Proceedings

8 pages, 3869 KiB

Open AccessProceeding Paper

Urban Road Collapse Risk Assessment Study Based on InSAR Spatiotemporal Data

by Juncai Jiang, Wenfeng Bai, Yizhao Wang, Fei Wang, Qinglun He, Long Chen, Yuming Qiao, Zhi Wang and Haitao Luo

Proceedings 2024, 110(1), 24; https://doi.org/10.3390/proceedings2024110024 - 12 Dec 2024

Abstract

Urban road collapse is a common disaster in modern cities, posing a severe threat to the safety of life and property of urban residents. Effective risk assessment is crucial for preventing collapses. This study proposes a novel method for assessing the risk of [...] Read more.

Urban road collapse is a common disaster in modern cities, posing a severe threat to the safety of life and property of urban residents. Effective risk assessment is crucial for preventing collapses. This study proposes a novel method for assessing the risk of urban road collapse by integrating Interferometric Synthetic Aperture Radar (InSAR) technology with Long Short-Term Memory (LSTM) networks. We conduct experimental analysis using collapse incidents and corresponding subsidence maps from five areas in Guangzhou. The results demonstrate that the proposed model successfully establishes a mapping relationship between the temporal features of InSAR data and the risk of road collapse, enabling a risk assessment of specific areas solely based on InSAR data. This model overcomes the difficulty of obtaining numerous assessment indicators in traditional risk assessment methods, providing strong support for the prevention and control of regional road collapse risks. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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11 pages, 4179 KiB

Open AccessProceeding Paper

A Methodology for Predicting the Stability Trend of Ground Collapse Under the Water Flow

by Qinglun He, Yizhao Wang, Wenfeng Bai, Fei Wang, Xing Min, Zhi Wang, Long Chen, Juncai Jiang and Yuming Qiao

Proceedings 2024, 110(1), 23; https://doi.org/10.3390/proceedings2024110023 - 12 Dec 2024

Abstract

Ground collapse is one of the common geological hazards in modern cities. With the development of urbanization, the risk of ground collapse increases, which has a great impact on urban public safety. Ground collapse accidents typically occur due to the presence of unstable [...] Read more.

Ground collapse is one of the common geological hazards in modern cities. With the development of urbanization, the risk of ground collapse increases, which has a great impact on urban public safety. Ground collapse accidents typically occur due to the presence of unstable cavities under the surface, or the generation and expansion of cavities induced by triggering factors. Investigating the stability of cavities in the strata is significant for identifying subsidence risks and mitigating the consequences of subsidence. This study proposed a method for predicting ground subsidence settlement based on the ARMA model. Firstly, CFD-DEM coupled simulation is employed to simulate the mechanism of cavity changes in the soil layers under the influence of triggering factors and to calculate the safety coefficient for ground subsidence stability. Subsequently, the safety coefficient data at different time points are fitted to predict the subsequent stability of the subsidence. We selected a subway permeable collapse accident in Foshan City, Guangdong Province for experimental verification, and compared the predicted results with the actual situation. The result shows that this method can effectively predict the changes in ground collapse safety factor and the collapse time point. With 40% of the data, high accuracy prediction can be achieved, improving the efficiency of collapse evolution prediction and providing strong support for ground collapse risk prevention and control. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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10 pages, 2345 KiB

Open AccessProceeding Paper

A Landslide Susceptibility Mapping Method Based on Geographic Information System and Data Enhancement Techniques: A Case Study of Guangzhou City, China

by Long Chen, Yizhao Wang, Wenfeng Bai, Fei Wang, Qinglun He, Juncai Jiang, Yuming Qiao, Shiyang Xu and Zhi Wang

Proceedings 2024, 110(1), 22; https://doi.org/10.3390/proceedings2024110022 - 12 Dec 2024

Abstract

Landslides are one of the most widespread and hazardous geologic hazards in the world. Landslide susceptibility mapping (LSM) is an effective way to identify landslide-prone areas to prevent and reduce landslide hazards. However, the accuracy of LSM is greatly limited by the balance [...] Read more.

Landslides are one of the most widespread and hazardous geologic hazards in the world. Landslide susceptibility mapping (LSM) is an effective way to identify landslide-prone areas to prevent and reduce landslide hazards. However, the accuracy of LSM is greatly limited by the balance of landslide data. The collection of high-quality landslide inventories is labor-intensive. In this paper, four data enhancement techniques are used to correct the unbalanced landslide dataset based on GIS. The method is applied to LSM in Guangzhou City. And the difference in the accuracy of the enhancement is compared with two assessment models, SVM and RF. The experimental results show that the data enhancement technique helps to improve the accuracy of the evaluated models, and the evaluation results of all models show the best performance by GAN-RF. This study shows that the balance of landslide data greatly affects the accuracy of the assessment model. And the data enhancement technique enhances the robustness of the assessment model and improves the accuracy of the prediction results. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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7 pages, 2576 KiB

Open AccessProceeding Paper

The Design of a Mobile Sensing Framework for Road Surfaces Based on Multi-Modal Sensors

by Haiyang Lyu, Yu Huang, Jianchun Hua, Wenmei Li, Tianju Wu, Hanru Zhang and Wangta Ma

Proceedings 2024, 110(1), 21; https://doi.org/10.3390/proceedings2024110021 - 11 Dec 2024

Abstract

Road surface information, encompassing aspects like road surface damages and facility distributions, is vital for maintaining and updating roads in smart cities. The proposed mobile sensing framework uses multi-modal sensors, including a GPS, gyroscope, accelerometer, camera, and Wi-Fi, integrated into a Jetson Nano [...] Read more.

Road surface information, encompassing aspects like road surface damages and facility distributions, is vital for maintaining and updating roads in smart cities. The proposed mobile sensing framework uses multi-modal sensors, including a GPS, gyroscope, accelerometer, camera, and Wi-Fi, integrated into a Jetson Nano to collect comprehensive road surface information. The collected data are processed, stored, and analyzed on the server side, with results accessible via RESTful APIs. This system enables the detection of road conditions, which are visualized through the web mapping technique. Based on this concept, the Mobile Sensor Framework for Road Surface analysis (MSF4RS) is designed, and its use significantly enhances road surface data acquisition and analysis. Key contributions include (1) the integration of multi-modal IoT sensors to capture comprehensive road surface data; (2) the development of a software environment that facilitates robust data processing; and (3) the execution of experiments using the MSF4RS, which synergistically combines hardware and software components. The framework leverages advanced sensor technologies and server-based computational methods and offers a user-friendly web interface for the dynamic visualization and interactive exploration of road surface conditions. Experiments confirm the framework’s effectiveness in capturing and visualizing road surface data, demonstrating significant potential for smart city applications. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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1118 KiB

Open AccessProceeding Paper

Geospatial Analytics to Support District Efficiency, Fairness and Equity

by Jiwon Baik, Alan T. Murray, Enbo Zhou and Jing Xu

Proceedings 2024, 110(1), 25; https://doi.org/10.3390/proceedings2024110025 - 6 Dec 2024

Abstract

Districting is essential in establishing efficient, fair, and equitable boundaries across various domains such as trade and service provision, land-use development, school attendance, and political representation. This paper introduces a robust spatial optimization model tailored to delineate a districting strategy that accounts for [...] Read more.

Districting is essential in establishing efficient, fair, and equitable boundaries across various domains such as trade and service provision, land-use development, school attendance, and political representation. This paper introduces a robust spatial optimization model tailored to delineate a districting strategy that accounts for issues of equinumerosity, compactness, and contiguity, among others. The model is designed to ensure an even distribution of population across districts while ensuring compact and contiguous boundaries. By integrating advanced GIScience and spatial optimization techniques, the model aims to maximize compactness metrics and balance population, producing districts that are both geometrically efficient and demographically representative. Explicit contiguity constraints are imposed to guarantee geographical connectedness among districts. To assess the efficacy of the proposed methodology, application to a school district in California is considered. Our findings highlight GIScience capabilities to identify district boundaries that outperform traditional districting schemes in addressing population balance and compactness. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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9 pages, 1422 KiB

Open AccessProceeding Paper

Utilizing CYGNSS Data for Flood Monitoring and Analysis of Influencing Factors

by Yan Jia, Quan Liu, Dawei Zhu, Heng Yu, Yuting Jiang and Junjie Wang

Proceedings 2024, 110(1), 20; https://doi.org/10.3390/proceedings2024110020 - 5 Dec 2024

Abstract

Flood disasters are among the most severe natural calamities worldwide and typically occur in densely populated areas with abundant lakes and high rainfall. These disasters cause significant damage to the environment and human settlements. Therefore, accurately monitoring and understanding the occurrence and evolution [...] Read more.

Flood disasters are among the most severe natural calamities worldwide and typically occur in densely populated areas with abundant lakes and high rainfall. These disasters cause significant damage to the environment and human settlements. Therefore, accurately monitoring and understanding the occurrence and evolution of floods, as well as studying the influencing factors, is of great importance. This study employs CYGNSS satellite data from a constellation of small satellites equipped with reflective radar, which observe the Earth’s surface with high spatial and temporal resolution. Such systems effectively monitor the distribution of water bodies and hydrological processes on land surfaces. By collecting and analyzing CYGNSS data, we can map the distribution of water bodies during flood events to assess the extent and severity of the flooding. Additionally, this study examines various factors influencing flooding, including rainfall, land use, and topography. By compiling relevant meteorological, geographical, and hydrological data, we aim to develop a model that elucidates the impacts of these factors on the initiation and progression of floods. Ultimately, this research offers a comprehensive analysis based on CYGNSS data for monitoring floods and their influencing factors. The goal is to yield significant insights and explore the potential of using CYGNSS data in flood monitoring efforts. In the context of global climate change and the increasing frequency of flood disasters, these findings are expected to provide a crucial scientific basis for improving flood prevention and management strategies, thereby helping to mitigate losses and enhance our warning and disaster response capabilities. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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8 pages, 3075 KiB

Open AccessProceeding Paper

Detecting Polder Water Surface Dynamics Using Multi-Source Remote Sensing Data

by Heng Yu, Dawei Zhu, Sicheng Wan, Yuting Jiang, Chao Lu, Rui Zhang and Yan Jia

Proceedings 2024, 110(1), 19; https://doi.org/10.3390/proceedings2024110019 - 5 Dec 2024

Abstract

The flow of water in plain river network areas is significantly influenced by various factors, including human activities, upstream water influx, downstream tidal forces, and local rainfall. This leads to a complex situation where poor drainage and flooding are frequent occurrences. Polders play [...] Read more.

The flow of water in plain river network areas is significantly influenced by various factors, including human activities, upstream water influx, downstream tidal forces, and local rainfall. This leads to a complex situation where poor drainage and flooding are frequent occurrences. Polders play a crucial role in water management and agriculture in China by facilitating drainage and flood control, as well as supporting irrigation and aquaculture. As agriculture and water resource management continue to modernize, the monitoring and analysis of changes in water bodies and levels within polders become increasingly important. This paper primarily focuses on the detection of open water features in polder regions, mainly employing Sentinel-2 satellite imagery. By analyzing these data, we can effectively monitor the changes in the surface areas of water bodies within the polders. For our study, we have selected the Lixiahe region in China as it frequently experiences both flooding and drought conditions and houses a considerable number of polder zones. This region provides an ideal case study to explore the intricate relationship between water management infrastructure and natural hydrological phenomena. The importance of this research is manifold and significant. It advances the capabilities of remote sensing technologies and provides valuable insights for improved water level management in complex agricultural landscapes. The research introduces new methods and technical support for the remote sensing of water level changes in polders, contributing scientific support for enhanced water management and agricultural water conservation. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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7 pages, 3543 KiB

Open AccessProceeding Paper

Spatial Optimization for Facilities with Anisotropic Coverage

by Enbo Zhou, Alan T. Murray, Jiwon Baik and Jing Xu

Proceedings 2024, 110(1), 18; https://doi.org/10.3390/proceedings2024110018 - 5 Dec 2024

Abstract

Locating facilities such as directional sensors and lights represents a challenging problem given their anisotropic coverage. This paper proposes a spatial optimization model to locate and orient facilities simultaneously. A finite dominating set approach considering occlusion is presented to reformulate the problem as [...] Read more.

Locating facilities such as directional sensors and lights represents a challenging problem given their anisotropic coverage. This paper proposes a spatial optimization model to locate and orient facilities simultaneously. A finite dominating set approach considering occlusion is presented to reformulate the problem as an integer programming problem. The model is then solved exactly using branch and bound. Applications to surveillance camera deployment in a university context demonstrate the performance of the proposed approach. The results show a pivotal enhancement in the estimation of coverage provided by the facility system. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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7 pages, 11708 KiB

Open AccessProceeding Paper

Urban Functional Zone Mapping by Integrating Multi-Source Data and Spatial Relationship Characteristics

by Daoyou Zhu, Xu Dang, Wenjia Shi, Yixiang Chen and Wenmei Li

Proceedings 2024, 110(1), 17; https://doi.org/10.3390/proceedings2024110017 - 4 Dec 2024

Abstract

Timely and precise acquisition of urban functional zone (UFZ) information is crucial for effective urban planning, management, and resource allocation. However, current UFZ mapping approaches primarily focus on individual functional units’ visual and semantic characteristics, often overlooking the crucial spatial relationships between them, [...] Read more.

Timely and precise acquisition of urban functional zone (UFZ) information is crucial for effective urban planning, management, and resource allocation. However, current UFZ mapping approaches primarily focus on individual functional units’ visual and semantic characteristics, often overlooking the crucial spatial relationships between them, resulting in classification inaccuracies. To address this limitation, our study presents a novel framework for UFZ classification that seamlessly integrates visual image features, Points of Interest (POI) semantic attributes, and spatial relationship information. This framework leverages the OpenStreetMap (OSM) road network to partition the study area into functional units, employs a graph model to represent urban functional nodes and their intricate spatial topological relationships, and harnesses the capabilities of Graph Convolutional Network (GCN) to fuse these multi-dimensional features through end-to-end learning for accurate urban function discrimination. Experimental evaluations utilizing Gaofen-2 (GF-2) satellite imagery, POI data, and OSM road network information from Shenzhen, China have yielded remarkable results. Our method has achieved significant improvements in classification accuracy across all functional categories, surpassing approaches that rely solely on visual or semantic features. Notably, the overall classification accuracy reached an impressive 87.92%, marking a significant 2.08% increase over methods that disregard spatial relationship features. Furthermore, our method has demonstrated superior performance when compared to similar techniques, underscoring its effectiveness and potential for widespread application in UFZ classification. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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8 pages, 10782 KiB

Open AccessProceeding Paper

Analysis of Cultivated Land Fragmentation and Its Influencing Factors in Northern China

by Qianyu Nie, Shuang Zhao, Zhiheng Wang and Dingyang Zhang

Proceedings 2024, 110(1), 16; https://doi.org/10.3390/proceedings2024110016 - 3 Dec 2024

Abstract

Revealing the spatial distribution patterns and driving factors of cultivated land fragmentation is of great significance for optimizing the utilization and management of cultivated land resources and promoting moderate-scale agricultural operations. Based on the 2020 Landsat8 Collection2 surface reflectance data from Changping District, [...] Read more.

Revealing the spatial distribution patterns and driving factors of cultivated land fragmentation is of great significance for optimizing the utilization and management of cultivated land resources and promoting moderate-scale agricultural operations. Based on the 2020 Landsat8 Collection2 surface reflectance data from Changping District, Binhai New Area, and Hulin City, this study comprehensively utilized the landscape pattern index method and the moving window method was used to evaluate the spatial distribution characteristics of cultivated land fragmentation. Furthermore, the Geodetector was employed to analyze the factors which influenced cultivated land fragmentation. The results indicated that the comprehensive indices of cultivated land fragmentation in the three research areas are 0.133, 0.132, and 0.140, respectively, suggesting that the degree of fragmentation is highest in Binhai New Area and lowest in Hulin City. Apart from topographical factors, there are differences in the secondary driving factors of cultivated land fragmentation across the different study areas. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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9 pages, 2175 KiB

Open AccessProceeding Paper

Geographical Spatial Characteristics and Low-Carbon Sustainable Paths of Coal Resource-Exhausted Cities

by Xiaotong Feng, Min Tan, Jihong Dong and Thomas Kienberger

Proceedings 2024, 110(1), 15; https://doi.org/10.3390/proceedings2024110015 - 3 Dec 2024

Abstract

Resource-exhausted cities are cities where the ratio of exploited reserves to recoverable reserves exceeds 70%. Long-term energy extraction and consumption lead to weak economic growth, idle industrial land, and ecological imbalances. It is imperative to explore sustainable development paths that are green and [...] Read more.

Resource-exhausted cities are cities where the ratio of exploited reserves to recoverable reserves exceeds 70%. Long-term energy extraction and consumption lead to weak economic growth, idle industrial land, and ecological imbalances. It is imperative to explore sustainable development paths that are green and low-carbon. The spatial characteristics of cities and the structure of energy networks are crucial foundations for low-carbon development and energy security in cities. The main research content includes three aspects: (1) The first involves the identification of the distribution characteristics of typical resource- exhausted cities worldwide. This mainly includes coal, oil, metallurgy, forestry, and non-metallic minerals. Among them, coal resource-exhausted cities are the most numerous, mainly distributed in China, Australia, the United States, etc. (2) The second includes an analysis of the spatial characteristics of resource-exhausted cities in China. This involves taking 24 resource-exhausted prefecture-level cities in China as the research objects, integrating geographic data such as Points of Interest (POIs), and using machine learning for accurate quantitative identification and spatial delineation of urban functions. The production space and ecological space of cities show an aggregated distribution pattern, while the living space is randomly distributed. (3) The third is based on urban energy consumption data, utilizing the modified gravity model and social network analysis (SNA), and analyzing the centrality/relevance, relationship density and frequency, and accessibility. The average degree of centrality of the 17 coal-related industries is 5.529, demonstrating the energy network structure of resource-exhausted cities. This paper provides data foundations and technical methods for achieving urban energy renewal, ecosystem stability, and optimized spatial structures. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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7 pages, 1105 KiB

Open AccessProceeding Paper

Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example

by Zihao Feng, Runjie Wang, Xianglei Liu, Ming Huang and Liang Huo

Proceedings 2024, 110(1), 14; https://doi.org/10.3390/proceedings2024110014 - 3 Dec 2024

Abstract

The comfort level of air temperature in a region is one of the influencing factors that affect tourists’ choice of tourism purpose. As a national red cultural mecca, the study of air temperature in Jiangxi Province can provide an important scientific reference for [...] Read more.

The comfort level of air temperature in a region is one of the influencing factors that affect tourists’ choice of tourism purpose. As a national red cultural mecca, the study of air temperature in Jiangxi Province can provide an important scientific reference for the development of tourism and the dissemination of red culture. Temperature is one of the most important indicators for climate comfort studies. Thus, in this paper, the average air temperature in Jiangxi Province in 2018 was studied. Three interpolation methods of Kriging interpolation, the inverse distance weight method, and the spline function method were used to spatially interpolate the data from 26 weather stations to obtain the spatial distribution map of air temperature for comparative study. At the same time, the method of cross-validation was adopted, and the average error and the root-mean-square error were quoted as the evaluation indexes for accuracy assessment. The conclusions of this paper are as follows: (1) the ME of IDW and spline method can reach 0.02–1.82 °C and the RMSE can reach 1.22–2.72 °C; (2) Kriging interpolation improves the RMSE by 27% and 55% compared to IDW and spline function methods, respectively; (3) considering the relatively sparse distribution of meteorological stations in Jiangxi Province, Kriging interpolation can avoid the extreme value phenomenon due to the influence of distance by reasonably choosing the shape and size associated with the surface space in the process of solving. Moreover, the results of this experimental study show that the accuracy of the kriging interpolation method is higher, so this method is more suitable for the spatial interpolation of the temperature in Jiangxi Province. In conclusion, this study provides a reference for the study of temperature comfort in Jiangxi Province. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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6 pages, 1354 KiB

Open AccessProceeding Paper

The Point Cloud Reduction Algorithm Based on the Feature Extraction of a Neighborhood Normal Vector and Fuzzy-c Means Clustering

by Hongxiao Xu, Donglai Jiao and Wenmei Li

Proceedings 2024, 110(1), 13; https://doi.org/10.3390/proceedings2024110013 - 3 Dec 2024

Abstract

The three-dimensional model of geographic elements serves as the primary medium for digital visualization. However, the original point cloud model is often vast and includes considerable redundant data, resulting in inefficiencies during the three-dimensional modeling process. To address this issue, this paper proposes [...] Read more.

The three-dimensional model of geographic elements serves as the primary medium for digital visualization. However, the original point cloud model is often vast and includes considerable redundant data, resulting in inefficiencies during the three-dimensional modeling process. To address this issue, this paper proposes a point cloud reduction algorithm that leverages domain normal vectors and fuzzy-c means (FCM) clustering for feature extraction. The algorithm first extracts the edge points of the model and then utilizes domain normal vectors to extract the overall feature points of the model. Next, utilizing point cloud curvature, coordinate information, and geometric attributes, the algorithm applies the FCM clustering method to isolate local feature points. Non-feature points are then sampled using an enhanced farthest point sampling technique. Finally, the algorithm integrates edge points, feature points, and non-feature points to generate simplified point cloud data. This paper compares the proposed algorithm with traditional methods, including the uniform grid method, random sampling method, and curvature sampling method, and evaluates the simplified point cloud in terms of reduction level and reconstruction time. This approach effectively preserves critical feature information from the majority of point cloud data, thereby addressing the complexities inherent in original point cloud models. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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9 pages, 1927 KiB

Open AccessProceeding Paper

UAV-Based Multi-Sensor Data Fusion for 3D Building Detection

by Mohsen Shahraki, Ahmed El-Rabbany and Ahmed Elamin

Proceedings 2024, 110(1), 12; https://doi.org/10.3390/proceedings2024110012 - 3 Dec 2024

Abstract

Three-dimensional building extraction is crucial for urban planning, environmental analysis, and autonomous navigation. One method for data collection involves using unmanned aerial vehicles (UAVs), which allow for flexible and rapid data acquisition. However, accurate 3D building extraction from these data remains challenging due [...] Read more.

Three-dimensional building extraction is crucial for urban planning, environmental analysis, and autonomous navigation. One method for data collection involves using unmanned aerial vehicles (UAVs), which allow for flexible and rapid data acquisition. However, accurate 3D building extraction from these data remains challenging due to the abundance of information in high-resolution datasets. To tackle this problem, a novel UAV-based multi-sensor data fusion model is developed, which utilizes deep neural networks (DNNs) to enhance point cloud segmentation. Urban datasets, acquired by a UAV equipped with a Zenmuse L1 payload, are collected and used to train, validate, and test the DNNs. It is shown that most building extraction results have precision, accuracy, and F-score values greater than 0.96. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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8 pages, 11999 KiB

Open AccessProceeding Paper

Sustainable Urbanization in the Yangtze River Basin Through Built-Up Area Extraction

by Zeshuo Li, Haoyu Fan and Yan Jin

Proceedings 2024, 110(1), 11; https://doi.org/10.3390/proceedings2024110011 - 3 Dec 2024

Abstract

The Yangtze River Economic Belt (YREB), spanning nine provinces and cities in eastern, central, and western China, is a key region for China’s urbanization. This study utilizes the Google Earth Engine (GEE) platform to integrate four land cover and impervious surface datasets, constructing [...] Read more.

The Yangtze River Economic Belt (YREB), spanning nine provinces and cities in eastern, central, and western China, is a key region for China’s urbanization. This study utilizes the Google Earth Engine (GEE) platform to integrate four land cover and impervious surface datasets, constructing built-up area datasets for the YREB at five-year intervals from 1985 to 2020. The employed random forest model achieved an overall accuracy (OA) and kappa coefficient both exceeding 90%, demonstrating high reliability and precision in the generated datasets. Using this dataset, we then calculated the United Nations Sustainable Development Goal 11.3.1 (SDG11.3.1) index for the YREB and its nine constituent provinces, which includes the land consumption rate (LCR), population growth rate (PGR), and ratio of land consumption rate to population growth rate (LCRPGR). The results show that the LCRPGR index for the entire region over the 35-year period is −0.006, 4.84, 0.44, 0.77, 5.15, 0.09, and 2.13, respectively. These values suggest that the land consumption rate significantly outpaced the population growth rate during 1990–1995, 2005–2010, and 2015–2020, reflecting periods of rapid urban development. This study offers important insights into urban expansion in the YREB, offering valuable data to inform sustainable urbanization practices. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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8 pages, 4795 KiB

Open AccessProceeding Paper

Unsupervised Domain Adaptive Transfer Learning for Urban Built-Up Area Extraction

by Feifei Peng, Shuai Yao, Yixiang Chen and Wenmei Li

Proceedings 2024, 110(1), 10; https://doi.org/10.3390/proceedings2024110010 - 3 Dec 2024

Abstract

Built-up areas are the main gathering place for human activities. The widespread availability of various satellite sensors provides a rich data source for mapping built-up areas. Deep learning can automatically learn multi-level features of targets from sample data in an end-to-end manner, overcoming [...] Read more.

Built-up areas are the main gathering place for human activities. The widespread availability of various satellite sensors provides a rich data source for mapping built-up areas. Deep learning can automatically learn multi-level features of targets from sample data in an end-to-end manner, overcoming the limitations of traditional methods based on handcrafted features. However, existing deep-learning-based methods rely on the quantity and distribution of sample data, and the trained models often exhibit limited generalization ability when faced with image data from novel scenarios. To effectively tackle this issue, this study proposes an unsupervised domain adaptive transfer learning method based on adversarial machine learning. This method aims to utilize the feature information of the source domain to train a classifier suitable for target domain feature discrimination without requiring a target domain label, and achieve built-up area extraction of different sensor images. The model comprises a feature extraction module, a label classification module, and a domain discrimination module. Through adversarial training, the feature knowledge from the source domain is transferred to the target domain, achieving feature alignment and efficient discrimination of built-up areas. The Gaofen-2 (GF-2) and Sentinel-2 datasets were employed for experimental evaluation. The results show that the proposed method, trained on the GF-2 image dataset (source domain), can be transferred unsupervised to the Sentinel-2 image dataset (target domain), demonstrating robust detection performance. Further comparative experiments have also demonstrated the superiority of our method in extracting built-up areas through transfer learning. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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7 pages, 4626 KiB

Open AccessProceeding Paper

Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine

by Pengdu Li, Cuiheng Ye, Lei Li and Jie Jiang

Proceedings 2024, 110(1), 9; https://doi.org/10.3390/proceedings2024110009 - 3 Dec 2024

Abstract

Ecosystem services, including climate regulation and biodiversity maintenance, are vital for human well-being and sustainable development. The ecological quality evaluation system, based on the three dimensions of ecological function, ecological stability, and ecological stress, was established using the Pixel Information Expert Engine (PIE-Engine) [...] Read more.

Ecosystem services, including climate regulation and biodiversity maintenance, are vital for human well-being and sustainable development. The ecological quality evaluation system, based on the three dimensions of ecological function, ecological stability, and ecological stress, was established using the Pixel Information Expert Engine (PIE-Engine) and Moderate Resolution Imaging Spectroradiometer (MODIS) products to assess the ecological quality of the Taihu Basin from 2001 to 2020. The findings reveal that (1) the average Ecological Function Index (EFI) of the Taihu Basin showed a trend of initially decreasing and then increasing, with significant spatial differences. The highest EFI was observed in the western and southwestern regions of the Taihu Basin, which are mainly covered by forest and grassland, while the relatively lower EFI was found in the densely urbanized northeastern part of the basin. (2) The average Ecological Stability Index (ESI) of the Taihu Basin showed a similar trend to the EFI, with the rate of increase higher than the rate of decrease. The ESI was higher in the southwestern part, while in the southeastern and western parts of cropland and wetlands, the ESI was relatively low. (3) The Ecological Threat Index (ETI) of the Taihu Basin showed a fluctuating decrease followed by an increase, with the rate of increase higher than the rate of decrease. The reduction in grassland and the expansion of urban space are the main factors contributing to the increase in ecological stress. The research results of this paper will provide an important reference value for the coordinated and sustainable development of the economy and ecosystem in the Taihu Basin. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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9 pages, 6603 KiB

Open AccessProceeding Paper

Spatially Seamless Downscaling of a SMAP Soil Moisture Product Through a CNN-Based Approach with Integrated Multi-Source Remote Sensing Data

by Yan Jin, Haoyu Fan, Zeshuo Li and Yaojie Liu

Proceedings 2024, 110(1), 8; https://doi.org/10.3390/proceedings2024110008 - 3 Dec 2024

Abstract

Surface soil moisture (SSM) is crucial for understanding terrestrial hydrological processes. Despite its widespread use since 2015, the Soil Moisture Active and Passive (SMAP) SSM dataset faces challenges due to its inherent low spatial resolution and data gaps. This study addresses these limitations [...] Read more.

Surface soil moisture (SSM) is crucial for understanding terrestrial hydrological processes. Despite its widespread use since 2015, the Soil Moisture Active and Passive (SMAP) SSM dataset faces challenges due to its inherent low spatial resolution and data gaps. This study addresses these limitations through a deep learning approach aimed at interpolating missing values and downscaling soil moisture data. The result is a seamless, daily 1 km resolution SSM dataset for China, spanning from 1 January 2016 to 31 December 2022. For the original 9 km daily SMAP products, a convolutional neural network (CNN) with residual connections was employed to achieve the spatially seamless 9 km SSM data, integrating multi-source remote sensing data. Subsequently, auxiliary data including land cover, land surface temperatures, vegetation indices, vegetation temperature drought indices, elevation, and soil texture were integrated into the CNN-based downscaling model to generate the spatially seamless 1 km SSM. Comparative analysis of the spatially seamless 9 km and 1 km SSM datasets with ground observations yielded unbiased root mean square error values of 0.09 cm³/cm³ for both, demonstrating the effectiveness of the downscaling method. This approach provides a promising solution for generating high-resolution, spatially seamless soil moisture data to meet the needs of hydrological, meteorological, and agricultural applications. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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11 pages, 5505 KiB

Open AccessProceeding Paper

Combining Deep Learning and Street View Images for Urban Building Color Research

by Wenjing Li, Qian Ma and Zhiyong Lin

Proceedings 2024, 110(1), 7; https://doi.org/10.3390/proceedings2024110007 - 3 Dec 2024

Abstract

The color of a cityscape plays a significant role in its atmosphere; however, the traditional city color analysis methods cover a wide range but are not precise enough, requiring field sampling, a lot of manual comparisons, and lacking quantitative analysis of color. With [...] Read more.

The color of a cityscape plays a significant role in its atmosphere; however, the traditional city color analysis methods cover a wide range but are not precise enough, requiring field sampling, a lot of manual comparisons, and lacking quantitative analysis of color. With the development of artificial intelligence, deep learning and computer vision technology show great potential in urban environment research. In this document, we focus on “building color” and present a deep learning-based framework that combines geospatial big data with AI technology to extract and analyze urban color information. The framework is composed of two phases: “deep learning” and “quantitative analysis.” In the “deep learning” phase, a deep convolutional neural network (DCNN)-based color extraction model is designed to automatically learn building color information from street view images; in the “quantitative analysis” phase, building color is quantitatively analyzed at the overall and local levels, and a color clustering model is designed to quantitatively display the color relationship to comprehensively understand the current status of urban building color. The research method and results of this paper are one of the effective ways to combine geospatial big data with GeoAI, which is helpful to the collection and analysis of urban color and provides direction for the construction of urban color information management. Full article

(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)

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Modern Metallurgy 2024

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