ISPRS International Journal of Geo-Information

20 pages, 10658 KiB

Open AccessArticle

Energy-Efficient 3D Path Planning for Complex Field Scenes Using the Digital Model with Landcover and Terrain

by Baodong Ma, Quan Liu, Ziwei Jiang, Defu Che, Kehan Qiu and Xiangxiang Shang

ISPRS Int. J. Geo-Inf. 2023, 12(2), 82; https://doi.org/10.3390/ijgi12020082 - 20 Feb 2023

Cited by 6 | Viewed by 2301

Path planning is widely used in many domains, and it is crucial for the advancement of map navigation, autonomous driving, and robot path planning. However, existing path planning methods have certain limitations for complex field scenes with undulating terrain and diverse landcover types. [...] Read more.

Path planning is widely used in many domains, and it is crucial for the advancement of map navigation, autonomous driving, and robot path planning. However, existing path planning methods have certain limitations for complex field scenes with undulating terrain and diverse landcover types. This paper presents an energy-efficient 3D path planning algorithm based on an improved A* algorithm and the particle swarm algorithm in complex field scenes. The evaluation function of the A* algorithm was improved to be suitable for complex field scenes. The slope parameter and friction coefficient were respectively used in the evaluation function to represent different terrain features and landcover types. The selection of expanding nodes in the algorithm depends not only on the minimum distance but also on the minimum consumption cost. Furthermore, the turning radius factor and slope threshold factor of vehicles were added to the definition of impassable points in the improved A* algorithm, so that the accessibility of path planning could be guaranteed by excluding some bends and steep slopes. To meet the requirements for multi-target path planning, the improved A* algorithm was used as the fitness function of the particle swarm algorithm to solve the traveling salesman problem. The experimental results showed that the proposed algorithm is capable of multi-target path planning in complex field scenes. Furthermore, the path planned by this algorithm is more passable and more energy efficient. In this experimental environment model, the average energy-saving efficiency of the path planned by the improved algorithm is 14.7% compared to the traditional A* algorithm. This would be beneficial to the development of ecotourism and geological exploration. Full article

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24 pages, 9616 KiB

Open AccessArticle

Mapping Cropland Extent in Pakistan Using Machine Learning Algorithms on Google Earth Engine Cloud Computing Framework

by Rana Muhammad Amir Latif, Jinliao He and Muhammad Umer

ISPRS Int. J. Geo-Inf. 2023, 12(2), 81; https://doi.org/10.3390/ijgi12020081 - 20 Feb 2023

Cited by 3 | Viewed by 3811

Abstract

An actual cropland extent product with a high spatial resolution with a precision of up to 60 m is believed to be particularly significant in tackling numerous water security concerns and world food challenges. To advance the development of niche, advanced cropland goods [...] Read more.

An actual cropland extent product with a high spatial resolution with a precision of up to 60 m is believed to be particularly significant in tackling numerous water security concerns and world food challenges. To advance the development of niche, advanced cropland goods such as crop variety techniques, crop intensities, crop water production, and crop irrigation, it is necessary to examine how cropland products typically span narrow or expansive farmlands. Some of the existing challenges are processing by constructing precision-high resolution cropland-wide items of training and testing data on diverse geographical locations and safe frontiers, computing capacity, and managing vast volumes of geographical data. This analysis includes eight separate Sentinel-2 multi-spectral instruments data from 2018 to 2019 (Short-wave Infrared Imagery (SWIR 2), SWIR 1, Cirrus, the near infrared, red, green, blue, and aerosols) have been used. Pixel-based classification algorithms have been employed, and their precision is measured and scrutinized in this study. The computations and analyses have been conducted on the cloud-based Google Earth Engine computing network. Training and testing data were obtained from the Google Earth Engine map console at a high spatial 10 m resolution for this analysis. The basis of research information for testing the computer algorithms consists of 855 training samples, culminating in a manufacturing field of 200 individual validation samples measuring product accuracy. The Pakistan cropland extent map produced in this study using four state-of-the-art machine learning (ML) approaches, Random Forest, SVM, Naïve Bayes & CART shows an overall validation accuracy of 82%, 89% manufacturer accuracy, and 77% customer accuracy. Among these four machine learning algorithms, the CART algorithm overperformed the other three, with an impressive classification accuracy of 93%. Pakistan’s average cropland areas were calculated to be 370,200 m², and the cropland’s scale of goods indicated that sub-national croplands could be measured. The research offers a conceptual change in the development of cropland maps utilizing a remote sensing multi-date. Full article

(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)

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23 pages, 13050 KiB

Open AccessArticle

Land Use Change and Hotspot Identification in Harbin–Changchun Urban Agglomeration in China from 1990 to 2020

by Shouzhi Chang, Jian Zhao, Mingming Jia, Dehua Mao, Zongming Wang and Boyu Hou

ISPRS Int. J. Geo-Inf. 2023, 12(2), 80; https://doi.org/10.3390/ijgi12020080 - 20 Feb 2023

Cited by 3 | Viewed by 2392

Abstract

An urban agglomeration is a growth pole of regional development. However, the land uses have changed significantly due to the impacts of intense human activities. Analyzing the overall change characteristics of land use and hotspots has direct reference value for the formulation and [...] Read more.

An urban agglomeration is a growth pole of regional development. However, the land uses have changed significantly due to the impacts of intense human activities. Analyzing the overall change characteristics of land use and hotspots has direct reference value for the formulation and implementation of land use management measures. This study used a complex network of analysis methods and a cluster and outlier analysis to study the land use changes and hotspots in the Harbin–Changchun urban agglomeration (HCUA). The results showed that farmland exhibited a high weighted degree of centrality, indicating that it is the key land type in the HCUA land use change network. From 1990 to 2000, the land use change in each city mainly manifested as the loss of ecological land, whereas from 2000 to 2010 it manifested as the restoration of ecological land. From 1990 to 2020, the average path length of the network in 11 cities was less than 1.4, which was reduced in 10 cities, indicating that the stability weakened and land use change more likely occurred. Specifically, the area of ecological land reduction hotspots gradually decreased from 15,237.81 km² to 11,533.95 km². In the ecological land concentration area, the change hotspots for ecological land use and ecological function had strong consistency. The distribution and changes of hotspots were affected by policies and the terrain. The increase in ecological land around urban built-up areas, however, did not improve the landscape connectivity. Therefore, in the planning of ecological land use, attention should be paid to the landscape pattern. Full article

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15 pages, 1965 KiB

Open AccessArticle

Spatio-Temporal Transformer Recommender: Next Location Recommendation with Attention Mechanism by Mining the Spatio-Temporal Relationship between Visited Locations

by Shuqiang Xu, Qunying Huang and Zhiqiang Zou

ISPRS Int. J. Geo-Inf. 2023, 12(2), 79; https://doi.org/10.3390/ijgi12020079 - 20 Feb 2023

Cited by 3 | Viewed by 2407

Abstract

Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal [...] Read more.

Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal big data, i.e., the next point-of-interest (POI) recommendation. At present, while some advanced methods have been proposed for POI recommendation, existing work only leverages the temporal information of two consecutive LBSN check-ins. Specifically, these methods only focus on adjacent visit sequences but ignore non-contiguous visits, while these visits can be important in understanding the spatio-temporal correlation within the trajectory. In order to fully mine this non-contiguous visit information, we propose a multi-layer Spatio-Temporal deep learning attention model for POI recommendation, Spatio-Temporal Transformer Recommender (STTF-Recommender). To incorporate the spatio-temporal patterns, we encode the information in the user’s trajectory as latent representations into their embeddings before feeding them. To mine the spatio-temporal relationship between any two visited locations, we utilize the Transformer aggregation layer. To match the most plausible candidates from all locations, we develop on an attention matcher based on the attention mechanism. The STTF-Recommender was evaluated with two real-world datasets, and the findings showed that STTF improves at least 13.75% in the mean value of the Recall index at different scales compared with the state-of-the-art models. Full article

(This article belongs to the Special Issue GIS Software and Engineering for Big Data)

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30 pages, 9513 KiB

Open AccessArticle

What Do We Know about Multidimensional Poverty in China: Its Dynamics, Causes, and Implications for Sustainability

by Jing He, Cheng Fu, Xiao Li, Fu Ren and Jiaxin Dong

ISPRS Int. J. Geo-Inf. 2023, 12(2), 78; https://doi.org/10.3390/ijgi12020078 - 20 Feb 2023

Cited by 10 | Viewed by 3604

Abstract

Poverty is a primary obstacle to achieving sustainable development. Therefore, exploring the spatiotemporal dynamics and causes of poverty is of great significance to the sustainable poverty reduction of the “post poverty alleviation era” in China. This paper used the multisource big data of [...] Read more.

Poverty is a primary obstacle to achieving sustainable development. Therefore, exploring the spatiotemporal dynamics and causes of poverty is of great significance to the sustainable poverty reduction of the “post poverty alleviation era” in China. This paper used the multisource big data of 2022 counties in China from 2000 to 2015 to establish a comprehensive evaluation framework to explore the multidimensional poverty situation in China. The results showed the following findings: There is an obvious spatiotemporal heterogeneity of multidimensional poverty, showing a typical stair-like gradient from high in the west to low in the east, with the poverty level in state-designated poverty counties higher and intensifying over time. The spatial differentiation of multidimensional poverty is contributed to by multiple factors, in which the geographical condition has a stronger impact on state-designated poverty counties, while natural endowment and human resources have a stronger effect on non-state-designated poverty counties. These things considered, the regional poverty causes were relatively stable before 2015, but the poverty spatial agglomeration of some regions in the Northwest, Northeast, and Yangtze River Economic Belt has undergone significant changes after 2015. These findings can help policymakers better target plans to eliminate various types of poverty in different regions. Full article

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15 pages, 3661 KiB

Open AccessArticle

Trajectory Forecasting Using Graph Convolutional Neural Networks Based on Prior Awareness and Information Fusion

by Zhuangzhuang Yang, Chengxin Pang and Xinhua Zeng

ISPRS Int. J. Geo-Inf. 2023, 12(2), 77; https://doi.org/10.3390/ijgi12020077 - 20 Feb 2023

Cited by 1 | Viewed by 3372

Abstract

Predicting the future trajectories of multiple agents is essential for various applications in real life, such as surveillance systems, autonomous driving, and social robots. The trajectory prediction task is influenced by many factors, including individual historical trajectory, interactions between agents, and the fuzzy [...] Read more.

Predicting the future trajectories of multiple agents is essential for various applications in real life, such as surveillance systems, autonomous driving, and social robots. The trajectory prediction task is influenced by many factors, including individual historical trajectory, interactions between agents, and the fuzzy nature of an agent’s motion. While existing methods have made great progress on the topic of trajectory prediction, a lot of trajectory prediction methods take into account all pedestrians in the scene when simply modeling the influence of nearby pedestrians, and this inevitably brings redundant information. We propose a pedestrian trajectory prediction model based on prior awareness and information fusion. To make the input information more effective, for the different levels of importance of input trajectory information, we design a time information weighting module to weigh the observed trajectory information differently at different moments based on the original observed trajectory information. To reduce the impact of redundant information on trajectory prediction and to improve interaction between pedestrians, we present a spatial interaction module of multi-pedestrians and a topological graph fusion module. In addition, we use a temporal convolutional network module to obtain the temporal interactions between pedestrians. Compared to Social-STGCNN, the experimental results show that the model we propose reduces the average displacement error (ADE) and final displacement error (FDE) by 32% and 38% in the datasets of ETH and UCY, respectively. Moreover, based on this model, we design an autonomous driving obstacle avoidance system that can effectively ensure the safety of road pedestrians. Full article

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21 pages, 15471 KiB

Open AccessArticle

Urban Growth Forecast Using Machine Learning Algorithms and GIS-Based Novel Techniques: A Case Study Focusing on Nasiriyah City, Southern Iraq

by Sadeq Khaleefah Hanoon, Ahmad Fikri Abdullah, Helmi Z. M. Shafri and Aimrun Wayayok

ISPRS Int. J. Geo-Inf. 2023, 12(2), 76; https://doi.org/10.3390/ijgi12020076 - 20 Feb 2023

Cited by 1 | Viewed by 8389

Abstract

Land use and land cover changes driven by urban sprawl has accelerated the degradation of ecosystem services in metropolitan settlements. However, most optimisation techniques do not consider the dynamic effect of urban sprawl on the spatial criteria on which decisions are based. In [...] Read more.

Land use and land cover changes driven by urban sprawl has accelerated the degradation of ecosystem services in metropolitan settlements. However, most optimisation techniques do not consider the dynamic effect of urban sprawl on the spatial criteria on which decisions are based. In addition, integrating the current simulation approach with land use optimisation approaches to make a sustainable decision regarding the suitable site encompasses complex processes. Thus, this study aims to innovate a novel technique that can predict urban sprawl for a long time and can be simply integrated with optimisation land use techniques to make suitable decisions. Three main processes were applied in this study: (1) a supervised classification process using random forest (RF), (2) prediction of urban growth using a hybrid method combining an artificial neural network and cellular automata and (3) the development of a novel machine learning (ML) model to predict urban growth boundaries (UGBs). The ML model included linear regression, RF, K-nearest neighbour and AdaBoost. The performance of the novel ML model was effective, according to the validation metrics that were measured by the four ML algorithms. The results show that the Nasiriyah City expansion (the study area) is haphazard and unplanned, resulting in disastrous effects on urban and natural systems. The urban area ratio was increased by about 10%, i.e., from 2.5% in the year 1992 to 12.2% in 2022. In addition, the city will be expanded by 34%, 25% and 19% by the years 2032, 2042 and 2052, respectively. Therefore, this novel technique is recommended for integration with optimisation land use techniques to determine the sites that would be covered by the future city expansion. Full article

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20 pages, 7254 KiB

Open AccessArticle

Potential Loss of Ecosystem Service Value Due to Vessel Activity Expansion in Indonesian Marine Protected Areas

by Adam Irwansyah Fauzi, Nur Azizah, Emi Yati, Aulia Try Atmojo, Arif Rohman, Raden Putra, Muhammad Ario Eko Rahadianto, Desi Ramadhanti, Nesya Hafiza Ardani, Balqis Falah Robbani, Muhammad Ulin Nuha, Agung Mahadi Putra Perdana, Anjar Dimara Sakti, Muhammad Aufaristama and Ketut Wikantika

ISPRS Int. J. Geo-Inf. 2023, 12(2), 75; https://doi.org/10.3390/ijgi12020075 - 18 Feb 2023

Cited by 5 | Viewed by 3971

Abstract

Sustainable Development Goal (SDG) number 14 pertains to the preservation of sustainable marine ecosystems by establishing marine protected areas (MPAs). However, studies have reported massive damage to Indonesian marine ecosystems due to shipping pollution, anchors, and fishing nets. Thus, this study estimated the [...] Read more.

Sustainable Development Goal (SDG) number 14 pertains to the preservation of sustainable marine ecosystems by establishing marine protected areas (MPAs). However, studies have reported massive damage to Indonesian marine ecosystems due to shipping pollution, anchors, and fishing nets. Thus, this study estimated the potential loss of ecosystem service value due to vessel activity expansion in the MPAs of Indonesia. This study was divided into three stages. The first stage is vessel activity expansion zone modeling based on kernel density. The second stage is marine ecosystem service value modeling through semantic harmonization, reclassification, and spatial harmonization. The last stage is the overlay of the vessel expansion zone model, marine ecosystem service value model, and the MPA of Indonesia. The results of this study indicate that the marine neritic zone of Indonesia has an ecosystem service value of USD 814.23 billion, of which USD 159.87 billion (19.63%) are in the MPA. However, the increase in vessel activity that occurred in 2013–2018 could potentially lead to the loss of the ecosystem service value of USD 27.63 billion in 14 protected areas. These results can assist policymakers in determining priority conservation areas based on the threat of vessel activity and value of ecosystem services. Full article

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18 pages, 5489 KiB

Open AccessArticle

Developing and Disseminating a New Historical Geospatial Database from Kitchener’s 19th Century Map of Cyprus

by Christos Chalkias, Evangelos Papadias, Christoforos Vradis, Christos Polykretis, Kleomenis Kalogeropoulos, Athanasios Psarogiannis and Georgios Chalkias

ISPRS Int. J. Geo-Inf. 2023, 12(2), 74; https://doi.org/10.3390/ijgi12020074 - 18 Feb 2023

Cited by 5 | Viewed by 2582

Abstract

Extraction and dissemination of historical geospatial data from early maps are major goals of historical geographic information systems (HGISs) in the context of the spatial humanities. This paper illustrates the process of interpreting, georeferencing, organizing, and visualizing the content of a historical map [...] Read more.

Extraction and dissemination of historical geospatial data from early maps are major goals of historical geographic information systems (HGISs) in the context of the spatial humanities. This paper illustrates the process of interpreting, georeferencing, organizing, and visualizing the content of a historical map of Cyprus in the context of GISs and highlights the development of a national-scale spatial database of the island in the 19th century. This method was applied to Lord Kitchener’s historical map of Cyprus (published in 1885), which is considered the product of the first scientific topographic survey of Cyprus, is rich in geographic information about the area, and covers the entire island at a scale of 1:63,360. Previous attempts to create historical geodatabases have either focused on small areas or, when conducted on a national scale, have been thematically focused. The positional accuracy of the map was found to be 1.08 mm in map units, which was equivalent to 68.76 m on the ground. Accordingly, the main categories of geographic content (land cover, administrative units, settlements, transportation/communication networks, stream networks/water bodies, points of interest, annotations) were digitized from the georeferenced historical map. The Web-based application developed in this study supported the visualization of the historical geographic content of the map and its comparison with modern basemaps. The creation of the geodatabase presented in the study provides a template for similar studies and a basis for further development of the historical geodatabase of Cyprus. Full article

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24 pages, 11643 KiB

Open AccessArticle

Exploring Spatiotemporal Patterns of Expressway Traffic Accidents Based on Density Clustering and Bayesian Network

by Yunfei Zhang, Fangqi Zhu, Qiuping Li, Zehang Qiu and Yajun Xie

ISPRS Int. J. Geo-Inf. 2023, 12(2), 73; https://doi.org/10.3390/ijgi12020073 - 18 Feb 2023

Cited by 3 | Viewed by 2576

Abstract

Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geospatial analysis methods have been successfully adopted for traffic accident [...] Read more.

Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geospatial analysis methods have been successfully adopted for traffic accident analysis. As characterized by high driving speeds, diverse vehicle types, and isolated traffic environments, expressways are confronted with more serious accident risks than urban roads. In this paper, we propose a combined method based on improved density clustering and the Bayesian inference network to explore spatiotemporal patterns of expressway accidents. Firstly, the spatiotemporal accident neighborhood is integrated into the DBSCAN clustering algorithm to discover multi-scale expressway black spots. Secondly, the Bayesian network model is separately employed in both local-scale black spots and regional-scale expressway networks to fully explore spatially heterogenous accident factors in various black spots and expressways. The experimental results show that the proposed method can correctly extract spatiotemporal aggregation patterns of multi-scale expressway black spots and meanwhile efficiently discover diverse causal factors for various black spots and expressways, providing a comprehensive analysis of accident prevention and safety management. Full article

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21 pages, 2667 KiB

Open AccessArticle

Making Transportation Systems in U.S. Cities Smarter and More Inclusive: A Synthesis of Challenges and Evaluation of Strategies

by Chihuangji Wang, Fuzhen Yin, Yixuan Zhao and Li Yin

ISPRS Int. J. Geo-Inf. 2023, 12(2), 72; https://doi.org/10.3390/ijgi12020072 - 18 Feb 2023

Cited by 3 | Viewed by 2977

Abstract

Smart City (SC) strategies developed by local governments reflect how governments and planners envision SC and apply smart technologies, and what challenges they face and try to address. Little attention, however, has been given to investigating SC strategies or applications, especially in the [...] Read more.

Smart City (SC) strategies developed by local governments reflect how governments and planners envision SC and apply smart technologies, and what challenges they face and try to address. Little attention, however, has been given to investigating SC strategies or applications, especially in the U.S. context. Moreover, there is insufficient attention paid to whether SC strategies address social issues such as equity and public participatory opportunities. Based on the documentation from the U.S. Department of Transportation 2015 Smart City Challenge, we developed a framework to evaluate SC strategies on urban transportation systems using six standards: Safety, Mobility, Sustainability, Opportunity, Efficiency, and Equity. In addition, we synthesized the challenges U.S. smart cities encounter, and SC strategies proposed by local municipal governments to tackle them. Our findings show that most SC strategies aimed to improve Efficiency (78%) and Mobility (57%), while less attention has been given to providing Equity (8%) or Opportunity (7%). The most well-acknowledged challenge that the local governments face is the limited data and tools for decision-making, with 416 SC strategies (27%) proposed to address related issues. Our framework and results contribute to the future SC strategy evaluation and inclusive smart city development. Our study also identified a broad spectrum of available SC strategies planners and policymakers can refer to when designing an SC or overcoming SC challenges. Full article

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20 pages, 13337 KiB

Open AccessArticle

A Contour Line Group Simplification Method Based on Classified Terrain Features

by Yuanfu Li, Qun Sun, Wenyue Guo, Qing Xu and Xinming Zhu

ISPRS Int. J. Geo-Inf. 2023, 12(2), 71; https://doi.org/10.3390/ijgi12020071 - 18 Feb 2023

Viewed by 1873

Abstract

Contour line group simplification methods can effectively preserve terrain features during map making and producing. This process involves two main steps, namely terrain feature line extraction and contour bend selection. The terrain feature line extraction includes two steps, that is, terrain feature point [...] Read more.

Contour line group simplification methods can effectively preserve terrain features during map making and producing. This process involves two main steps, namely terrain feature line extraction and contour bend selection. The terrain feature line extraction includes two steps, that is, terrain feature point extraction and classification, and terrain feature line connection. However, to date, many similar studies have not considered the hierarchy of terrain features. Therefore, we proposed a group simplification method for contour lines based on classified terrain features and tested it on a study area with mainly positive landforms. In accordance with geomorphological theory, we divided valleys into either gradually descending or ordinary. Valley points were extracted based on the constrained Delaunay triangulation method and then classified into the two categories. Gradually descending and ordinary valley lines were then connected. The contour bends were grouped based on the valley lines extracted and then selected according to the geometric indicators of the bend group. The results have demonstrated that the valley lines extracted closely matched human perception in integrity and structure. Contour lines simplified by our method achieved effective reduction of map information and adequate retention of the main terrain structures, which are similar to those from manual simplification. Full article

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29 pages, 5753 KiB

Open AccessFeature PaperArticle

A Comparison of Cartographic and Toponymic Databases in a Multilingual Environment: A Methodology for Detecting Redundancies Using ETL and GIS Tools

by Oihana Mitxelena-Hoyos and José-Lázaro Amaro-Mellado

ISPRS Int. J. Geo-Inf. 2023, 12(2), 70; https://doi.org/10.3390/ijgi12020070 - 18 Feb 2023

Cited by 2 | Viewed by 2439

Abstract

Toponymy, a transversal discipline for geography, linguistics, and history, finds one of its main supports in cartography. Due to exhaustiveness on the territory, cadastral cartography and its toponymy have the ideal characteristics to develop systematic geographical analyses. Moreover, cadastre and geographical names are [...] Read more.

Toponymy, a transversal discipline for geography, linguistics, and history, finds one of its main supports in cartography. Due to exhaustiveness on the territory, cadastral cartography and its toponymy have the ideal characteristics to develop systematic geographical analyses. Moreover, cadastre and geographical names are part of the geographic reference data according to Annex 1 of the INSPIRE directive. This work presents the design, implementation, and application of a methodology based on Geographic Information Systems and Extract, Transform, and Load (ETL) tools for detecting coincidences between the cadastral geoinformation and the official gazetteer corresponding to the province of Gipuzkoa, Spain. Methodologically, this study proposes a solution to the issues raised by bilingualism in the study area. This problem is approached a priori, in the previous data treatment, and a posteriori, applying semantic criteria. The results show a match between the datasets of close to 40%. In this way, the uniqueness and richness of the analyzed source and its outstanding contribution to the potential integration of the official toponymic corpus are evidenced. Full article

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19 pages, 29396 KiB

Open AccessArticle

Multi-Source Data and Machine Learning-Based Refined Governance for Responding to Public Health Emergencies in Beijing: A Case Study of COVID-19

by Demiao Yu, Xiaoran Huang, Hengyi Zang, Yuanwei Li, Yuchen Qin and Daoyong Li

ISPRS Int. J. Geo-Inf. 2023, 12(2), 69; https://doi.org/10.3390/ijgi12020069 - 14 Feb 2023

Viewed by 2101

Abstract

The outbreak of COVID-19 in Beijing has been sporadic since the beginning of 2022 and has become increasingly severe since October. In China’s policy of insisting on dynamic clearance, fine-grained management has become the focus of current epidemic prevention and control. In this [...] Read more.

The outbreak of COVID-19 in Beijing has been sporadic since the beginning of 2022 and has become increasingly severe since October. In China’s policy of insisting on dynamic clearance, fine-grained management has become the focus of current epidemic prevention and control. In this paper, we conduct a refined COVID-19 risk prediction and identification of its influencing factors in Beijing based on neighborhood-scale spatial statistical units. We obtained geographic coordinate data of COVID-19 cases in Beijing and quantified them into risk indices of each statistical unit. Additionally, spatial autocorrelation was used to analyze the epidemic risk clustering characteristics. With the multi-source data, 20 influencing elements were constructed, and their spatial heterogeneity was explored by screening 8 for Multiscale Geographically weighted regression (MGWR) model analysis. Finally, a neural network classification model was used to predict the risk of COVID-19 within the sixth ring of Beijing. The MGWR model and the neural network classification model showed good performance: the R2 of the MGWR model was 0.770, and the accuracy of the neural network classification model was 0.852. The results of this study show that: (1) COVID-19 risk is uneven, with the highest clustering within the Fifth Ring Road of Beijing; (2) The results of the MGWR model show that population structure, population density, road density, residential area density, and living service facility density have significant spatial heterogeneity on COVID-19 risk; and (3) The prediction results show a high COVID-19 risk, with the most severe risk being in the eastern, southeastern and southern regions. It should be noted that the prediction results are highly consistent with the current epidemic situation in Shijingshan District, Beijing, and can provide a strong reference for fine-grained epidemic prevention and control in Beijing. Full article

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3 pages, 196 KiB

Open AccessEditorial

Climate Change Adaptation: The Role of Geospatial Data in Sustainable Infrastructures

by Cesar Casiano Flores and Joep Crompvoets

ISPRS Int. J. Geo-Inf. 2023, 12(2), 68; https://doi.org/10.3390/ijgi12020068 - 14 Feb 2023

Cited by 1 | Viewed by 2475

Abstract

Climate change is a challenging reality, making adaptation at local, national and international levels a crucial need [...] Full article

(This article belongs to the Special Issue Climate Adaptation: The Role of Geospatial Data in Sustainable Infrastructures)

20 pages, 7087 KiB

Open AccessArticle

Overview of Lightning Trend and Recent Lightning Variability over Sri Lanka

by Vindhya Kalapuge, Dilaj Maduranga, Niranga Alahacoon, Mahesh Edirisinghe, Rushan Abeygunawardana and Manjula Ranagalage

ISPRS Int. J. Geo-Inf. 2023, 12(2), 67; https://doi.org/10.3390/ijgi12020067 - 13 Feb 2023

Cited by 2 | Viewed by 3972

Abstract

The study was conducted to analyze spatial and temporal variations of lightning activity over Sri Lanka and the surrounding coastal belt region bounded by 5.75–10.00 N and 79.50–89.00 E. Flash data collected by the Lightning Imaging Sensor (LIS) on NASA’s Tropical Rainfall Measuring [...] Read more.

The study was conducted to analyze spatial and temporal variations of lightning activity over Sri Lanka and the surrounding coastal belt region bounded by 5.75–10.00 N and 79.50–89.00 E. Flash data collected by the Lightning Imaging Sensor (LIS) on NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2014 and the Lightning Imaging Sensor placed on the International Space Station (ISS) from 2018 to 2021 were used for the study. The Mann-Kendall test and Sen’s slope estimator were applied to annual and seasonal lightning data from 1998 to 2014 to identify the trends in the TRMM dataset. A positive slope of 0.23 was obtained for annual flash densities, while a slope of 0.956 was obtained for First Inter-Monsoon (FIM) seasonal data. Considering the ISS data, the annual variation of lightning activity in 2020 displays the lowest flash density, whereas the highest is represented in 2019 with a value of 10.48 flashes km⁻² year⁻¹. The highest mean flash density is observed in Colombo in 2019 at a value of 34.85 flashes km⁻² year⁻¹. Overall, April displayed the highest annual flash distribution from 2018 to 2021, whereas the second peak was mostly viewed around September and November. All districts have displayed a significant amount of lightning during April for the period 2018 to 2021. FIM displayed the highest lightning distribution over the country. When considering the seasonal variation, districts belonging to the wet zone and intermediate zone displayed most flashes during the FIM. Full article

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17 pages, 7240 KiB

Open AccessArticle

Influencing Pedestrians’ Route Choice Using Route Shape Simplification

by Peng Ti, Ruyu Dai, Fangyi Wan, Tao Xiong, Hao Wu and Zhilin Li

ISPRS Int. J. Geo-Inf. 2023, 12(2), 66; https://doi.org/10.3390/ijgi12020066 - 12 Feb 2023

Cited by 2 | Viewed by 2035

Abstract

Pedestrians’ route choice is critical for several purposes, while deliberately changing map representations can influence map users’ route choice. Simplifying routes’ geometric shapes is one way to achieve this. However, the other geometric characteristics of routes (e.g., the relative distance of different routes, [...] Read more.

Pedestrians’ route choice is critical for several purposes, while deliberately changing map representations can influence map users’ route choice. Simplifying routes’ geometric shapes is one way to achieve this. However, the other geometric characteristics of routes (e.g., the relative distance of different routes, differences in initial orientation, the number of intersections, and the direction changes) also influence pedestrians’ route choice, per relevant research. Hence, this study conducted an experimental investigation to examine the influence of route shape simplification on pedestrians’ route choice, under various geometric characteristics conditions. The results of the statistical tests and correlation analyses showed that (1) route shape simplification has a significant influence on route choice; (2) larger relative distance and direction changes reduce shape simplification’s influence, while the number of intersections and differences in initial orientation have weak effects; (3) 1.3 times the relative distance may be the threshold for the selection of recommended routes, and the improvement of visual continuity at route nodes may prove more influential. The results of this study can support the applicability of shape simplification to influence route choice. Full article

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26 pages, 3301 KiB

Open AccessArticle

Co-Creating GIS-Based Dashboards to Democratize Knowledge on Urban Resilience Strategies: Experience with Camerino Municipality

by Maria Luisa Villani, Sonia Giovinazzi and Antonio Costanzo

ISPRS Int. J. Geo-Inf. 2023, 12(2), 65; https://doi.org/10.3390/ijgi12020065 - 12 Feb 2023

Cited by 7 | Viewed by 2819

Abstract

Natural hazards are increasingly threatening our communities; hence it is imperative to provide communities with reliable information on possible impacts of such disasters, and on resilience measures that can be adopted to recover from disasters. To increase the engagement of various stakeholders in [...] Read more.

Natural hazards are increasingly threatening our communities; hence it is imperative to provide communities with reliable information on possible impacts of such disasters, and on resilience measures that can be adopted to recover from disasters. To increase the engagement of various stakeholders in decision-making processes related to resilience to natural hazards, problem-specific information needs to be presented to them in a language understandable to non-experts in the field. To this end, this paper illustrates experimentation with low-code platforms for fast digitalization of resilience reports, incorporating the perspectives of various stakeholders in the analysis, thus making informed decision-making practicable. We present a co-creation-based approach to develop GIS-based user-friendly dashboards in support to the identification of resilience strategies against natural hazards; this approach has been developed within the framework of the European project ARCH. Urban areas are regarded as complex social-ecological systems whose various dimensions should be considered in this resilience endeavor, during all phases of the Disaster Risk Reduction and Climate Change Adaptation cycle. The work presented in this paper specifically targets the possible impacts and risks that might affect the cultural heritage subsystems of our cities, generally underrepresented in the international literature related to urban resilience assessment. We describe how we applied our approach to the Camerino municipality, a historic Italian town exposed to seismic risk, which was struck by a severe earthquake sequence in 2016–2017 and discuss the results of our experience. Full article

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18 pages, 343 KiB

Open AccessReview

Self-Supervised Representation Learning for Geographical Data—A Systematic Literature Review

by Padraig Corcoran and Irena Spasić

ISPRS Int. J. Geo-Inf. 2023, 12(2), 64; https://doi.org/10.3390/ijgi12020064 - 12 Feb 2023

Cited by 3 | Viewed by 3201

Abstract

Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine learning problems. SSRL has been demonstrated to be a [...] Read more.

Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine learning problems. SSRL has been demonstrated to be a useful tool in the field of geographical information science (GIS). In this article, we systematically review the existing research literature in this space to answer the following five research questions. What types of representations were learnt? What SSRL models were used? What downstream problems were the representations used to solve? What machine learning models were used to solve these problems? Finally, does using a learnt representation improve the overall performance? Full article

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6 pages, 397 KiB

Open AccessArticle

Matching Standard and Secant Parallels in Cylindrical Projections

by Miljenko Lapaine

ISPRS Int. J. Geo-Inf. 2023, 12(2), 63; https://doi.org/10.3390/ijgi12020063 - 11 Feb 2023

Cited by 2 | Viewed by 1641

Abstract

Map projections are usually interpreted by mapping a sphere onto an auxiliary surface, and then the surface is developed into a plane. It is taken as a fact without proof that the parallels in which the auxiliary surface intersects the sphere are mapped [...] Read more.

Map projections are usually interpreted by mapping a sphere onto an auxiliary surface, and then the surface is developed into a plane. It is taken as a fact without proof that the parallels in which the auxiliary surface intersects the sphere are mapped without distortions. In a previous paper, based on a theoretical consideration and illustrated with several examples, the author concluded that explaining cylindrical projections as mapping onto a cylindrical surface is not a good approach, because it leads to misunderstanding important properties of projection. In this paper I prove that there are no equal-area, equidistant, or conformal cylindrical projections for which the standard parallel will coincide with secant parallel after folding the map into a cylinder. Full article

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17 pages, 13972 KiB

Open AccessArticle

Where Am I Now? Modelling Disorientation in Pan-Scalar Maps

by Guillaume Touya, Maïeul Gruget and Ian Muehlenhaus

ISPRS Int. J. Geo-Inf. 2023, 12(2), 62; https://doi.org/10.3390/ijgi12020062 - 10 Feb 2023

Cited by 4 | Viewed by 3549

Abstract

Disorientation is a common feeling for all users of zoomable multi-scale maps, even for those with good orientation and spatial skills. We make the assumption that this problem is mainly due to the desert fog effect, documented in human–computer interaction within multi-scale zoomable [...] Read more.

Disorientation is a common feeling for all users of zoomable multi-scale maps, even for those with good orientation and spatial skills. We make the assumption that this problem is mainly due to the desert fog effect, documented in human–computer interaction within multi-scale zoomable environments. Starting with a collection of reported experiences of disorientation, this paper explores this notion from the spatial cognition, philosophical and human–computer interaction perspectives and proposes a model of disorientation in the exploration of multi-scale maps. We argue that disorientation is a problem of reconciliation between the current map view and the mental map of the user, where landmarks visible on the map or memorised in the mental map play a key role. The causes for failed reconciliation are discussed and illustrated by our collected experiences of disorientation. Full article

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25 pages, 21829 KiB

Open AccessEditor’s ChoiceArticle

BiodivAR: A Cartographic Authoring Tool for the Visualization of Geolocated Media in Augmented Reality

by Julien Mercier, Nicolas Chabloz, Gregory Dozot, Olivier Ertz, Erwan Bocher and Daniel Rappo

ISPRS Int. J. Geo-Inf. 2023, 12(2), 61; https://doi.org/10.3390/ijgi12020061 - 9 Feb 2023

Cited by 5 | Viewed by 3176

Abstract

Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge [...] Read more.

Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge of the context. However, the creation of such experiences typically requires complex programming tools that are beyond the reach of mainstream users. We introduce BiodivAR, a web cartographic tool for the authoring of location-based AR experiences. Developed using a user-centered design methodology and open-source interoperable web technologies, it is the second iteration of an effort that started in 2016. It is designed to meet needs defined through use cases co-designed with end users and enables the creation of custom geolocated points of interest. This approach enabled substantial progress over the previous iteration. Its reliance on geolocation data to anchor augmented objects relative to the user’s position poses a set of challenges: On mobile devices, GNSS accuracy typically lies between 1 m and 30 m. Due to its impact on the anchoring, this lack of accuracy can have deleterious effects on usability. We conducted a comparative user test using the application in combination with two different geolocation data types (GNSS versus RTK). While the test’s results are undergoing analysis, we hereby present a methodology for the assessment of our system’s usability based on the use of eye-tracking devices, geolocated traces and events, and usability questionnaires. Full article

(This article belongs to the Special Issue Cartography and Geomedia)

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13 pages, 2272 KiB

Open AccessArticle

Using HyperLogLog to Prevent Data Retention in Social Media Streaming Data Analytics

by Marc Löchner and Dirk Burghardt

ISPRS Int. J. Geo-Inf. 2023, 12(2), 60; https://doi.org/10.3390/ijgi12020060 - 9 Feb 2023

Cited by 1 | Viewed by 2392

Abstract

Social media data are widely used to gain insights about social incidents, whether on a local or global scale. Within the process of analyzing and evaluating the data, it is common practice to download and store it locally. Considerations about privacy protection of [...] Read more.

Social media data are widely used to gain insights about social incidents, whether on a local or global scale. Within the process of analyzing and evaluating the data, it is common practice to download and store it locally. Considerations about privacy protection of social media users are often neglected thereby. However, protecting privacy when dealing with personal data is demanded by laws and ethics. In this paper, we introduce a method to store social media data using the cardinality estimator HyperLogLog. Based on an exemplary disaster management scenario, we show that social media data can be analyzed by counting occurrences of posts, without becoming in possession of the actual raw data. For social media data analyses like these, that are based on counting occurrences, cardinality estimation suffices the task. Thus, the risk of abuse, loss, or public exposure of the data can be mitigated and privacy of social media users can be preserved. The ability to do unions and intersections on multiple datasets further encourages the use of this technology. We provide a proof-of-concept implementation for our introduced method, using data provided by the Twitter API. Full article

(This article belongs to the Special Issue Trustful and Ethical Use of Geospatial Data)

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14 pages, 5189 KiB

Open AccessArticle

Examining the Nonlinear Impacts of Origin-Destination Built Environment on Metro Ridership at Station-to-Station Level

by Ben Liu, Yunfei Xu, Sizhen Guo, Mingming Yu, Ziyue Lin and Hong Yang

ISPRS Int. J. Geo-Inf. 2023, 12(2), 59; https://doi.org/10.3390/ijgi12020059 - 9 Feb 2023

Cited by 6 | Viewed by 2327

Abstract

Although many studies have explored the relationship between the built environment and metro ridership, the literature offers limited evidence on the nonlinear effect of origin and destination built environments on station-to-station ridership. Using data from Chongqing, this study uses the gradient boosting decision [...] Read more.

Although many studies have explored the relationship between the built environment and metro ridership, the literature offers limited evidence on the nonlinear effect of origin and destination built environments on station-to-station ridership. Using data from Chongqing, this study uses the gradient boosting decision trees (GBDT) model to explore the nonlinear impact of origin and destination built environments on metro ridership. The research results show that the built environment at the origin has a greater impact on metro ridership than the built environment at the destination. All the independent variables examined have complex nonlinear effects and threshold effects on metro ridership. The distance to the city center, the number of companies, and the building volume rate have a greater positive effect on metro ridership, both at the origin and at the destination. The research results provide suggestions for optimizing the built environment around metro stations. Full article

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16 pages, 2579 KiB

Open AccessArticle

Mini-Map Design Features as a Navigation Aid in the Virtual Geographical Space Based on Video Games

by Krzysztof Zagata and Beata Medyńska-Gulij

ISPRS Int. J. Geo-Inf. 2023, 12(2), 58; https://doi.org/10.3390/ijgi12020058 - 8 Feb 2023

Cited by 9 | Viewed by 4666

Abstract

The main objective of this study is to identify features of mini-map design as a navigational aid in the virtual geographical space in 100 popular video games for a computer platform. The following research methods were used: visual comparative analysis, classification and selection [...] Read more.

The main objective of this study is to identify features of mini-map design as a navigational aid in the virtual geographical space in 100 popular video games for a computer platform. The following research methods were used: visual comparative analysis, classification and selection of cartographic material, comparison of specific parameters for selected features of design elements, and application of cartographic design rules and popularity of design solutions in video games. The study revealed eight features of mini-map design and their popular parameters and attributes in video games, with only one game meeting all conditions of popularity: projection: orthographic; centring: player-centred; base layers: artificial; shape: circle; orientation: camera view; position: bottom left; proportions: 2.1–3%; additional navigational element: north arrow. The key attributes of the mini-map’s features were captured, which, when considered separately, complementarily and potentially holistically, confirm the possibility of designing the mini-map according to traditional cartographic design principles. The identified parameters of the mini-map can be useful not only in the design of the game cartography interface, but also for other geomedia products. Full article

(This article belongs to the Special Issue Cartography and Geomedia)

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20 pages, 8438 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Travel Cost between Geographic Coordinates Using Artificial Neural Network: Potential Application in Vehicle Routing Problems

by Keyju Lee and Junjae Chae

ISPRS Int. J. Geo-Inf. 2023, 12(2), 57; https://doi.org/10.3390/ijgi12020057 - 8 Feb 2023

Cited by 3 | Viewed by 2010

Abstract

The vehicle routing problem (VRP) attempts to find optimal (minimum length) routes for a set of vehicles visiting a set of locations. Solving a VRP calls for a cost matrix between locations. The size of the matrix grows quadratically with an increasing number [...] Read more.

The vehicle routing problem (VRP) attempts to find optimal (minimum length) routes for a set of vehicles visiting a set of locations. Solving a VRP calls for a cost matrix between locations. The size of the matrix grows quadratically with an increasing number of locations, restricting large-sized VRPs from being solved in a reasonable amount of time. The time needed to obtain a cost matrix is expensive when routing engines are used, which solve shortest path problems in the back end. In fact, details on the shortest path are redundant; only distance or time values are necessary for VRPs. In this study, an artificial neural network (ANN) that receives two geo-coordinates as input and provides estimated cost (distance and time) as output is trained. The trained ANN model was able to estimate with a mean absolute percentage error of 7.68%, surpassing the quality of 13.2% with a simple regression model on Euclidean distance. The possibility of using a trained model in VRPs is examined with different implementation scenarios. The experimental results with VRPs confirm that using ANN estimation instead of Euclidean distance produces a better solution, which is verified to be statistically significant. The results also suggest that an ANN can be a better choice than routing engines when the trade-off between response time and solution quality is considered. Full article

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17 pages, 69177 KiB

Open AccessEditor’s ChoiceArticle

Crowd Density Estimation and Mapping Method Based on Surveillance Video and GIS

by Xingguo Zhang, Yinping Sun, Qize Li, Xiaodi Li and Xinyu Shi

ISPRS Int. J. Geo-Inf. 2023, 12(2), 56; https://doi.org/10.3390/ijgi12020056 - 8 Feb 2023

Cited by 7 | Viewed by 4700

Abstract

Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation [...] Read more.

Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation model (CSSM) and a crowd denoising model (CDM) suitable for high-altitude scenarios are constructed by transfer learning. Then, based on the homography matrix between the video and remote sensing image, the crowd areas in the video are projected to the map space. Finally, according to the distance from the crowd target to the camera, the camera inclination, and the area of the crowd polygon in the geographic space, a BP neural network for the crowd density estimation is constructed. The results show the following: (1) The test accuracy of the CSSM was 96.70%, and the classification accuracy of the CDM was 86.29%, which can achieve a high-precision crowd extraction in large scenes. (2) The BP neural network for the crowd density estimation was constructed, with an average error of 1.2 and a mean square error of 4.5. Compared to the density map method, the MAE and RMSE of the CDEM-M are reduced by 89.9 and 85.1, respectively, which is more suitable for a high-altitude camera. (3) The crowd polygons were filled with the corresponding number of points, and the symbol was a human icon. The crowd mapping and visual expression were realized. The CDEM-M can be used for crowd supervision in stations, shopping malls, and sports venues. Full article

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20 pages, 18367 KiB

Open AccessEditor’s ChoiceArticle

Revealing the Impact of COVID-19 on Urban Residential Travel Structure Based on Floating Car Trajectory Data: A Case Study of Nantong, China

by Fei Tao, Junjie Wu, Shuang Lin, Yaqiao Lv, Yu Wang and Tong Zhou

ISPRS Int. J. Geo-Inf. 2023, 12(2), 55; https://doi.org/10.3390/ijgi12020055 - 8 Feb 2023

Cited by 4 | Viewed by 2192

Abstract

The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics [...] Read more.

The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics of urban mobility affected by the pandemic but has rarely examined the impact of COVID-19 on the travel conditions and psychological needs of residents. To quantitatively assess travel characteristics during COVID-19, this paper proposed a method by which to determine the purpose of residential travel by combining urban functional areas (UFAs) based on machine learning. Then, the residential travel structure, which includes origin–destination (OD) points, residential travel flow, and the proportion of flows for different purposes, was established. Based on taxi trajectory data obtained during the epidemic in Nantong, China, the case study explores changes in travel flow characteristics under the framework of the residential travel structure. Through comparison of the number and spatial distribution of OD points in the residential travel structure, it is found that residential travel hotspots decreased significantly. The ratios of commuting and medical travel increased from 43.8% to 45.7% and 7.1% to 8.1%, respectively. Conversely, the ratios of other travel types all decreased sharply. Moreover, under Maslow’s hierarchy of needs model, further insights into the impacts of COVID-19 on changes in residential psychological needs are discussed in this paper. This work can provide a reference for decision makers to cope with the change in urban traffic during a public health emergency, which is beneficial to the sustainable healthy development of cities. Full article

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23 pages, 28368 KiB

Open AccessArticle

Interactive Web Mapping Applications for 2D and 3D Geo-Visualization of Persistent Scatterer Interferometry SAR Data

by Panagiotis Kalaitzis, Michael Foumelis, Christos Vasilakos, Antonios Mouratidis and Nikolaos Soulakellis

ISPRS Int. J. Geo-Inf. 2023, 12(2), 54; https://doi.org/10.3390/ijgi12020054 - 7 Feb 2023

Cited by 3 | Viewed by 2512

Abstract

Surface motion is a complex, dynamic phenomenon that draws significant scientific attention. This study focuses on the development of a cartographic toolset for the visualization of space-borne Persistent Scatterer Interferometry (PSI) surface motion measurements. The entire archive of Sentinel-1 Synthetic Aperture Radar (SAR) [...] Read more.

Surface motion is a complex, dynamic phenomenon that draws significant scientific attention. This study focuses on the development of a cartographic toolset for the visualization of space-borne Persistent Scatterer Interferometry (PSI) surface motion measurements. The entire archive of Sentinel-1 Synthetic Aperture Radar (SAR) imagery over the broader Thessaloniki (Greece) area has been exploited to derive the PSI measurements utilizing the Surface motioN mAPPING (SNAPPING) service on the Geohazards Exploitation Platform (GEP). A collection of web map applications, interactive visualization tools, and an animated map were developed based on state-of-the-art approaches. This geo-visualization toolset consists of the following: (i) Three web map applications exploring PSI velocity rates, PSI time series, and a comparison of PSI with geodetic leveling data; (ii) Two interactive map tools focusing on 3D visualization of PSI time series and estimating velocity rates for predefined temporal frames; and (iii) An animated map of PSI time series. The utilization of the aforementioned visualization toolset provided useful conclusions about the variety of land displacement that occurs in different subareas of Northern Greece from different causes. More specifically, certain subareas with significant subsidence rates range from −2 mm/year up to −18 mm/year from 2015 to 2020. The magnitude of displacement and the underlying causes (subsidence, faults, landslides, human processes, etc.) varies across different subareas. On the other hand, a subarea of uplift trend exists in the north of the city of Thessaloniki with displacements up to 5 mm/year for the period between 2015–2020, despite being constrained by the fact that the geo-visualization platform is able to display SNAPPING PSI measurements from any location around the world, making it a useful tool for global exploration. The platform’s contents are available through a user-friendly graphical interface and are mostly addressed to domain experts as well as end-users. Opposed to similar approaches where static 2D maps and velocity rates web applications are presented through this platform, users can monitor and study the dynamic behavior of surface motion to a spatiotemporal extent more thoroughly. Full article

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20 pages, 45679 KiB

Open AccessArticle

Multi-Temporal Sentinel-1 SAR and Sentinel-2 MSI Data for Flood Mapping and Damage Assessment in Mozambique

by Manuel Nhangumbe, Andrea Nascetti and Yifang Ban

ISPRS Int. J. Geo-Inf. 2023, 12(2), 53; https://doi.org/10.3390/ijgi12020053 - 7 Feb 2023

Cited by 13 | Viewed by 3700

Abstract

Floods are one of the most frequent natural disasters worldwide. Although the vulnerability varies from region to region, all countries are susceptible to flooding. Mozambique was hit by several cyclones in the last few decades, and in 2019, after cyclones Idai and Kenneth, [...] Read more.

Floods are one of the most frequent natural disasters worldwide. Although the vulnerability varies from region to region, all countries are susceptible to flooding. Mozambique was hit by several cyclones in the last few decades, and in 2019, after cyclones Idai and Kenneth, the country became the first one in southern Africa to be hit by two cyclones in the same raining season. Aiming to provide the local authorities with tools to yield better responses before and after any disaster event, and to mitigate the impact and support in decision making for sustainable development, it is fundamental to continue investigating reliable methods for disaster management. In this paper, we propose a fully automated method for flood mapping in near real-time utilizing multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data acquired in the Beira municipality and Macomia district. The procedure exploits the processing capability of the Google Earth Engine (GEE) platform. We map flooded areas by finding the differences of images acquired before and after the flooding and then use Otsu’s thresholding method to automatically extract the flooded area from the difference image. To validate and compute the accuracy of the proposed technique, we compare our results with the Copernicus Emergency Management Service (Copernicus EMS) data available in the study areas. Furthermore, we investigated the use of a Sentinel-2 multi-spectral instrument (MSI) to produce a land cover (LC) map of the study area and estimate the percentage of flooded areas in each LC class. The results show that the combination of Sentinel-1 SAR and Sentinel-2 MSI data is reliable for near real-time flood mapping and damage assessment. We automatically mapped flooded areas with an overall accuracy of about 87–88% and kappa of 0.73–0.75 by directly comparing our prediction and Copernicus EMS maps. The LC classification is validated by randomly collecting over 600 points for each LC, and the overall accuracy is 90–95% with a kappa of 0.80–0.94. Full article

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ISPRS Int. J. Geo-Inf., Volume 12, Issue 2 (February 2023) – 57 articles

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