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Proceedings, 2024, Geoinformatics'2024

The 31st International Conference on Geoinformatics

Toronto, ON, Canada | 14–16 August 2024

Volume Editors:
Dongmei Chen, Queens University, Canada
Yuhong He, University of Toronto Mississauga, Canada
Songnian Li, Toronto Metropolitan University, Canada

Number of Papers: 21
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Cover Story (view full-size image): The International Conference on Geoinformatics is the official conference of the International Association of Chinese Professionals in Geographic Information Sciences (CPGIS). This annual conference, [...] Read more.
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8 pages, 2806 KiB  
Proceeding Paper
Constructing Rasterized Covariates from LiDAR Point Cloud Data via Structured Query Language
by Rory Pittman and Baoxin Hu
Proceedings 2024, 110(1), 1; https://doi.org/10.3390/proceedings2024110001 - 3 Dec 2024
Viewed by 266
Abstract
For point cloud data compiled over larger spatial domains, the rasterization of features is effectively streamlined by means of structured query language (SQL). This comprises enhanced control with filtering data and implementing specific metrics for summarization to derive environmental covariates. LiDAR (light detection [...] Read more.
For point cloud data compiled over larger spatial domains, the rasterization of features is effectively streamlined by means of structured query language (SQL). This comprises enhanced control with filtering data and implementing specific metrics for summarization to derive environmental covariates. LiDAR (light detection and ranging) point cloud data were analyzed via SQL to generate rasterized covariates of the digital terrain model (DTM), canopy height model (CHM), and a gap fraction for a boreal study region in Northern Ontario, Canada. These features, along with topographic covariates computed from the DTM, were later ascertained as important for subsequent tree species classification research. Full article
(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)
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7 pages, 3886 KiB  
Proceeding Paper
Event/Visual/IMU Integration for UAV-Based Indoor Navigation
by Ahmed Elamin and Ahmed El-Rabbany
Proceedings 2024, 110(1), 2; https://doi.org/10.3390/proceedings2024110002 - 2 Dec 2024
Viewed by 313
Abstract
Unmanned aerial vehicle (UAV) navigation in indoor environments is challenging due to varying light conditions, the dynamic clutter typical of indoor spaces, and the absence of GNSS signals. In response to these complexities, emerging sensors, such as event cameras, demonstrate significant potential in [...] Read more.
Unmanned aerial vehicle (UAV) navigation in indoor environments is challenging due to varying light conditions, the dynamic clutter typical of indoor spaces, and the absence of GNSS signals. In response to these complexities, emerging sensors, such as event cameras, demonstrate significant potential in indoor navigation with their low latency and high dynamic range characteristics. Unlike traditional RGB cameras, event cameras mitigate motion blur and operate effectively in low-light conditions. Nevertheless, they exhibit limitations in terms of information output during scenarios of limited motion, in contrast to standard cameras that can capture detailed surroundings. This study proposes a novel event-based visual–inertial odometry approach for precise indoor navigation. In the proposed approach, the standard images are leveraged for feature detection and tracking, while events are aggregated into frames to track features between consecutive standard frames. The fusion of IMU measurements and feature tracks facilitates the continuous estimation of sensor states. The proposed approach is evaluated and validated using a controlled office environment simulation developed using Gazebo, employing a P230 simulated drone equipped with an event camera, an RGB camera, and IMU sensors. This simulated environment provides a testbed for evaluating and showcasing the proposed approach’s robust performance in realistic indoor navigation scenarios. Full article
(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)
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8 pages, 4314 KiB  
Proceeding Paper
Exploitation of Class Activation Map to Improve Land Cover and Land Use Classification Using Deep Learning
by Taewoong Ham and Baoxin Hu
Proceedings 2024, 110(1), 3; https://doi.org/10.3390/proceedings2024110003 - 2 Dec 2024
Viewed by 229
Abstract
This study investigates the potential of gradient-weighted class activation mapping (Grad-CAM++) in enhancing land cover and land use (LCLU) classification using deep learning models. A U-Net and an Attention U-Net model were trained on Sentinel-2 imagery to classify 10 LCLU classes in a [...] Read more.
This study investigates the potential of gradient-weighted class activation mapping (Grad-CAM++) in enhancing land cover and land use (LCLU) classification using deep learning models. A U-Net and an Attention U-Net model were trained on Sentinel-2 imagery to classify 10 LCLU classes in a study area in Northern Ontario, Canada (centered at 49.17° N, 83.03° W). The classes included water, wetland, deciduous forest, mixed forest, coniferous forest, barren, urban/development, agriculture, shrubland, and no data (masked areas). The U-Net model achieved overall accuracy of 70.68%, a mean intersection over union (IoU) of 0.4852, and an F1 score of 0.7150, slightly outperforming the Attention U-Net model. Grad-CAM++ visualizations revealed that both models correctly focused on relevant features for each LCLU class, enhancing the interpretability of deep learning models in remote sensing applications. The findings suggest that integrating Grad-CAM++ with deep learning architectures can improve model transparency and guide future enhancements in LCLU classification tasks. Full article
(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)
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8 pages, 1242 KiB  
Proceeding Paper
Subtropical Tree Species Identification Based on Domain Generalization with Hyperspectral Images
by Xu Wang, Wenmei Li, Lei Zhao and Yuhong He
Proceedings 2024, 110(1), 4; https://doi.org/10.3390/proceedings2024110004 - 2 Dec 2024
Viewed by 181
Abstract
Subtropical tree species identification is a crucial aspect of forest resource monitoring, and the advancement of deep learning has introduced new opportunities for subtropical tree species identification. But, its performance often relies heavily on the availability of sufficient training samples. In this study, [...] Read more.
Subtropical tree species identification is a crucial aspect of forest resource monitoring, and the advancement of deep learning has introduced new opportunities for subtropical tree species identification. But, its performance often relies heavily on the availability of sufficient training samples. In this study, we propose a method for tree species identification via domain generalization with hyperspectral images. The network comprises a generator and a discriminator; the former produces similar samples, and the latter outputs predicted probabilities and classification loss to guide model optimization. The results demonstrate its superiority over traditional CNN-based algorithms. Full article
(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)
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7 pages, 5004 KiB  
Proceeding Paper
How to Understand Carbon Intensity? A Comparative Study of China and Europe Regarding the Relationship Between Rural Development Regimes and Carbon Emission Intensity
by Jiaqi Li and Yishao Shi
Proceedings 2024, 110(1), 5; https://doi.org/10.3390/proceedings2024110005 - 2 Dec 2024
Viewed by 207
Abstract
Background: China’s rural revitalisation policy has promoted the transformation of rural industries, which always neglect the “dual-carbon” goal in rural. Rural industrial upgrading in Europe can inspire sustainable rural development in China. Methods: Based on EDGAR and NEP data, the carbon emission intensity [...] Read more.
Background: China’s rural revitalisation policy has promoted the transformation of rural industries, which always neglect the “dual-carbon” goal in rural. Rural industrial upgrading in Europe can inspire sustainable rural development in China. Methods: Based on EDGAR and NEP data, the carbon emission intensity of rural ecosystems was calculated in terms of area. By Isodata cluster algorithm and k-means, the Chinese and European rural regions were classified based on agricultural areas. Pearson’s coefficient and geographical convergent cross-mapping (GCCM) were used to explore the correlation and causality between carbon intensity and development patterns in rural China and Europe. Results: The expansion of the land share of the primary industry and land consolidation will lead to more carbon emissions in the study areas. The proportion of land used for tertiary industry increases carbon emission intensity in rural China, but not in European study areas. The area carbon emission intensity shows that the fragmented industrial layout may hinder the development of rural industries in Europe, but not in China, from a productivity perspective. Conclusions: Carbon emission distribution and industrial development patterns vary spatially. GCCM can help identify the interactions for this variation between China and Europe, providing insights into China’s sustainable development. Full article
(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)
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7 pages, 4765 KiB  
Proceeding Paper
Spatiotemporal Analysis of Carbon Emissions and Uptake Changes from Land-Use in the Yangtze River Delta Region
by Cuiheng Ye, Jie Jiang and Yan Jin
Proceedings 2024, 110(1), 6; https://doi.org/10.3390/proceedings2024110006 - 3 Dec 2024
Viewed by 254
Abstract
Land use change and energy consumption caused by human activities is the primary source of carbon emissions and a driver of climate change. The study focused on the Yangtze River Delta (YRD), using the China Land Cover Dataset (CLCD) to calculate the region’s [...] Read more.
Land use change and energy consumption caused by human activities is the primary source of carbon emissions and a driver of climate change. The study focused on the Yangtze River Delta (YRD), using the China Land Cover Dataset (CLCD) to calculate the region’s carbon emissions from 1990 to 2020. Based on the Natural Segment Method, the spatial distribution of carbon emissions in the YRD region were constructed by dividing them into three categories: heavy, medium, and light. The results indicate that: (1) Carbon emissions of the YRD region was 594.02 million tons at the end of 2020, an increase of 468.53 million tons compared with that of 1990. The impervious surface was the major source of carbon emissions, accounting for more than 98.51% of the total, and woodland was the most important carbon sink, accounting for more than 91.32% of the total carbon uptake. (2) The carbon emissions increase rate over the 30-year period has risen from rapid to gradual, with the fastest rate of increase occurring between 2000 and 2010. (3) Differences in economic development and land type lead to spatial variability in carbon emissions. Regions with substantial emissions were predominantly located in coastal areas, indicating a trend toward shifting inland. The assessment of carbon emissions is helpful for designing emissions mitigation policies. Full article
(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)
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11 pages, 5505 KiB  
Proceeding 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
Viewed by 348
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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9 pages, 6603 KiB  
Proceeding 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
Viewed by 280
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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7 pages, 4626 KiB  
Proceeding 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
Viewed by 245
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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8 pages, 4795 KiB  
Proceeding 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
Viewed by 277
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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8 pages, 11999 KiB  
Proceeding 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
Viewed by 310
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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9 pages, 1927 KiB  
Proceeding 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
Viewed by 164
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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6 pages, 1354 KiB  
Proceeding 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
Viewed by 297
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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7 pages, 1105 KiB  
Proceeding 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
Viewed by 203
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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9 pages, 2175 KiB  
Proceeding 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
Viewed by 155
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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8 pages, 10782 KiB  
Proceeding 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
Viewed by 255
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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7 pages, 11708 KiB  
Proceeding 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
Viewed by 340
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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7 pages, 3543 KiB  
Proceeding 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
Viewed by 222
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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8 pages, 3075 KiB  
Proceeding 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
Viewed by 311
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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9 pages, 1422 KiB  
Proceeding 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
Viewed by 330
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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7 pages, 2576 KiB  
Proceeding 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
Viewed by 112
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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