25 December 2023, Volume 25 Issue 12 Previous Issue   
An Evaluation Method of Community Supply Support and Resilience Based on Complex Network
HUANG Hao, WANG Junchao, WANG Chengfang, XIE Yuanyi, ZHANG Wenchu
2023, 25 (12):  2303-2314.  doi: 10.12082/dqxxkx.2023.230208
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The assurance of a consistent supply of daily necessities in megacities is pivotal in fortifying community supply resilience. It is axiomatic that a community system is not an insular entity; rather, it intricately intertwines with various elements of urban systems. As a foundational unit of urban governance, the urban community is instrumental in facilitating a congruent nexus between supply and demand, thereby augmenting urban resilience. This study proposes an exploratory evaluation method for the urban community supply support and resilience based on complex network theory, attempting to achieve a breakthrough in the underlying theoretical framework of resilience assessment from "single system assessment" to "multi-system correlation assessment". Taking the six districts in the central city of Guangzhou as an example, we build a supply-demand network based on citizens' spatio-temporal behaviors using multi-source data such as mobile phone signaling data and other data. The attacking strategies of network are based on five community resilience indicators. Besides, the cascade failure mechanism is introduced to evaluate the network resilience, and the entropy-weighted method is employed to obtain resilience evaluation results. The influence mechanism of community resilience on the supply system is further analyzed by studying the factors affecting community node failure at different stages of supply network. The findings are as follows: (1) The proposed evaluation model of the community supply support and resilience can effectively simulate urban community supply-demand networks and evaluate the resilience of communities. Low-resilience communities are mainly categorized into three spatial types: old blocks, urban villages, and suburban blocks; (2) Through the analysis of network resilience under five different attack strategies, it is found that the dominant influencing factors are different, with the population density being the primary factor; (3) There exists a complex bidirectional relationship between community resilience and supply security, including the obvious vulnerability of low-resilience communities. And the community self-organization ability, the supply facility layout, and the linkage scheduling between supply points all affect the overall community resilience.

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RUG: A Revenue-driven User-based Relocation Approach in One-way Car-sharing Incorporating Public Transportation
MA Yuzhe, WANG Meng, LI Hui, CUI Jiangtao, LIU Junhua, LI Ruimeng
2023, 25 (12):  2315-2328.  doi: 10.12082/dqxxkx.2023.230280
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Car-sharing services can meet the diverse travel needs of users while helping to alleviate traffic congestion and reduce pollution. In many scenarios, car-sharing is more economical than taxis. One-way car-sharing allows users to rent and return cars at any station within the system, which leads to low operating costs and flexible services. However, the spatiotemporal skewness of user travel demand gives rise to imbalances between vehicle supply and demand among stations, which limits the profitability of car-sharing companies. Relocating vehicles can alleviate the above problems to some extent. Most existing studies construct optimization models with the goal of maximizing expected revenue or reducing system imbalance. The former is limited by the insufficient accuracy of travel demand prediction, and the mode of discarding definite orders and pursuing higher possible expected benefits instead cannot guarantee actual profits. To improve system balance, the latter pays more relocation costs such that reduces the profitability. To this end, we propose a revenue-driven one-way car-sharing user relocation model RUG that is suitable for real-time scenarios. The model is based on the deterministic effect of prospect theory, which ensures the current definite gains. For orders that cannot be fulfilled due to imbalanced resources, RUG provides users with alternative travel routes, which not only attempts for promising gains but also effectively balances the system. Users are incorporated into the system as relocation subjects by designing rational user incentive and acceptance models. Public transportation is utilized to break through the distance limitations of user relocation. Relocation plans are evaluated with a greedy heuristic. Experimental results on real-world New York datasets show that the RUG model has significant advantages over existing user-based relocation methods. Under the same parameters, compared to the representative user-based relocation method, RUG increases service order volume and profit by 14% and 60%, respectively. Notably, RUG can effectively raise unit profits during traffic rush hours. By incorporating travel demand forecasting, the model further increases revenue by 5.4% while also improving user service level and system balance.

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A Method for Urban Rainstorm Cascading Disaster Scenario Converting Deduction by Integrating Multi-Source Spatial Data
LIU Zhaoge, LI Xiangyang, ZHU Xiaohan
2023, 25 (12):  2329-2339.  doi: 10.12082/dqxxkx.2023.230236
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The converting evolution of cascading disaster scenario refers to that in the process of disaster scenario evolution, the disaster bearing bodies transform into new disaster hazards, forming a disaster chain. Rainstorm can easily cause serious secondary disasters such as waterlogging, debris flow and flood, and the combination of these secondary disasters will make the city more vulnerable. However, existing research on rainstorm cascading scenario deduction lacks the analysis of specific scenario evolution situations such as multi disaster combination, scenario element converting, and human-induced emergencies. Meanwhile, traditional research often relies on the probability inference based on existing scenario evolution networks, without providing a construction method for scenario evolution networks, making it difficult to adapt to the knowledge requirements of actual scenario situation converting deduction. To address the scenario converting evolution problems of urban rainstorm cascading disasters, this paper proposes a scenario converting deduction method for rainstorm cascading disaster response based on multi-source spatial data and probability analysis tools. First, based on local and non-local historical emergency cases, the scenario elements involved in the rainstorm cascading disaster scenarios and their potential converting paths are identified. Next, with the support of Baidu Encyclopedia and Wikipedia network knowledge resources, relevant scenario element features and their associations are extracted, and a Group Lasso machine learning method is adopted to achieve feature selection of involved scenario elements. Then, considering the multi-stage and complex scenario correlation in the process of cascading scenario evolution, a dynamic Bayesian network model for scenario converting deduction is constructed. Finally, a Markov chain Monte Carlo method is used to solve the Bayesian network and generate the converting probabilities. The proposed method is applied to the rainstorm response practice of Wuhan High-tech Zone. The use case results show that the proposed method can combine historical cases and network data to achieve rapid and effective generation of key scenario elements and their features, helping to improve the reliability of scenario converting deduction. At the same time, the proposed method supports the scenario converting deduction of small-scale disaster-bearing bodies such as geographic grids, which helps to provide more accurate rainstorm emergency decision-making support and provide good performance in visual analysis. The uncertainty analysis of the proposed method shows that the precision of original probabilities of key scenario element features and the size of generated geographic grids significantly affect the scenario converting deduction results. These findings provide important information for the local area and are expected to help the rainstorm disaster management of other jurisdictions.

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Mining the Influence of Industrial Pollution on Cancer: An Improved Spatial Co-location Pattern
ZHANG Lingli, WANG Lizhen, YANG Peizhong
2023, 25 (12):  2340-2360.  doi: 10.12082/dqxxkx.2023.230148
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About 60% of all known causes of cancer are related to environmental pollution. Identifying the spatial co-location pattern of prevalent neighbor spatial feature sets in geographical space is important to explore the potential relationship between industrial outdoor air pollutants and cancer risk. The traditional spatial co-location pattern mining algorithms usually calculate the prevalence of co-locations based on the frequency of cancer instances when measuring pattern interest. However, the influence of pollution source on cancer instances is also dependent on their proximity. In addition, pollution sources are also influenced by factors such as meteorological conditions, concentration levels, and the degree of harm. So, the pattern interest cannot be measured by relying solely on the number of instance occurences. To address this issue, a new spatial co-location pattern (called spatial ordered-pair pattern) is defined, and a novel mining algorithm is proposed based on the Gaussian kernel density estimation model. The Gaussian kernel function can well capture the decay of the influence of pollution sources on cancer cases with distance. To better represent the real-world diffusion of pollution sources, a spatial neighbor relationship between pollution source and cancer is defined, which considers urban wind direction, wind speed, and pollution emission concentration. Furthermore, pollution sources are categorized into different carcinogenic groups, and a weighted differentiation method is employed to distinguish pollutants based on their carcinogenic categories. The influence of various pollutants on cancer is calculated by weighting their contributions by the "carcinogenic coefficient." Therefore, a novel metric of the influence of pollution sources on cancer along with corresponding mining algorithm is presented. It not only effectively measures the impact of distance between pollution sources and cancer instances on the prevalence patterns but also models the mechanism of the influence of pollution sources on cancer by incorporating real-world conditions, overcoming the limitations of the traditional methods. Furthermore, this study improves the robustness of the method by using a smoothing factor to mitigate mining anomalies caused by uneven distributions of cancer instances. Finally, the effectiveness and efficiency of the metric and the mining algorithm proposed in this study are tested through experiments on real and synthetic datasets, and insights are also provided for cancer prevention and urban planning for Yunnan Province. The experimental results indicate that both the influence degree and participation index can accurately reflect the pattern interest from both macroscopic and microscopic perspectives. Furthermore, the mining efficiency increases by an average of 60% compared to other algorithms. The proposed influence degree measurement can more effectively capture spatial co-location patterns and can better reflect the impact of pollution sources on the incidence of cancer.

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Modelling and Storage Method for Hexagonal Remote Sensing Images in Rhombic Triacontahedron Discrete Global Grid System
LIANG Qishuang, CHEN Yihang, BEN Jin, ZHOU Jianbin, DING Junjie, DAI Jinchi
2023, 25 (12):  2361-2373.  doi: 10.12082/dqxxkx.2023.230425
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The explosive growth of multisource Remote Sensing (RS) data poses challenges to the application of fusion analysis. Discrete Global Grid System (DGGS) is a digital earth reference model supporting integration and analysis of multisource and multiscale geospatial data. In this study, a Rhombic Triacontahedron (RT) hexagonal DGGS is chosen as the basic digital framework to improve the overall fitting accuracy to the Earth and the spatial sampling efficiency of the grid which is conceptually equivalent to pixel. A mathematical model of hexagonal pixels of RS images is established, and the storage scheme of hexagonal pixels compatible with open-standard format is also proposed. First, the RS images are gridded according to the geographical location, and the hexagonal DGGS modeling of RS image was completed. Secondly, a rigorous correspondence between hexagon and rectangular pixels is established geometrically to preserve the neighborhood information of hexagon pixels by improving the way of double offset coordinates mapped to the rectangular array. Then, the GeoTIFF open-standard format is used to accurately store the hexagonal pixel values, projection and transformation parameters. Finally, a multiscale hexagonal DGGS generation algorithm based on the hexagonal DGGS standard dataset is designed. Experiment results show that using RT hexagonal equal area grid to organize global scale RS images can realize uniform sampling of global data, ensure the overall consistency of cells in different latitudes, avoid drastic changes of cells in high latitudes, and be more suitable for global scale data processing and analysis. The proposed storage scheme can not only ensure that the hexagon pixel RS image dataset is compatible with the standard file format, but also ensure that the rectangular pixels correspond to the hexagon pixels one by one. Hexagonal pixels could be stored in the open-standard format GeoTIFF with a fixed pattern in the form of rectangular arrays for data access, with transformation parameters and metadata used to reconstruct the hexagons. The image information and spatial distribution characteristics of hexagonal DGGS data are preserved well, which is more advantageous than the Soil Moisture and Ocean Salinity (SMOS) data organization scheme. This scheme break through the hexagonal with rectangular pixels in RS image data organization barriers, GIS/RS software that can be read using common hexagon pixels in RS image, and can complete the hexagonal pixels by the operation of the rectangular pixel equivalent processing, is expected to promote hexagon global discrete grid systems in RS data organization, processing, sharing, and other applications.

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Methodology for Constructing Urban Business Area/Region Knowledge Graph and the Applications in Urban Sustainable Development
LIU Yu, LI Yong
2023, 25 (12):  2374-2386.  doi: 10.12082/dqxxkx.2023.230262
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Nowadays, cities have emerged as one of the core elements for the sustainable development of human society. This also aligns well with the United Nations Sustainable Development Goals on sustainable cities. The pivotal role of cities is also demonstrated by the rapid development of big data and artificial intelligence technologies. There have been more and more studies dedicated to the realm of data-driven urban sustainability, in which the complex processes of urban sustainable development, encompassing social, economic, and ecological dimensions, are monitored, interpreted, and evaluated through massive urban data from multiple sources. However, a common limitation is that most existing studies concentrate on individual application scenarios and singular data sources and ignore the intricate interconnections among diverse urban data sources and multiple urban elements, making it challenging to explore findings across diverse urban sustainability contexts. Therefore, to address this critical gap, in this paper, we propose a novel approach for urban sustainable development driven by Urban Business Area/Region Knowledge Graph (UKG). This approach incudes two fundamental steps: the construction of a comprehensive ontology for the UKG based on massive multi-source urban data, and the subsequent synthesis of knowledge guided by this ontology to create the UKG. The construction of the UKG ontology captures important elements in cities as well as their complex interconnections, e.g., people, locations, and organizations, and their relationships in terms of spatiality, function, and association. This ontological architecture lays the foundation for the subsequent knowledge fusion, ultimately leading to the construction of UKG. The practical applications of UKG in driving urban sustainability are manifold, ranging from real-time status monitoring and nuanced interpretation of urban phenomena to the holistic evaluation of decisions made for urban sustainability. To verify the effectiveness and efficiency of the proposed approach, the paper introduces a novel cross-modality contrastive learning framework that incorporates semantic knowledge for urban sustainability. The proposed framework includes a semantic encoder and a visual encoder to capture information from UKG and urban images (satellite images and street view images), respectively. Based on the assumption that the semantic representation of UKG entities should be close to their corresponding image representations, the proposed framework successfully incorporate semantic knowledge into visual encoder, which further enhances the predictive capabilities of urban socioeconomic indicators derived from urban images. Through empirical validation, this study demonstrates the real-world applicability and generalizability of the UKG framework for urban sustainability.

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Fruit Tree Canopy Segmentation by Unmanned Aerial Vehicle Photogrammetry Coupled on Convolutional Neural Network and Attention Mechanism
HE Haiqing, ZHOU Fuyang, CHEN Min, CHEN Ting, GUAN Yunlan, ZENG Huaien, WEI Yan
2023, 25 (12):  2387-2401.  doi: 10.12082/dqxxkx.2023.230370
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The segmentation of fruit tree canopy based on Unmanned Aerial Vehicle (UAV) visible spectral images is greatly influenced by complex background information such as topographic relief, shrubs, and weeds. Although existing deep neural networks can improve the robustness of canopy segmentation to a certain extent, they ignore the global context and local detailed information of the canopy due to limited receptive field and information interaction, which restricts the improvement of canopy segmentation accuracy. To address these issues, this paper introduces the Canopy Height Model (CHM) and deep learning algorithms, and proposes a fruit tree canopy segmentation method that couples Convolutional Neural Networks (CNN) and Attention Mechanisms (AM) based on UAV photogrammetry. This method first constructs a coupled deep neural network based on CNN and AM through transfer learning to extract both the local and global high-level contextual features of fruit tree canopies. Meanwhile, considering the correlation between deep semantic features and the position information of fruit tree canopies, a local and global feature fusion module is designed to achieve collaborative tree canopy segmentation of attributes and spatial positions. Taking the citrus tree canopy segmentation as an example, the experimental results demonstrate that the use of the CHM can effectively suppress the influence of topographic relief. Our proposed method can also significantly reduce the interference of underlying weeds or shrubs on canopy segmentation, and achieves the highest Overall Accuracy (OA), F1 score, and mean Intersection over Union (mIoU) of 97.57%, 95.49%, and 94.05%, respectively. Compared with other state-of-the-art networks such as SegFormer, SETR_PUP,TransUNet, TransFuse, and CCTNet, the mIoU obtained by the proposed method increases by 1.79%, 8.83%, 1.16%, 1.43%, and 1.85%, respectively. The proposed method can achieve high-precision segmentation of fruit tree canopies with complex background information, which has important practical value for understanding the growth status of fruit trees and fine management of orchards.

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High Precision Extraction of Surface Deformation Information Based on Principal Component Spatiotemporal Analysis and Time-series InSAR: Taking Xuzhou as an Example
CHEN Yu, CHEN Si, LI Jie, LI Huaizhan, GAO Yandong, WANG Yong, DU Peijun
2023, 25 (12):  2402-2417.  doi: 10.12082/dqxxkx.2023.220779
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Urban areas often suffer from varying degrees of land surface deformation due to infrastructure construction and resources exploitation, which threatens the safety of residents' lives and property. So regular monitoring of urban surface deformation is of great significance for preventing related geological disasters. However, urban surface deformation has the characteristics of small-scale and continuous-slow change, it is necessary to process the error carefully in order to improve the monitoring accuracy. This paper proposes a high-precision surface deformation extraction method combining the principal component spatiotemporal analysis and time-series Interferometric Synthetic Aperture Radar (InSAR). Through the mining and analysis of time-series InSAR signals, a surface deformation model combined with polynomial functions is constructed to realize the hierarchical estimation of error and noise signals. Then the high-precision, small-scale surface deformation information is extracted. Taking Xuzhou, a typical city prone to geological disasters, as the research area, the results show that the proposed method can accurately separate the surface deformation information and error in the time-series InSAR signal, and the deformation monitoring accuracy is 10%~57% higher than other existing methods. The deformation rate from 2018 to 2022 is about -17~35 mm/a in Xuzhou, which is mainly distributed in the urban area, along the subway and in the old goaf. In recent 8 years, urban construction has continuously triggered local subsidence areas, the secondary deformation of the old goaf can last for more than 6 years, and the surface of several mining areas is still in an unstable state. The results can provide important technical support and decision support for high-precision monitoring of urban surface deformation and prevention of potential geological disasters.

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Calculation of Nondimensional Shape Similarity between Simple Curves
YAN Haowen, YANG Weifang, LU Xiaomin, ZHU Tianshu, MA Ben, YIN Shuoshuo
2023, 25 (12):  2418-2426.  doi: 10.12082/dqxxkx.2023.230368
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Calculation of shape similarity between curves is one of the most fundamental and theoretical problems in cartography, graphics, and geometry. Although existing machine learning methods can be used to calculate curve shape similarity, they often rely on extensive sets of sample curves, leading to a low efficiency. To address this issue, this paper proposes a method for directly calculating shape similarity between simple curves. First, two curves are moved, rotated, and scaled to obtain the optimal position where the mean distance between the two curves is the least. Second, the two curves are divided into a number of subsections based on their intersections of the curves. Third, the shape similarity within each subsection (i.e., two sub-curves) is calculated by the principle of proximity in Gestalt. Finally, the shape similarity of the two curves can be obtained by calculating the weighted shape similarity of all subsections. The proposed method is validated through the psychological experiments, and the results show that the calculated shape similarity aligns with human spatial cognition, indicating its practical applicability in specific scenarios. Moreover, the proposed method not only directly calculates curve shape similarity but also eliminates the reliance on a large number of curve samples, resulting in increased computational efficiency. The method presented in this paper provides a more efficient and direct tool for calculating curve shape similarity and holds promise for applications in various fields such as cartography, graphics, and geometry.

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Spatial Simulation of Compound Flood Hazard Risk in Coastal Megacities under Climate Change
SUN Qinke, ZHOU Liang, WANG Bao
2023, 25 (12):  2427-2438.  doi: 10.12082/230326.2023.230326
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Coastal megacities are typically situated in low-lying and densely populated areas. The occurrence of storm surge compound flooding has the potential to result in catastrophic social, economic, and ecological impacts for these coastal cities. The rising sea levels and the increased intensity and frequency of tropical cyclones caused by global warming will exacerbate the challenges faced by coastal cities. Therefore, accurately assessing compound flooding events caused by tropical cyclones is critical to protecting coastal areas from inundation. However, research on the impact of climate change on the risk of tropical cyclone induced compound flooding in coastal areas is still limited. In this study, we used the EC-EARTH3P climate model and selected a dataset of climate change tropical cyclone trajectories synthesized by the STORM model. This dataset is generated using historical data from the International Best Track Archive for Climate Stewardship (IBTrACS) to simulate synthetic tropical cyclones under future climate conditions. Subsequently, we used the coupled Delft3D FLOW & WAVE hydrodynamic model to simulate the impact of storm surge compound water levels on coastal areas due to the nonlinear effects of tropical cyclones wind fields and waves. Furthermore, we investigated the contributions of tropical cyclones and sea level rise to coastal storm surge compound flooding under different Shared Socioeconomic Pathways (SSPs) scenarios, taking the Shanghai city, located within an estuary and along the coastline of China, as our case study. The results showed that climate change had a significant impact on storm surge compound flooding. The future compound flooding disasters exhibited spatial variations in shanghai and differences in water level heights, influenced by future cyclone paths and intensities. Among these areas, Chongming district was the most seriously affected area by storm surge compound flooding. In addition, sea level rise under different climate scenarios will lead to more severe flood hazards in the Shanghai area. We found that although sea level rise will further intensify the impact of storm surge compound flooding in Shanghai, tropical cyclones will have a greater influence on future compound flooding in the city. The spatial risk analysis framework for compound flooding hazards under climate change designed in this study can also be applied to research future storm surge compound flooding hazards in other coastal megacities. Our research findings not only provide a foundational basis for policymakers and flood risk managers to identify risk vulnerable areas, but also provide significant implications for coastal adaptation measures and urban emergency response planning.

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Research Status and Development Trend of Urban Pocket Parks
WU Qiong, LI Zhigang, WU Min
2023, 25 (12):  2439-2455.  doi: 10.12082/dqxxkx.2023.230608
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Under the background of high-density urban areas and aging population in China, it is not only necessary but also urgent to strengthen the research on the design and construction of urban pocket parks. This paper uses CiteSpace, literature review, technical analysis and some other methods to conduct cluster analysis and comprehensive literature analysis on the study of urban pocket parks in China from 2000 to 2022. The results indicate that the current research hotspots in this field are pocket parks, roadside green space, landscaping, vest-pocket park, public space, landscape architecture, micro green spaces, street green land, design strategy, planning and design, etc. The research progress of pocket parks is divided into three stages: basic research (2000—2006), steady progress (2007—2018), and rapid development (2019—2022). In the basic research stage, the paper mainly studies the basic theories of street green space and vest-pocket park, which are the predecessor of the concept of pocket park, such as the development status at home and abroad, humanized design, and behavioral psychology, which lays a good foundation for the research of pocket park in China. In the stage of steady progress, the concept of pocket park is clearly proposed, the connotation of pocket park is interpreted, and the basic strategy of pocket park planning and landscape design is summarized. In the stage of rapid development, the research perspective turns to more micro aspects such as urban renewal, spatial layout of pocket park in the context of park city, optimization strategy, accessibility, fairness, interactivity, and comprehensive evaluation, etc. The research focus includes basic research, planning and design research, and evaluation research. The basic research has systematically sorted out and summarized the concept and connotation, construction scale, construction types, and usage functions of pocket parks. The planning and design research has extracted design strategies related to pocket parks from aspects such as spatial layout, landscape design, and elderly-oriented design. The evaluation research has evaluated the current situation of pocket parks from three aspects: social benefits, landscape benefits, and spatial structure. The development directions of urban pocket park research in our country in the future include: research on collaborative group layout of multiple pocket parks and optimization of internal spatial layout of a single pocket park, optimization of landscape facility layout, and plant configuration and optimization; research on the adaptability of pocket parks to the elderly, children, accessibility, and humanization according to the behavioral characteristics and psychological needs of residents, based on the theoretical foundations of environmental behavior and environmental psychology; systematically study on the coupling relationship between pocket parks and the natural environmental effects in the area by comprehensively applying architectural environmental theory, Remote Sensing (RS) technology, and Geographic Information System(GIS) technology; normative research on design guidelines, construction, operation and maintenance standard paradigms of pocket parks; research on digitization of pocket parks design and intelligent operation and maintenance management, as well as evaluation system, evaluation method and statistical analysis of pocket parks on this basis.

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Land Surface Temperature Retrieval Method Based on UAV Thermal Infrared Remote Sensing and Synchronized Atmospheric Profiles
JI Meng, XU Yongming, MO Yaping, ZHANG Yang, ZHOU Ruiyu, ZHU Shanyou
2023, 25 (12):  2456-2467.  doi: 10.12082/dqxxkx.2023.230351
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Land surface temperature is one of the important land surface parameters that characterizes the local thermal environment. Unmanned Aerial Vehicle (UAV) thermal infrared remote sensing has the advantage of high spatial resolution, which provides data support for obtaining high-resolution local land surface temperature data. In recent years, how to accurately retrieve the surface temperature based on UAV thermal infrared remote sensing data has attracted great attention. This paper systematically explores the method of retrieving land surface temperature from UAV thermal infrared remote sensing data and synchronized atmospheric vertical profile data. We collected the UAV thermal infrared images and atmospheric vertical profile data simultaneously within the central campus of Nanjing University of Information Science and Technology and its surrounding area using the UAV-based WIRIS Pro Sc thermal imager and temperature and humidity sensor. To obtain the accurate land surface thermal radiance, the atmospheric influence on the UAV thermal infrared images was eliminated by calculating the atmospheric downward thermal radiation, upward thermal radiation, and atmospheric transmittance. Land cover data were generated from UAV multispectral data, and then the land surface emissivity was calculated based on the land cover data and emissivity spectrum library. Finally, the land surface temperature was retrieved based on the land surface thermal radiance and surface emissivity. The retrieved land surface temperature was validated by comparing with the corresponding measured land surface temperature after corrections. We also analyzed the spatial pattern of the UAV land surface temperature and the factors that affect surface temperature retrieval. The results showed that the use of synchronized temperature and humidity profiles can effectively remove atmospheric effects, ensuring accuracy of off-ground radiance measurements under varying water vapor conditions. Our retrieval method can effectively retrieve surface temperature from UAV thermal infrared images. The retrieved land surface temperature achieved a coefficient of determination of 0.91. The difference between the retrieved and observed land surface temperature ranged from 0.06 to 4.96 K, with 55.56% of the samples showing differences less than 2 K. The surface temperature showed obvious spatial variation which was closely related to the type of surface cover. Artificial surfaces such as buildings and roads had relatively high surface temperatures, generally above 325 K. Natural surfaces such as woodlands and grasslands had relatively low surface temperatures, generally not exceeding 310 K. This study provides a valuable reference for retrieving high resolution land surface temperature from UAV-based thermal infrared remote sensing data, and also provides a technological support for local thermal environment monitoring.

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Segmentation of Building Roofs from Airborne LiDAR Point Clouds Using Voxel-based Region Growing
WANG Jingxue, JIANG Ying, WNAG Liqin
2023, 25 (12):  2468-2486.  doi: 10.12082/dqxxkx.2023.230305
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Accurate segmentation of building roofs is important for 3D building model reconstruction. However, traditional segmentation methods have some problems such as under-segmentation, over-segmentation, and difficulty in accurately segmenting small surfaces due to the diverse and complex characteristics of building roofs such as varying sizes and shapes, as well as the properties of LiDAR point clouds such as uneven density and large amount of data. To address these problems, this paper proposes a building roof segmentation method using voxel-based region growing to improve the segmentation accuracy from airborne LiDAR point clouds. First, the voxel size is determined based on the density of points derived from the point clouds projected onto the xy-plane, and the voxelization of the point cloud is performed. Then, the normal vector and curvature of each voxel are estimated using the PCA method. This leads to the initial roof segmentation results based on voxel growing. During the segmentation process, the voxel with the minimum curvature value is selected as the initial seed voxel, and the surrounding 26-neighborhood voxels are assigned as the growing voxels. The growth is constrained by the angle of the normal vector between the seed voxel and the growing voxels. The new seed voxels to be grown are determined iteratively based on the absolute difference of the curvature values between the current seed voxel and the growing voxels. The growth continues until no new seed voxels appear. This process is repeated by selecting a new initial seed voxel until the segmentation of all voxels is completed. Finally, the final roof surface is obtained through optimization processes such as merging the over-segmented roof surface into the initial segmentation results, repairing the integrity of the roof surface, and extracting small surfaces for complicated buildings. In this paper, airborne LiDAR point cloud data from two regions, i.e., Vaihingen and Toronto, provided by ISPRS official website, are selected to perform roof segmentation experiments of single buildings and building areas. The results show that the completeness, accuracy, and quality of the point cloud segmentation of complicated building roofs are 95.36%~99.58%, 94.83%~100%, and 90.65%~98.28%, respectively. The proposed method can effectively improve the accuracy of roof segmentation based on LiDAR point clouds without under-segmentation and over-segmentation problems, which provides reliable basic data for the automatic construction of 3D building models based on LiDAR data.

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Multi-scale Cross Dual Attention Network for Building Change Detection in Remote Sensing Images
ZHANG Jianbing, YAN Zexiao, MA Shufang
2023, 25 (12):  2487-2500.  doi: 10.12082/dqxxkx.2023.230432
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Remote sensing image change detection is a crucial technique that utilizes remote sensing technology to analyze and compare image data captured at different time periods or scenes. In practice, features at varying scales encompass diverse representation ranges, enabling the extraction of more comprehensive and detailed information. This paper proposes a Multi-Scale Cross Dual Attention Network (MSCDAN) method for building change detection in remote sensing images using the multi-scale Cross Dual Attention (CDA) mechanism and residual convolution neural network architecture. The proposed method leverages the characteristics of a residual network to extract change features of different dimensions from remote sensing images. For each feature dimension, a CDA module is created, which utilizes both cross attention and dual attention mechanisms. It combines spatiotemporal information to capture time-series features of surface changes and identifies time-series related change patterns, such as periodic and persistent changes. In this way, the multi-scale CDA module enhances the correlation between different perspectives or feature maps within the input data, which facilitates the exchange and fusion of information in multiple dimensions and enhances the model capability for complex change scenes, leading to improved change detection performance. A Fully Transposed Convolutional Upsampling Module (FTCUM) is introduced to perform local feature fusion for each point in the feature map, and the change boundary is identified by the neural network. This avoids the problems of blurring and jaggedness brought by traditional methods like bilinear interpolation and allows for end-to-end training and optimization, making the method more effective in meeting the requirements of change detection tasks. Extensive experiments are conducted on two benchmark datasets, namely WHU-CD and DSIFN, to evaluate the performance of the proposed method. Compared to the mainstream method, i.e., DTCDSCN (Dual-Task Constrained Deep Siamese Convolutional Network), our proposed method increases the accuracy by 5.13% on the DSIFN dataset and by 1.3% on the WHU-CD dataset. Additionally, for other exiting methods, the proposed method is also better than the ChangeNet and LamboiseNet on the three datasets and outperforms the improved DeepLabv3+ and SRCD-Net on the CDD Dataset. These exceptional findings across various datasets confirm the effectiveness of the proposed method in detecting changes in remote sensing images. Through the application of residual networks and attention mechanisms, our approach achieves superior results in intricate scenarios. This study shows that our proposed method performs remarkably well on various datasets. It serves as a reference for further comprehensive research on remote sensing image change detection using multi-scale cross-pairwise attention networks.

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