersion of the accommodation and catering industry plays an important role in guiding the spatial structure of the urban industry and population. Applying the contour tree and location quotient index methods, based on points of interest (POI) data of the accommodation and catering industry in Beijing and on the identification of the spatial structure and cluster center of the accommodation and catering industry, we investigated the distribution and agglomeration characteristics of the urban accommodation and catering industry from the perspective of industrial spatial differentiation. The results show that: (1) the accommodation and catering industry in Beijing presents a polycentric agglomeration pattern in space, mainly distributed within a radius of 20 km from the city center and on a relatively large scale; areas beyond this distance contain isolated single cluster centers. (2) From the perspective of the industry, the cluster centers close to the core area of the city are characterized by the agglomeration of multiple advantageous industries, while those in the outer suburbs of the city are more prominent in a single industry. (3) From the perspective of the location quotient of cluster centers, the leisure catering industries are mainly located close to the urban centers. On the contrary, the cluster centers in the outer suburbs and counties are relatively small and dominated by restaurants and fast food industries. Commercial accommodation businesses are mainly distributed in the transportation hub centers and in entertainment and leisure areas. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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20 pages, 988 KiB  
Article
STS: Spatial–Temporal–Semantic Personalized Location Recommendation
by Wenchao Li, Xin Liu, Chenggang Yan, Guiguang Ding, Yaoqi Sun and Jiyong Zhang
ISPRS Int. J. Geo-Inf. 2020, 9(9), 538; https://doi.org/10.3390/ijgi9090538 - 08 Sep 2020
Cited by 9 | Viewed by 2389
Abstract
The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial [...] Read more.
The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations, we propose a Gaussian process based model for each user to systematically and non-linearly combine temporal and spatial information to predict the user’s displacement from their currently checked-in location to the next one. The locations whose distances to the user’s current checked-in location are the closest to the predicted displacement are recommended. We also propose an enhancement to take into account category information of locations for semantic-aware recommendation. A unified recommendation framework called spatial–temporal–semantic (STS) is introduced to combine displacement prediction and the semantic-aware enhancement to provide final top-N recommendation. Extensive experiments over real datasets show that the proposed STS framework significantly outperforms the state-of-the-art location recommendation models in terms of precision and mean reciprocal rank (MRR). Full article
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22 pages, 26545 KiB  
Article
Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation
by Francesca Matrone, Eleonora Grilli, Massimo Martini, Marina Paolanti, Roberto Pierdicca and Fabio Remondino
ISPRS Int. J. Geo-Inf. 2020, 9(9), 535; https://doi.org/10.3390/ijgi9090535 - 07 Sep 2020
Cited by 77 | Viewed by 6233
Abstract
In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based [...] Read more.
In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)
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14 pages, 5336 KiB  
Article
Modeling Diurnal Changes in Land Surface Temperature in Urban Areas under Cloudy Conditions
by Jaroslav Hofierka, Jozef Bogľarský, Štefan Kolečanský and Anastasia Enderova
ISPRS Int. J. Geo-Inf. 2020, 9(9), 534; https://doi.org/10.3390/ijgi9090534 - 07 Sep 2020
Cited by 6 | Viewed by 3215
Abstract
Land surface temperature (LST) in urban areas is a dynamic phenomenon affected by various factors such as solar irradiance, cloudiness, wind or urban morphology. The problem complexity requires a comprehensive geographic information system (GIS)-based approach. Our solution is based on solar radiation tools, [...] Read more.
Land surface temperature (LST) in urban areas is a dynamic phenomenon affected by various factors such as solar irradiance, cloudiness, wind or urban morphology. The problem complexity requires a comprehensive geographic information system (GIS)-based approach. Our solution is based on solar radiation tools, a high-resolution digital surface model of urban areas, spatially distributed data representing thermal properties of urban surfaces and meteorological conditions. The methodology is implemented in GRASS GIS using shell scripts. In these shell scripts, the r.sun solar radiation model was used to calculate the effective solar irradiance for selected time horizons during the day. The calculation accounts for attenuation of beam solar irradiance by clouds estimated by field measurements. The suggested algorithm accounts for heat storage in urban structures depending on their thermal properties and geometric configuration. Computed land surface temperature was validated using field measurements of LST in 10 locations within the study area. The study confirmed the applicability of our approach with an acceptable accuracy expressed by the root mean square error of 3.45 K. The proposed approach has the advantage of providing high spatial detail coupled with the flexibility of GIS to evaluate various geometrical and land surface properties for any daytime horizon. Full article
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18 pages, 6691 KiB  
Article
Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework
by Jiantao Liu, Quanlong Feng, Ying Wang, Bayartungalag Batsaikhan, Jianhua Gong, Yi Li, Chunting Liu and Yin Ma
ISPRS Int. J. Geo-Inf. 2020, 9(9), 527; https://doi.org/10.3390/ijgi9090527 - 02 Sep 2020
Cited by 12 | Viewed by 2913
Abstract
With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has [...] Read more.
With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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19 pages, 6153 KiB  
Article
A Comprehensive Measurement of Progress toward Local SDGs with Geospatial Information: Methodology and Lessons Learned
by Jun Chen, Shu Peng, Hao Chen, Xuesheng Zhao, Yuejing Ge and Zhilin Li
ISPRS Int. J. Geo-Inf. 2020, 9(9), 522; https://doi.org/10.3390/ijgi9090522 - 01 Sep 2020
Cited by 14 | Viewed by 3966
Abstract
The UN’s 2030 Agenda defined 17 Sustainable Development Goals (SDGs). In order to ensure the implementation of this global agenda, the UN proposed a systematic follow-up and review through indicator-based tracking and reporting of the progress with statistical and geospatial information toward SDGs [...] Read more.
The UN’s 2030 Agenda defined 17 Sustainable Development Goals (SDGs). In order to ensure the implementation of this global agenda, the UN proposed a systematic follow-up and review through indicator-based tracking and reporting of the progress with statistical and geospatial information toward SDGs at national, regional, and global levels. This has posed many technical and institutional challenges. Although international communities have devoted great attention to this hot topic, most of their work has focused on the conceptual design and preliminary testing. There are very few good practices for a comprehensive measurement and assessment of progress toward SDGs with the integration of statistical and geospatial information at national or local levels. This paper presents the methodology and results of a pioneer project which measured the progress toward SDGs at a local level in China (i.e., Deqing County) by integrating statistical and geospatial information. In this study, a number of technical/institutional issues have been tackled, such as the adoption of appropriate indicators at a local level, availability and acquisition of reliable data sets, and spatiotemporal analysis with a geographical perspective, interaction between SDGs and cross-sector coordination. The major conclusions are (a) the comprehensive progress toward SDGs in Deqing can be most appropriately measured and assessed by integrating geospatial and statistical information; (b) Deqing has made significant economic and social advances while maintaining a good ecological environment over the past few years. The results were released at the first United Nations World Geospatial Information Congress as a good practice and a live example to stimulate discussions. Full article
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20 pages, 1447 KiB  
Article
An Overview of Social Media Apps and their Potential Role in Geospatial Research
by Innocensia Owuor and Hartwig H. Hochmair
ISPRS Int. J. Geo-Inf. 2020, 9(9), 526; https://doi.org/10.3390/ijgi9090526 - 01 Sep 2020
Cited by 17 | Viewed by 5174
Abstract
Social media apps provide analysts with a wide range of data to study behavioral aspects of our everyday lives and to answer societal questions. Although social media data analysis is booming, only a handful of prominent social media apps, such as Twitter, Foursquare/Swarm, [...] Read more.
Social media apps provide analysts with a wide range of data to study behavioral aspects of our everyday lives and to answer societal questions. Although social media data analysis is booming, only a handful of prominent social media apps, such as Twitter, Foursquare/Swarm, Facebook, or LinkedIn are typically used for this purpose. However, there is a large selection of less known social media apps that go unnoticed in the scientific community. This paper reviews 110 social media apps and assesses their potential usability in geospatial research through providing metrics on selected characteristics. About half of the apps (57 out of 110) offer an Application Programming Interface (API) for data access, where rate limits, fee models, and type of spatial data available for download vary strongly between the different apps. To determine the current role and relevance of social media platforms that offer an API in academic research, a search for scientific papers on Google Scholar, the Association for Computing Machinery (ACM) Digital Library, and the Science Core Collection of the Web of Science (WoS) is conducted. This search revealed that Google Scholar returns the highest number of documents (Mean = 183,512) compared to ACM (Mean = 1895) and WoS (Mean = 1495), and that data and usage patterns from prominent social media apps are more frequently analyzed in research studies than those of less known apps. The WoS citation database was also used to generate lists of themes covered in academic publications that analyze the 57 social media platforms that offer an API. Results show that among these 57 platforms, for 26 apps at least some papers evolve around a geospatial discipline, such as Geography, Remote Sensing, Transportation, or Urban Planning. This analysis, therefore, connects apps with commonly used research themes, and together with tabulated API characteristics can help researchers to identify potentially suitable social media apps for their research. Word clouds generated from titles and abstracts of papers associated with the 57 platforms, grouped into seven thematic categories, show further refinement of topics addressed in the analysis of social media platforms. Considering various evaluation criteria, such as provision of geospatial data or the number (i.e., absence) of currently published research papers in connection with a social media platform, the study concludes that among the numerous social media apps available today, 17 less known apps deserve closer examination since they might be used to investigate previously underexplored research topics. It is hoped that this study can serve as a reference for the analysis of the social media landscape in the future. Full article
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17 pages, 3405 KiB  
Article
School Commuting Mode Shift: A Scenario Analysis for Active School Commuting Using GIS and Online Map API
by Anqi Liu, Keone Kelobonye, Zhenqi Zhou, Qiuxia Xu, Zhen Xu and Lingyun Han
ISPRS Int. J. Geo-Inf. 2020, 9(9), 520; https://doi.org/10.3390/ijgi9090520 - 31 Aug 2020
Cited by 5 | Viewed by 3152
Abstract
Active school commuting provides a convenient opportunity to promote physical activity for children, while also reducing car dependence and its associated environmental impacts. School–home distance is a critical factor in school commuting mode choice, and longer distances have been proven to increase the [...] Read more.
Active school commuting provides a convenient opportunity to promote physical activity for children, while also reducing car dependence and its associated environmental impacts. School–home distance is a critical factor in school commuting mode choice, and longer distances have been proven to increase the likelihood of driving. In this study, we combine open-access data acquired from Baidu Map application programming interface (API) with GIS (Geographic Information System) technology to estimate the extent to which the present school–home distances can be reduced for public middle schools in Jianye District, Nanjing, China. Based on the policies for school planning and catchment allocation, we conduct a scenario analysis of school catchment reassignment whereby residences are reassigned to the nearest school. The results show that, despite the government’s ‘attending nearby school’ policy, some students in the study area are subjected to excess school–home distances, and the overall journey-to-school trips can be reduced by 20.07%, accounting for 330.8 km. This excess distance indicates the extent to which the need for vehicle travel can be potentially reduced in favor of active school commuting and a low-carbon lifestyle. Therefore, these findings provide important insights into school siting and school catchment assignment policies seeking to facilitate active school commuting, achieve educational spatial equity and reduce car dependence. Full article
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24 pages, 6998 KiB  
Article
Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks
by Qing Zhu, Meite Chen, Bin Feng, Yan Zhou, Maosu Li, Zhaowen Xu, Yulin Ding, Mingwei Liu, Wei Wang and Xiao Xie
ISPRS Int. J. Geo-Inf. 2020, 9(9), 518; https://doi.org/10.3390/ijgi9090518 - 28 Aug 2020
Cited by 4 | Viewed by 2258
Abstract
Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This [...] Read more.
Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment. Full article
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26 pages, 2639 KiB  
Article
Knowing My Village from the Sky: A Collaborative Spatial Learning Framework to Integrate Spatial Knowledge of Stakeholders in Achieving Sustainable Development Goals
by Aulia Akbar, Johannes Flacke, Javier Martinez, Rosa Aguilar and Martin F. A. M. van Maarseveen
ISPRS Int. J. Geo-Inf. 2020, 9(9), 515; https://doi.org/10.3390/ijgi9090515 - 26 Aug 2020
Cited by 12 | Viewed by 3433
Abstract
Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global [...] Read more.
Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global south still experience a geospatial data scarcity at the rural level due to complex geographical terrains, weak coordination among institutions and a lack of knowledge and technologies to produce visualised geospatial data like maps. We proposed a collaborative spatial learning framework that integrates the spatial knowledge of stakeholders to obtain geospatial data. By conducting participatory mapping workshops in three villages in the Deli Serdang district in Indonesia, we tested the framework in terms of facilitating communication and collaboration of the village stakeholders while also supporting knowledge co-production and social learning among them. Satellite images were used in digital and non-digital mapping workshops to support village stakeholders to produce proper village maps while fulfilling the SDGs’ emphasis to make geospatial data available through a participatory approach. Full article
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25 pages, 7982 KiB  
Article
A New Algorithm for Calculating the Flow Path Curvature (C) from the Square-Grid Digital Elevation Model (DEM)
by Qianjiao Wu, Yumin Chen, Hongyan Zhou, Shujie Chen and Han Wang
ISPRS Int. J. Geo-Inf. 2020, 9(9), 510; https://doi.org/10.3390/ijgi9090510 - 24 Aug 2020
Cited by 3 | Viewed by 2356
Abstract
This paper proposes a flow-path-network-based (FPN-based) algorithm, constructed from a square-grid digital elevation model (DEM) to improve the simulation of the flow path curvature (C). First, the flow-path network model was utilized to obtain an FPN. Then, a flow-path-network-flow-path-curvature (FPN-C) algorithm [...] Read more.
This paper proposes a flow-path-network-based (FPN-based) algorithm, constructed from a square-grid digital elevation model (DEM) to improve the simulation of the flow path curvature (C). First, the flow-path network model was utilized to obtain an FPN. Then, a flow-path-network-flow-path-curvature (FPN-C) algorithm was proposed to estimate C from the FPN. The experiments consisted of two sections: (1) quantitatively evaluating the accuracy using 5 m DEMs generated from the mathematical ellipsoid and Gauss models, and (2) qualitatively assessing the accuracy using a 30 m DEM of a real-world complex region. The three algorithms proposed by Evans (1980), Zevenbergen and Throne (1987), and Shary (1995) were used to validate the accuracy of the new algorithm. The results demonstrate that the C value of the proposed algorithm was generally closer to the theoretical C value derived from two mathematical surfaces. The root mean standard error (RMSE) and mean absolute error (MAE) of the new method are 0.0014 and 0.0002 m, reduced by 42% and 82% of that of the third algorithm on the ellipsoid surface, respectively. The RMSE and MAE of the presented method are 0.0043 and 0.0025 m at best, reduced by up to 35% and 14% of that of the former two algorithms on the Gauss surface, respectively. The proposed algorithm generally produces better spatial distributions of C on different terrain surfaces. Full article
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18 pages, 3908 KiB  
Article
OSMWatchman: Learning How to Detect Vandalized Contributions in OSM Using a Random Forest Classifier
by Quy Thy Truong, Guillaume Touya and Cyril de Runz
ISPRS Int. J. Geo-Inf. 2020, 9(9), 504; https://doi.org/10.3390/ijgi9090504 - 22 Aug 2020
Cited by 8 | Viewed by 2992
Abstract
Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in [...] Read more.
Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”. Full article
(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)
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33 pages, 25960 KiB  
Article
Tools for BIM-GIS Integration (IFC Georeferencing and Conversions): Results from the GeoBIM Benchmark 2019
by Francesca Noardo, Lars Harrie, Ken Arroyo Ohori, Filip Biljecki, Claire Ellul, Thomas Krijnen, Helen Eriksson, Dogus Guler, Dean Hintz, Mojgan A. Jadidi, Maria Pla, Santi Sanchez, Ville-Pekka Soini, Rudi Stouffs, Jernej Tekavec and Jantien Stoter
ISPRS Int. J. Geo-Inf. 2020, 9(9), 502; https://doi.org/10.3390/ijgi9090502 - 21 Aug 2020
Cited by 51 | Viewed by 8099
Abstract
The integration of 3D city models with Building Information Models (BIM), coined as GeoBIM, facilitates improved data support to several applications, e.g., 3D map updates, building permits issuing, detailed city analysis, infrastructure design, context-based building design, to name a few. To solve the [...] Read more.
The integration of 3D city models with Building Information Models (BIM), coined as GeoBIM, facilitates improved data support to several applications, e.g., 3D map updates, building permits issuing, detailed city analysis, infrastructure design, context-based building design, to name a few. To solve the integration, several issues need to be tackled and solved, i.e., harmonization of features, interoperability, format conversions, integration of procedures. The GeoBIM benchmark 2019, funded by ISPRS and EuroSDR, evaluated the state of implementation of tools addressing some of those issues. In particular, in the part of the benchmark described in this paper, the application of georeferencing to Industry Foundation Classes (IFC) models and making consistent conversions between 3D city models and BIM are investigated, considering the OGC CityGML and buildingSMART IFC as reference standards. In the benchmark, sample datasets in the two reference standards were provided. External volunteers were asked to describe and test georeferencing procedures for IFC models and conversion tools between CityGML and IFC. From the analysis of the delivered answers and processed datasets, it was possible to notice that while there are tools and procedures available to support georeferencing and data conversion, comprehensive definition of the requirements, clear rules to perform such two tasks, as well as solid technological solutions implementing them, are still lacking in functionalities. Those specific issues can be a sensible starting point for planning the next GeoBIM integration agendas. Full article
(This article belongs to the Special Issue Integration of BIM and GIS for Built Environment Applications)
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20 pages, 2471 KiB  
Article
Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover
by Ba-Huy Tran, Nathalie Aussenac-Gilles, Catherine Comparot and Cassia Trojahn
ISPRS Int. J. Geo-Inf. 2020, 9(9), 503; https://doi.org/10.3390/ijgi9090503 - 21 Aug 2020
Cited by 14 | Viewed by 3111
Abstract
Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way [...] Read more.
Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way of qualifying geographic units through their spatio-temporal features. We propose (i) a modular ontology that contributes to the semantic and homogeneous description of spatio-temporal data to qualify predefined areas; (ii) a Semantic Extraction, Transformation, and Load (ETL) process, allowing us to extract data from rasters and to link them to the corresponding spatio-temporal units and features; and (iii) a resulting dataset that is published as an RDF triplestore, exposed through a SPARQL endpoint, and exploited by a semantic interface. We illustrate the integration process with raster files providing the land cover of a specific French winery geographic area, its administrative units, and their land registers over different periods. The results have been evaluated with regards to three use-cases exploiting these EO data: integration of time series observations; EO process guidance; and data cross-comparison. Full article
(This article belongs to the Special Issue Geographic Information Extraction and Retrieval)
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18 pages, 4687 KiB  
Article
Development of a Novel Framework to Propose New Strategies for Automated External Defibrillators Deployment Targeting Residential Out-Of-Hospital Cardiac Arrests: Application to the City of Milan
by Gianquintieri Lorenzo, Brovelli Maria Antonia, Brambilla Piero Maria, Pagliosa Andrea, Villa Guido Francesco and Caiani Enrico Gianluca
ISPRS Int. J. Geo-Inf. 2020, 9(8), 491; https://doi.org/10.3390/ijgi9080491 - 17 Aug 2020
Cited by 3 | Viewed by 2941
Abstract
Public Access Defibrillation (PAD) is the leading strategy in reducing time to first defibrillation in cases of Out-Of-Hospital Cardiac Arrest (OHCA), but PAD programs are underperforming considering their potentiality. Our aim was to develop an analysis and optimization framework, exploiting georeferenced information processed [...] Read more.
Public Access Defibrillation (PAD) is the leading strategy in reducing time to first defibrillation in cases of Out-Of-Hospital Cardiac Arrest (OHCA), but PAD programs are underperforming considering their potentiality. Our aim was to develop an analysis and optimization framework, exploiting georeferenced information processed with Geographic Information Systems (GISs), specifically targeting residential OHCAs. The framework, based on an historical database of OHCAs, location of Automated External Defibrillators (AEDs), topographic and demographic information, proposes new strategies for AED deployment focusing on residential OHCAs, where performance assessment was evaluated using AEDs “catchment area” (area that can be reached within 6 min walk along streets). The proposed framework was applied to the city of Milan, Lombardy (Italy), considering the OHCA database of four years (2015–2018), including 8152 OHCA, of which 7179 (88.06%) occurred in residential locations. The proposed strategy for AEDs deployment resulted more effective compared to the existing distribution, with a significant improvement (from 41.77% to 73.33%) in OHCAs’ spatial coverage. Further improvements were simulated with different cost scenarios, resulting in more cost-efficient solutions. Results suggest that PAD programs, either in brand-new territories or in further improvements, could significantly benefit from a comprehensive planning, based on mathematical models for risk mapping and on geographical tools. Full article
(This article belongs to the Special Issue GIS in Healthcare)
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16 pages, 6387 KiB  
Article
Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets
by Jing Yu, Shu Peng, Weiwei Zhang and Shun Kang
ISPRS Int. J. Geo-Inf. 2020, 9(8), 483; https://doi.org/10.3390/ijgi9080483 - 11 Aug 2020
Cited by 4 | Viewed by 4982
Abstract
Recognizing land cover heterogeneity is essential for the assessment of spatial patterns to guide conservation planning. One of the top research priorities is the quantification of land cover heterogeneity using effective landscape metrics. However, due to the diversity of land cover types and [...] Read more.
Recognizing land cover heterogeneity is essential for the assessment of spatial patterns to guide conservation planning. One of the top research priorities is the quantification of land cover heterogeneity using effective landscape metrics. However, due to the diversity of land cover types and their varied distribution, a consistent, larger-scale, and standardized framework for heterogeneity information extraction from this complex perspective is still lacking. Consequently, we developed a new Land Cover Complexity Index (LCCI), which is based on information-theory. The LCCI contains two foundational aspects of heterogeneity, composition and configuration, thereby capturing more comprehensive information on land cover patterns than any single metric approach. In this study, we compare the performance of the LCCI with that of other landscape metrics at two different scales, and the results show that our newly developed indicator more accurately characterizes and distinguishes different land cover patterns. LCCI provides an alternative way to measure the spatial variation of land cover distribution. Classification maps of land cover heterogeneity generated using the LCCI provide valuable insights and implications for regional conservation planning. Thus, the LCCI is shown to be a consistent indicator for the quantification of land cover heterogeneity that functions in an adaptive way by simultaneously considering both composition and configuration. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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26 pages, 6256 KiB  
Article
A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics
by Linfang Ding, Guohui Xiao, Diego Calvanese and Liqiu Meng
ISPRS Int. J. Geo-Inf. 2020, 9(8), 474; https://doi.org/10.3390/ijgi9080474 - 28 Jul 2020
Cited by 13 | Viewed by 4988
Abstract
In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making [...] Read more.
In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making sense out of the combined data via sophisticated analysis methods. To address this challenge we rely on two well-established research areas: data integration and geovisual analytics, and propose to adopt an ontology-based approach to decouple the challenges of data access and analytics. Our framework consists of two modules centered around an ontology: (1) an ontology-based data integration (OBDI) module, in which mappings specify the relationship between the underlying data and a domain ontology; (2) a geovisual analytics (GeoVA) module, designed for the exploration of the integrated data, by explicitly making use of standard ontologies. In this framework, ontologies play a central role by providing a coherent view over the heterogeneous data, and by acting as a mediator for visual analysis tasks. We test our framework in a scenario for the investigation of the spatiotemporal patterns of meteorological and traffic data from several open data sources. Initial studies show that our approach is feasible for the exploration and understanding of heterogeneous geospatial data. Full article
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22 pages, 2838 KiB  
Article
Measuring Community Disaster Resilience in the Conterminous Coastal United States
by Shaikh Abdullah Al Rifat and Weibo Liu
ISPRS Int. J. Geo-Inf. 2020, 9(8), 469; https://doi.org/10.3390/ijgi9080469 - 23 Jul 2020
Cited by 41 | Viewed by 6578
Abstract
In recent years, building resilient communities to disasters has become one of the core objectives in the field of disaster management globally. Despite being frequently targeted and severely impacted by disasters, the geographical extent in studying disaster resilience of the coastal communities of [...] Read more.
In recent years, building resilient communities to disasters has become one of the core objectives in the field of disaster management globally. Despite being frequently targeted and severely impacted by disasters, the geographical extent in studying disaster resilience of the coastal communities of the United States (US) has been limited. In this study, we developed a composite community disaster resilience index (CCDRI) for the coastal communities of the conterminous US that considers different dimensions of disaster resilience. The resilience variables used to construct the CCDRI were justified by examining their influence on disaster losses using ordinary least squares (OLS) and geographically weighted regression (GWR) models. Results suggest that the CCDRI score ranges from −12.73 (least resilient) to 8.69 (most resilient), and northeastern communities are comparatively more resilient than southeastern communities in the study area. Additionally, resilience components used in this study have statistically significant impact on minimizing disaster losses. The GWR model performs much better in explaining the variances while regressing the disaster property damage against the resilience components (explains 72% variance) than the OLS (explains 32% variance) suggesting that spatial variations of resilience components should be accounted for an effective disaster management program. Moreover, findings from this study could provide local emergency managers and decision-makers with unique insights for enhancing overall community resilience to disasters and minimizing disaster impacts in the study area. Full article
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19 pages, 8220 KiB  
Article
An Open-Source Framework of Generating Network-Based Transit Catchment Areas by Walking
by Diao Lin, Ruoxin Zhu, Jian Yang and Liqiu Meng
ISPRS Int. J. Geo-Inf. 2020, 9(8), 467; https://doi.org/10.3390/ijgi9080467 - 22 Jul 2020
Cited by 4 | Viewed by 2571
Abstract
The transit catchment area is an important concept for public transport planning. This study proposes a methodological framework to generate network-based transit catchment areas by walking. Three components of the framework, namely subgraph construction, extended shortest path tree construction and contour generation are [...] Read more.
The transit catchment area is an important concept for public transport planning. This study proposes a methodological framework to generate network-based transit catchment areas by walking. Three components of the framework, namely subgraph construction, extended shortest path tree construction and contour generation are presented step by step. Methods on how to generalize the framework to the cases of the directed road network and non-point facilities are developed. The implementation of the framework is provided as an open-source project. Using metro stations in Shanghai as a case study, we illustrate the feasibility of the proposed framework. Experiments show that the proposed method generates catchment areas of high geospatial accuracy and significantly increases computational efficiency. The open-source program can be applied to support research related to transit catchment areas and has the potential to be extended to include more routing-related factors. Full article
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22 pages, 11839 KiB  
Article
Post-Earthquake Recovery Phase Monitoring and Mapping Based on UAS Data
by Nikolaos Soulakellis, Christos Vasilakos, Stamatis Chatzistamatis, Dimitris Kavroudakis, Georgios Tataris, Ermioni-Eirini Papadopoulou, Apostolos Papakonstantinou, Olga Roussou and Themistoklis Kontos
ISPRS Int. J. Geo-Inf. 2020, 9(7), 447; https://doi.org/10.3390/ijgi9070447 - 17 Jul 2020
Cited by 8 | Viewed by 3009
Abstract
Geoinformatics plays an essential role during the recovery phase of a post-earthquake situation. The aim of this paper is to present the methodology followed and the results obtained by the utilization of Unmanned Aircraft Systems (UASs) 4K-video footage processing and the automation of [...] Read more.
Geoinformatics plays an essential role during the recovery phase of a post-earthquake situation. The aim of this paper is to present the methodology followed and the results obtained by the utilization of Unmanned Aircraft Systems (UASs) 4K-video footage processing and the automation of geo-information methods targeted at both monitoring the demolition process and mapping the demolished buildings. The field campaigns took place on the traditional settlement of Vrisa (Lesvos, Greece), which was heavily damaged by a strong earthquake (Mw=6.3) on June 12th, 2017. For this purpose, a flight campaign took place on 3rd February 2019 for collecting aerial 4K video footage using an Unmanned Aircraft. The Structure from Motion (SfM) method was applied on frames which derived from the 4K video footage, for producing accurate and very detailed 3D point clouds, as well as the Digital Surface Model (DSM) of the building stock of the Vrisa traditional settlement, twenty months after the earthquake. This dataset has been compared with the corresponding one which derived from 25th July 2017, a few days after the earthquake. Two algorithms have been developed for detecting the demolished buildings of the affected area, based on the DSMs and 3D point clouds, correspondingly. The results obtained have been tested through field studies and demonstrate that this methodology is feasible and effective in building demolition detection, giving very accurate results (97%) and, in parallel, is easily applicable and suit well for rapid demolition mapping during the recovery phase of a post-earthquake scenario. The significant advantage of the proposed methodology is its ability to provide reliable results in a very low cost and time-efficient way and to serve all stakeholders and national and local organizations that are responsible for post-earthquake management. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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17 pages, 5758 KiB  
Article
An Accurate Matching Method for Projecting Vector Data into Surveillance Video to Monitor and Protect Cultivated Land
by Zhenfeng Shao, Congmin Li, Deren Li, Orhan Altan, Lei Zhang and Lin Ding
ISPRS Int. J. Geo-Inf. 2020, 9(7), 448; https://doi.org/10.3390/ijgi9070448 - 17 Jul 2020
Cited by 48 | Viewed by 3198
Abstract
The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or [...] Read more.
The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or systems have their own shortcomings when implemented in monitoring cultivated land. To address this problem, this paper aims to propose an accurate matching method for projecting vector data into surveillance video, considering the topographic characteristics of cultivated land in plain area. Once an adequate number of control points are identified from 2D (two-dimensional) GIS data and the selected reference video image, the alignment of 2D GIS data and PTZ (pan-tilt-zoom) video frames can be realized by automatic feature matching method. Based on the alignment results, we can easily identify the occurrence of farmland destruction by visually inspecting the image content covering the 2D vector area. Furthermore, a prototype of intelligent surveillance video system for cultivated land is constructed and several experiments are conducted to validate the proposed approach. Experimental results show that the proposed alignment methods can achieve a high accuracy and satisfy the requirements of cultivated land monitoring. Full article
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21 pages, 5753 KiB  
Article
High Resolution Viewscape Modeling Evaluated Through Immersive Virtual Environments
by Payam Tabrizian, Anna Petrasova, Perver K. Baran, Jelena Vukomanovic, Helena Mitasova and Ross K. Meentemeyer
ISPRS Int. J. Geo-Inf. 2020, 9(7), 445; https://doi.org/10.3390/ijgi9070445 - 17 Jul 2020
Cited by 6 | Viewed by 3331
Abstract
Visual characteristics of urban environments influence human perception and behavior, including choices for living, recreation and modes of transportation. Although geospatial visualizations hold great potential to better inform urban planning and design, computational methods are lacking to realistically measure and model urban and [...] Read more.
Visual characteristics of urban environments influence human perception and behavior, including choices for living, recreation and modes of transportation. Although geospatial visualizations hold great potential to better inform urban planning and design, computational methods are lacking to realistically measure and model urban and parkland viewscapes at sufficiently fine-scale resolution. In this study, we develop and evaluate an integrative approach to measuring and modeling fine-scale viewscape characteristics of a mixed-use urban environment, a city park. Our viewscape approach improves the integration of geospatial and perception elicitation techniques by combining high-resolution lidar-based digital surface models, visual obstruction, and photorealistic immersive virtual environments (IVEs). We assessed the realism of our viewscape models by comparing metrics of viewscape composition and configuration to human subject evaluations of IVEs across multiple landscape settings. We found strongly significant correlations between viewscape metrics and participants’ perceptions of viewscape openness and naturalness, and moderately strong correlations with landscape complexity. These results suggest that lidar-enhanced viewscape models can adequately represent visual characteristics of fine-scale urban environments. Findings also indicate the existence of relationships between human perception and landscape pattern. Our approach allows urban planners and designers to model and virtually evaluate high-resolution viewscapes of urban parks and natural landscapes with fine-scale details never before demonstrated. Full article
(This article belongs to the Special Issue GIS-Based Analysis for Quality of Life and Environmental Monitoring)
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18 pages, 12046 KiB  
Article
A Machine Learning Approach to Delineating Neighborhoods from Geocoded Appraisal Data
by Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee and Erik Linstead
ISPRS Int. J. Geo-Inf. 2020, 9(7), 451; https://doi.org/10.3390/ijgi9070451 - 17 Jul 2020
Cited by 1 | Viewed by 3320
Abstract
Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries [...] Read more.
Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information that can be extracted from property appraisals. This paper demonstrates the potential of using only the distance between subjects and their comparable properties, identified in an appraisal, to delineate neighborhoods that are composed of properties with similar prices and features. Using spatial filters, we first identify regions with the most appraisal activity, and through the application of a spatial clustering algorithm, generate neighborhoods composed of properties sharing similar characteristics. Through an application of bootstrapped linear regression, we find that delineating neighborhoods using geolocation of subjects and comparable properties explains more variation in a property’s features, such as valuation, square footage, and price per square foot, than ZIP codes or Census tracts. We also discuss the ability of the neighborhoods to grow and shrink over the years, due to shifts in each housing submarket. Full article
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36 pages, 7836 KiB  
Article
Measuring Accessibility to Various ASFs from Public Transit using Spatial Distance Measures in Indian Cities
by Pavan Teja Yenisetty and Pankaj Bahadure
ISPRS Int. J. Geo-Inf. 2020, 9(7), 446; https://doi.org/10.3390/ijgi9070446 - 17 Jul 2020
Cited by 9 | Viewed by 3826
Abstract
Nowadays, accessibility to facilities is one of the most discussed issues in sustainable urban planning. In the current research, two spatial distance accessibility measures were applied to evaluate the accessibility to amenities, services, and facilities (ASFs) from public transit (PT) by walking distance [...] Read more.
Nowadays, accessibility to facilities is one of the most discussed issues in sustainable urban planning. In the current research, two spatial distance accessibility measures were applied to evaluate the accessibility to amenities, services, and facilities (ASFs) from public transit (PT) by walking distance in six Indian cities. The first stage accounts for distance measures using the Euclidean distance with a new methodical approach derived from the built-up area with a spatial resolution of 30 m from Landsat data, and for the network distance method, the actual road distances using OpenStreetMap (OSM) for different threshold ranges of distances were derived. Meanwhile, in the second stage, indicators such as built-up area, network connectivity, and network density with the percentage of ASFs are evaluated and combined for normalization process for ranking the city. The present study assesses the accessibility to various ASFs from PT at city level and explores whether the actual road network access (by measuring distance) in Indian cities is contributing to a high level of accessibility. It adopts a unique approach using statistical tools while assessing both Euclidean and network distances. It models a framework for overall benchmarking in all six cities by ranking them for their accessibility. The results show various scenarios in terms of the rank of cities, which had been strongly affected by distance metrics (Euclidean vs. network) and thus emphasize the careful use of these measures as supporting tools for planning. This facilitates the identification of the local barriers and problems with network access that affect the actual distance. This unique approach can help policymakers to identify the gaps in PT coverage for reaching ASFs. Furthermore, it helps in crucial implementation by strategic planning that can be achieved using these distance criteria. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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19 pages, 3164 KiB  
Article
Capacitated Refuge Assignment for Speedy and Reliable Evacuation
by Takanori Hara, Masahiro Sasabe, Taiki Matsuda and Shoji Kasahara
ISPRS Int. J. Geo-Inf. 2020, 9(7), 442; https://doi.org/10.3390/ijgi9070442 - 16 Jul 2020
Cited by 5 | Viewed by 2297
Abstract
When a large-scale disaster occurs, each evacuee should move to an appropriate refuge in a speedy and safe manner. Most of the existing studies on the refuge assignment consider the speediness of evacuation and refuge capacity while the safety of evacuation is not [...] Read more.
When a large-scale disaster occurs, each evacuee should move to an appropriate refuge in a speedy and safe manner. Most of the existing studies on the refuge assignment consider the speediness of evacuation and refuge capacity while the safety of evacuation is not taken into account. In this paper, we propose a refuge assignment scheme that considers both the speediness and safety of evacuation under the refuge capacity constraint. We first formulate the refuge assignment problem as a two-step integer linear program (ILP). Since the two-step ILP requires route candidates between evacuees and their possible refuges, we further propose a speedy and reliable route selection scheme as an extension of the existing route selection scheme. Through numerical results using the actual data of Arako district of Nagoya city in Japan, we show that the proposed scheme can improve the average route reliability among evacuees by 13.6% while suppressing the increase of the average route length among evacuees by 7.3%, compared with the distance-based route selection and refuge assignment. In addition, we also reveal that the current refuge capacity is not enough to support speedy and reliable evacuation for the residents. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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22 pages, 4697 KiB  
Article
Analyzing Links between Spatio-Temporal Metrics of Built-Up Areas and Socio-Economic Indicators on a Semi-Global Scale
by Marta Sapena, Luis A. Ruiz and Hannes Taubenböck
ISPRS Int. J. Geo-Inf. 2020, 9(7), 436; https://doi.org/10.3390/ijgi9070436 - 11 Jul 2020
Cited by 8 | Viewed by 3801
Abstract
Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will [...] Read more.
Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will help to elucidate their linkages and, thus, aid in the development of new strategies for more sustainable development. In this study, we identified empirical and significant relationships between income, inequality, GDP, air pollution and employment indicators and their change over time with the spatial organization of the built and natural elements in functional urban areas. We were able to demonstrate this in 32 countries using spatio-temporal metrics, using geoinformation from databases available worldwide. We employed random forest regression, and we were able to explain 32% to 68% of the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. We also identified the spatio-temporal metrics that were more relevant in the models: we found that urban compactness, concentration degree, the dispersion index, the densification of built-up growth, accessibility and land-use/land-cover density and change could be used as proxies for some socio-economic indicators. This study is a first and fundamental step for the identification of such relationships at a global scale. The proposed methodology is highly versatile, the inclusion of new datasets is straightforward, and the increasing availability of multi-temporal geospatial and socio-economic databases is expected to empirically boost the study of these relationships from a multi-temporal perspective in the near future. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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25 pages, 50699 KiB  
Article
Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web
by Candan Eylül Kilsedar and Maria Antonia Brovelli
ISPRS Int. J. Geo-Inf. 2020, 9(7), 434; https://doi.org/10.3390/ijgi9070434 - 11 Jul 2020
Cited by 12 | Viewed by 10625
Abstract
The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of [...] Read more.
The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of this research are provided through a geoportal comprised of multiple applications that are related to 3D visualization of cities, ground deformation, land use and land cover and mobility. In a subset of the applications, the datasets handled are considered to be large in volume. The geospatial data were visualized on dynamic and interactive virtual globes to enable visual exploration. The geoportal is available on the web to enable cross-platform access to it. Furthermore, the geoportal was developed employing open standards, free and open source software (FOSS) and open data, most importantly to ensure interoperability and reduce the barriers to access it. The geoportal brings together various datasets, different both in terms of context and format employing numerous technologies. As a result, the existing web technologies for geovisualization and geospatial data processing were examined and exemplary and innovative software was developed to extend the state of the art. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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26 pages, 10737 KiB  
Article
Research on the Colors of Military Symbols in Digital Situation Maps Based on Event-Related Potential Technology
by Yafeng Niu, Guorui Ma, Wei Xue, Chengqi Xue, Tianyu Zhou, Yue Gao, Hongrui Zuo and Tao Jin
ISPRS Int. J. Geo-Inf. 2020, 9(7), 420; https://doi.org/10.3390/ijgi9070420 - 30 Jun 2020
Cited by 4 | Viewed by 4702
Abstract
Under the trend of increasingly informationalized military operations and the increasing maneuverability of combat units, military commanders have put forward higher requirements for the accuracy and promptness of information on battlefield situation maps. Based on the sea battlefield, this paper studies the pros [...] Read more.
Under the trend of increasingly informationalized military operations and the increasing maneuverability of combat units, military commanders have put forward higher requirements for the accuracy and promptness of information on battlefield situation maps. Based on the sea battlefield, this paper studies the pros and cons of the color matching of military symbols on sea situation maps. Fifteen colors, where each Hue had five colors, were chosen using the Munsell Color System according to Chroma axis and the Value axis on a span of 2 and 4. By collecting and analyzing the P300 EEG data, reaction time data, and accuracy data of 20 subjects, a better color matching selection of military symbols on pure color (L = 85, a = −10, and b = −23) sea situation maps is put forward, and the conclusions are as follows: (1) the different colors all cause the P300 component in EEG experiment. Among them, the P300 amplitude that is caused by military symbols with lower Chroma is smaller and the latency is shorter, indicating that the user experience and efficiency of low Chroma color symbols will be better than those with high Chroma color symbols. (2) High Value color map military symbols cause higher P300 amplitude and longer latency. According to the results above, this paper puts forward three optimized colors, namely, blue (L = 39, a = 20, and b = −49), green (L = 80, a = −72, and b = 72), and red (L = 20, a = 41, and b = 28). Additionally, three map interfaces were designed to confirm the validity of these colors. By means of applying the NASA-TLX (Task Load Index) scale to evaluate the task load of the confirmation interfaces, it can be concluded that these three optimized colors are preferred by users who are skilled in GIS and interface design. Therefore, the research conclusion of this paper can provide important reference values for military map design, which is helpful in shortening the identification and judgment time during the use of situation maps and it can improve users’ operation performance. Full article
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23 pages, 4154 KiB  
Article
Numbers on Thematic Maps: Helpful Simplicity or Too Raw to Be Useful for Map Reading?
by Jolanta Korycka-Skorupa and Izabela Małgorzata Gołębiowska
ISPRS Int. J. Geo-Inf. 2020, 9(7), 415; https://doi.org/10.3390/ijgi9070415 - 28 Jun 2020
Cited by 9 | Viewed by 3497
Abstract
As the development of small-scale thematic cartography continues, there is a growing interest in simple graphic solutions, e.g., in the form of numerical values presented on maps to replace or complement well-established quantitative cartographic methods of presentation. Numbers on maps are used as [...] Read more.
As the development of small-scale thematic cartography continues, there is a growing interest in simple graphic solutions, e.g., in the form of numerical values presented on maps to replace or complement well-established quantitative cartographic methods of presentation. Numbers on maps are used as an independent form of data presentation or function as a supplement to the cartographic presentation, becoming a legend placed directly on the map. Despite the frequent use of numbers on maps, this relatively simple form of presentation has not been extensively empirically evaluated. This article presents the results of an empirical study aimed at comparing the usability of numbers on maps for the presentation of quantitative information to frequently used proportional symbols, for simple map-reading tasks. The study showed that the use of numbers on single-variable and two-variable maps results in a greater number of correct answers and also often an improved response time compared to the use of proportional symbols. Interestingly, the introduction of different sizes of numbers did not significantly affect their usability. Thus, it has been proven that—for some tasks—map users accept this bare-bones version of data presentation, often demonstrating a higher level of preference for it than for proportional symbols. Full article
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20 pages, 4400 KiB  
Article
A Sightseeing Spot Recommendation System That Takes into Account the Visiting Frequency of Users
by Yudai Kato and Kayoko Yamamoto
ISPRS Int. J. Geo-Inf. 2020, 9(7), 411; https://doi.org/10.3390/ijgi9070411 - 27 Jun 2020
Cited by 14 | Viewed by 2847
Abstract
The present study aimed to design, develop, operate and evaluate a sightseeing spot recommendation system that can efficiently and usefully support tourists while considering their visiting frequencies. This system was developed by integrating social networking services (SNSs), Web-geographic information systems (GIS) and recommendation [...] Read more.
The present study aimed to design, develop, operate and evaluate a sightseeing spot recommendation system that can efficiently and usefully support tourists while considering their visiting frequencies. This system was developed by integrating social networking services (SNSs), Web-geographic information systems (GIS) and recommendation systems. The system recommends sightseeing spots to users with different visiting frequencies, adopting two recommendation methods (knowledge-based recommendation and collaborative recommendation methods). Additionally, the system was operated for six weeks in Kamakura City, Kanagawa Prefecture, Japan, and the total number of users was 61. Based on the results of the web questionnaire survey, the usefulness of the system when sightseeing was high, and the recommendation function of sightseeing spots, which is an original function, received mainly good ratings. From the results of the access analysis of users’ log data, the total number of sessions in this system was 329, 77% used mobile devices, and smartphones were used most frequently. Therefore, it is evident that the system was used by different types of devices just as it was designed for, and that the system was used according to the purpose of the present study, which is to support the sightseeing activities of users. Full article
(This article belongs to the Special Issue Multimedia Cartography)
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18 pages, 6859 KiB  
Article
A Simplified Method of Cartographic Visualisation of Buildings’ Interiors (2D+) for Navigation Applications
by Dariusz Gotlib, Michał Wyszomirski and Miłosz Gnat
ISPRS Int. J. Geo-Inf. 2020, 9(6), 407; https://doi.org/10.3390/ijgi9060407 - 26 Jun 2020
Cited by 7 | Viewed by 2995
Abstract
This article proposes an original method of a coherent and simplified cartographic presentation of the interior of buildings called 2D+, which can be used in geoinformation applications that do not support an extensive three-dimensional visualisation or do not have access to a 3D [...] Read more.
This article proposes an original method of a coherent and simplified cartographic presentation of the interior of buildings called 2D+, which can be used in geoinformation applications that do not support an extensive three-dimensional visualisation or do not have access to a 3D model of the building. A simplified way of cartographic visualisation can be used primarily in indoor navigation systems and other location-based services (LBS) applications. It can also be useful in systems supporting facility management (FM) and various kinds of geographic information systems (GIS). On the one hand, it may increase an application’s efficiency; on the other, it may unify the method of visualisation in the absence of a building’s 3D model. Thanks to the proposed method, it is possible to achieve the same effect regardless of the data source used: Building Information Modelling (BIM), a Computer-aided Design (CAD) model, or traditional architectural and construction drawings. Such a solution may be part of a broader concept of a multi-scale presentation of buildings’ interiors. The article discusses the issues of visualising data and converting data to the appropriate coordinate system, as well as the properties of the application model of data. Full article
(This article belongs to the Special Issue Geovisualization and Map Design)
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