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An attention model with multiple decoders for solving p-Center problems

intERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2023年 125卷

作者： Chen, Xu Wang, Shaohua Li, Huilai Liang, Haojian Li, Ziqiong Lu, Hao Jilin Univ Sch Artificial Intelligence Changchun 130015 Jilin Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Remote Sensing & Digital Earth Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Jilin Univ Sch Math Changchun 130015 Jilin Peoples R China UCL Bartlett Ctr Adv Spatial Anal London W1T 4TJ England SuperMap Software Co Ltd Beijing 100015 Peoples R China

The p-Center Problem (PCP) is a classical discrete facility location problem (FLP) with broad real-world application scenarios, such as public facilities and urban emergency services facilities. Most of the current methods for solving PCP are exact and heuristic. However, as the problem conditions are added or the problem scale is increased, the complexity of exact and heuristic methods for solving PCP increases significantly, which is reflected in the exponentially increased solving time and declined accuracy, respectively. Therefore, this research experiments with an attentional model with multiple decoders for solving the p-Center problem (AMMD-PC), considering different real-world contexts. AMMD-PC learns the construction heuristics with the deep reinforcement learning method, adopting an encoder-multiple decoder structure. The multi-head self-attention mechanism in the encoder learns the feature representation of the candidate facility nodes in different sub-spaces. Multiple decoders train the construction strategy of solutions separately to increase the diversity of solutions so as to find high-quality solutions. Experiments on PCPs of different scales and conditions show that AMMD-PC is superior to traditional heuristic methods in solving time and quality. In addition, the advantages of the AMMD-PC become more pronounced as the complexity of the problem increases to the point where the exact solver cannot compute the optimal solution in an acceptable time.

关键词： Facility location problem p-Center problem Deep reinforcement learning Attention model Multiple decoders

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Quality assessment on remote sensing image based on neural networks

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年 63卷 102580-000页

作者： Chen, Guobin Zhai, Maotong Chongqing Technol & Business Univ Rongzhi Coll Chongqing Key Lab Spatial Data Min & Big Data Int Chongqing 401320 Peoples R China Jiangxi Univ Finance & Econ Sch Tourism & Urban Management Nanchang Jiangxi Peoples R China

Image quality assessment is of great significance for the designment and application of remote sensing systems. CNN based method is proposed for image quality assessment on remote sensing image in this paper. Specifically, we first introduce the convolutional neural network and deep learning method. Then a deep CNN architecture is constructed to automatically extract image features to evaluate image quality. Afterward, the information entropy threshold is used to remove the image blocks with less information content. Finally, a deep network model with two convolutional layers is used to achieve feature extraction and image quality scoring. The experimental results show that the quality score of this method has good subjective and objective consistency for multi-distortion remote sensing images and common multi-distortion images. Evaluation of distorted images does not depend on a specific database and has database independence. In addition, our proposed method is simple to achievement. (C) 2019 Published by Elsevier Inc.

关键词： Image quality assessment Remote sensing image Deep learning Information entropy

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Worldwide rooftop photovoltaic electricity generation may mitigate global warming

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NATURE CLIMATE CHANGE 2025年第4期15卷 1-10页

作者： Zhang, Zhixin Qian, Zhen Chen, min Zhu, Rui Zhang, Fan Zhong, Teng Lin, Jian Ning, Liang Xie, Wei Creutzig, Felix Tang, Wenjun Liu, Laibao Yang, Jiachuan Pu, Ye Cai, Wenjia Pu, Yingxia Liu, Deer Yang, Hui Su, Hongjun Lu, mingyue Li, Fei Cui, Xufeng Xie, Zhiwei Sheng, Tianyu Zhang, Kai Santi, Paolo Tian, Lixin Lu, Guonian Yan, Jinyue Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ PRC Nanjing Peoples R China Potsdam Inst Climate Impact Res Potsdam Germany Tech Univ Berlin Sustainabil Econ Human Settlements Berlin Germany Tech Univ Munich Sch Engn & Design Earth Syst Modelling Munich Germany Jiangsu Ctr Collaborat Innovat Geog Informat Resou Nanjing Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing Peoples R China ASTAR Inst High Performance Comp IHPC Singapore Singapore Peking Univ Inst Remote Sensing & Geog Informat Syst Sch Earth & Space Sci Beijing Peoples R China Univ Sussex Bennett Inst Innovat & Policy Accelerat Business Sch Brighton England Chinese Acad Sci Inst Tibetan Plateau Res Natl Tibetan Plateau Data Ctr TPDC State Key Lab Tibetan Plateau Earth SystEnvironm Beijing Peoples R China Univ Hong Kong Dept Geog Hong Kong Peoples R China Univ Hong Kong Inst Climate & Carbon Neutral Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China Chinese Acad Sci Inst Atmospher Phys State Key Lab Numer Modeling Atmospher Sci & Geoph Beijing Peoples R China Tsinghua Univ Inst Global Change Studies Dept Earth Syst Sci Minist Educ Ecol Field Stn East Asian Migratory Bi Beijing Peoples R China Nanjing Univ Sch Geog & Ocean Sci Nanjing Peoples R China Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing Peoples R China Jiangxi Univ Sci & Technol Sch Civil Engn & Surveying & Mapping Engn Ganzhou Peoples R China China Univ Min & Technol Sch Resources & Geosci Xuzhou Peoples R China Hohai Univ Coll Geog & Remote Sensing Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Geog Sci Coll Nanjing Peoples R China Zhongnan Univ Econ & Law Res Ctr Environm & Hlth Wuhan Peoples R China Zhongnan Univ Econ & Law Sch Business Adm Wuhan Peoples R China Shenyang Jianzhu Univ Sch Transportat & Geomatics Engn Shenyang Peoples R China MIT Dept Urban Studies & Planning Senseable City Lab Cambridge

Rooftop photovoltaic (RPV) is often understood as a niche contribution to climate change mitigation. However, the global potential of RPVs to mitigate global warming is unknown. Here we map the global rooftop area at 1-km resolution, quantifying 286,393 km2 of rooftops worldwide through geospatial data mining and artificial intelligence techniques. Using nine advanced Earth system models from the coupled model intercomparison project phase 6, we reveal that RPVs could substantially contribute to reducing global temperatures by 0.05-0.13 degrees C before 2050. Region-specific analysis underscores the variability in RPV potential and the necessity of tailored approaches to optimize RPV deployment, considering local solar resources, existing infrastructure and grid carbon intensity. Our findings reveal that leveraging RPV systems offers a viable and impactful strategy for reducing carbon footprints and combating climate change globally, while advocating targeted interventions to enhance the benefits of RPVs, particularly in areas with high solar radiation or rapid urbanization.

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Uneven impacts of COVID-19 on residents' utilization of urban parks: A case study of Guangzhou, China

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APPLIED GEOGRAPHY 2023年 153卷 102905页

作者： Li, Li Li, Xiaojian Niu, Ning He, Jialin Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Ecosyst Network Observat & Modeling Beijing 100101 Peoples R China Henan Univ Key Res Inst Yellow River Civilizat & Sustainable Coll Geog & Environm Kaifeng 475001 Peoples R China Henan Univ Econ & Law Sch Resources & Environm Acad Lab Urban & Rural Spatial Data Min Henan Prov Zhengzhou 450046 Peoples R China United Nat Environm Programme Int Ecosyst Management Partnership Beijing 100101 Peoples R China

As COVID-19 increased people's dependency on urban parks for physical and psychological well-being, it also has uncertain impacts on park utilization. Understanding these impacts and how the pandemic has contributed to them is an issue that warrants urgent attention. We used multi-source spatio-temporal data to examine urban park use before and during COVID-19 in Guangzhou, China, and constructed a set of regression models to evaluate the associated factors. We found that COVID-19 has significantly reduced the overall utilization of urban parks while also exacerbating spatial unevenness. This was due to residents' limited movement distance, and the diminished role of urban transportation affecting the efficient citywide use of parks. Meanwhile, residents' increased demand for nearby parks amplified the importance of community parks, which exacerbated the consequences caused by the uneven distribution of park resources. We propose that city administrators improve the efficiency of existing parks and prioritize the adequate placement of community parks at urban fringes to improve access. Furthermore, cities with similar layouts as Guangzhou should plan for urban parks from a multi -perspective and consider the sub-city level differences to address unevenness during the current pandemic and in the future.

关键词： Unevenness Urban park utilization COVID-19 Guangzhou

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Deep Learning-Based Automatic Extraction of Cyanobacterial Blooms from Sentinel-2 MSI Satellite data

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REMOTE SENSING 2022年第19期14卷 4763-4763页

作者： Yan, Kai Li, Junsheng Zhao, Huan Wang, Chen Hong, Danfeng Du, Yichen Mu, Yunchang Tian, Bin Xie, Ya Yin, Ziyao Zhang, Fangfang Wang, Shenglei Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Minist Ecol & Environm Peoples Republ China Satellite Applicat Ctr Ecol & Environm Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Computat Opt Imaging Technol Beijing 100094 Peoples R China China Univ Min & Technol Sch Environm & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China China Univ Geosci Beijing Sch Earth Sci & Resources Beijing 100083 Peoples R China

Cyanobacterial harmful algal blooms (CyanoHABs) in inland water have emerged as a major global environmental challenge. Although satellite remote sensing technology has been widely used to monitor CyanoHABs, there are also some automatic extraction methods of CyanoHABs based on spectral indices (such as gradient mode, fixed threshold, and the Otsu method, etc.), the accuracy is generally not very high. This study developed a high-precision automatic extraction model for CyanoHABs using a deep learning (DL) network based on Sentinel-2 multi-spectral instrument (MSI) data of Chaohu Lake, China. First, we generated the CyanoHABs "ground truth" dataset based on visual interpretation. Thereafter, we trained the CyanoHABs extraction model based on a DL image segmentation network (U-Net) and extracted CyanoHABs. Then, we compared three previous automatic CyanoHABs extraction methods based on spectral index threshold segmentation and evaluated the accuracy of the results. Based on "ground truth", at the pixel level, the F1 score and relative error (RE) of the DL model extraction results are 0.90 and 3%, respectively, which are better than that of the gradient mode (0.81,40%), the fixed threshold (0.81, 31%), and the Otsu method (0.53, 62%);at CyanoHABs area level, the R-2 of the scatter fitting between DL model result and the "ground truth" is 0.99, which is also higher than the other three methods (0.90, 0.92, 0.84, respectively). Finally, we produced the annual CyanoHABs frequency map based on DL model results. The frequency map showed that the CyanoHABs on the northwest bank are significantly higher than in the center and east of Chaohu Lake, and the most serious CyanoHABs occurred in 2018 and 2019. Furthermore, CyanoHAB extraction based on this model did not cause cloud misjudgment and exhibited good promotion ability in Taihu Lake, China. Hence, our findings indicate the high potential of the CyanoHABs extraction model based on DL in further high-precision and automati

关键词： cyanobacterial blooms CyanoHABs deep learning automatic extraction Chaohu Lake Sentinel-2 MSI

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Enhancing intercity transportation will improve the equitable distribution of high-quality health care in China

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APPLIED GEOGRAPHY 2023年 152卷

作者： Zhang, Han Zhou, Bing-Bing Liu, Shuwen Hu, Guohua Meng, Xing Liu, Xiaojuan Shi, Hong Gao, Yukun Hou, Haiyan Li, Xia East China Normal Univ Sch Geog Sci Key Lab Geog Informat Sci Minist Educ 500 Dongchuan Rd Shanghai 200241 Peoples R China Ocean Univ China Sch Int Affairs & Publ Adm 238 Songling Rd Qingdao 266100 Peoples R China East China Normal Univ Chongqing Inst 2 Huizhu Rd Chongqing 401123 Peoples R China Minist Nat Resources Key Lab Spatial temporal Big Data Anal & Applicat Shanghai 200063 Peoples R China Univ Hong Kong Sch Biol Sci Res Area Ecol & Biodivers Pokfulam Rd Hong Kong 999077 Peoples R China Southwest Minzu Univ Sch Tourism & Hist Culture Chengdu 610041 Peoples R China

With the implementation of the patient-mobility policy and the development of intercity transportation in China, interprovincial patient mobility has become an important phenomenon. However, little is known whether or to what extent intercity transportation reshapes the delivery of high-quality health care. This study mapped the accessibility of 41,259 township-level divisions to 1152 tertiary hospitals in multiple patient-mobility scenarios according to whether to seek interprovincial health care and whether to adopt intercity transportation. Our analysis included different transportation systems, such as conventional rail, high-speed rail (HSR), and air transport. Then the geographical detector model was used to contrast the spatial influences of different acces-sibility patterns on mortality. Our results show that HSR had the largest influence on the utilization of high -quality health care, while air transport was statistically insignificant. HSR can promote the equitable distribu-tion of high-quality health care in that HSR decreases the Gini index and urban-rural disparity in accessibility by 9.52% and 13.25%, respectively. Further, a synergistic effect exists between the intercity transportation and the patient-mobility policy, which could reduce the Gini index and urban-rural disparity in accessibility by an additional 5.07% and 22.50%, respectively. Achieving high-quality universal health coverage relies on sus-tainable transportation policies and services.

关键词： intercity transportation High -quality health care Accessibility Geographical detector model

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Mapping urban construction sites in China through geospatial data fusion: Methods and applications

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REMOTE SENSING OF ENVIRONMENT 2024年 315卷

作者： Zhang, Chaoqun Chen, Ziyue Luo, Lei Zhu, Qiqi Fu, Yuheng Gao, Bingbo Hu, Jianqiang Cheng, Liurun Lv, Qiancheng Yang, Jing Li, Manchun Zhou, Lei Wang, Qiao Beijing Normal Univ Fac Geog Sci Beijing 100875 Peoples R China State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals CBAS Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst AIR Key Lab Digital Earth Sci Beijing 100094 Peoples R China China Univ Geosci Sch Geog & Informat Engn Wuhan 430078 Hubei Province Peoples R China China Univ Geosci Natl Engn Res Ctr GIS Wuhan 430078 Hubei Province Peoples R China China Agr Univ Coll Land Sci & Technol Beijing 100193 Peoples R China Beijing Univ Civil Engn & Architecture Sch Geomat & Urban Spatial Informat Beijing 102616 Peoples R China Nanjing Univ Sch Geog & Ocean Sci Nanjing 210023 Peoples R China

The rapid increase in Urban Construction Sites (UCSs) due to urbanization has become a global trend. UCSs are crucial for timely tracking of urban expansion and renewal progress, understanding settlement environments and human activities, and achieving Sustainable Development Goals (SDGs) 3 and 11. However, distinguishing UCSs from other land covers remains challenging, whether using spatial texture and spectral features or time-series characteristics. There is an urgent need for a universally applicable UCS mapping method at the national scale, a gap that current research has yet to fill. In this study, we proposed a method combining geospatial data with remote sensing data for national UCS mapping under medium spatial resolution. Additionally, we combine the UCS mapping results with SDGSAT-1 GLI data to evaluate the utilization status of new construction areas, thereby supporting SDG 11.3. The results showed that, for six representative cities, the F1-Score and Matthews Correlation Coefficients (MCC) for exposed UCS mapping results ranged from 98.83 % to 99.49 % and from 0.64 to 0.77, respectively. Variable importance detected in the Random Forest (RF) model highlighted that the key to identifying UCSs lay in geospatial information describing UCS spatial distribution, including distance to roads, city boundaries, and dust-proof nets. The assessment of the utilization status for new construction areas highlights the differences in the utilization status with which cities at various stages of development utilize these new areas. We then compared the ability of UCS distribution with existing impervious surface products in reflecting the dynamics of urban construction. The results showed that UCS spatial distribution could reflect urban construction patterns more timely and accurately, providing key insights for urban planners. Overall, this study provides a universal methodology that can be referenced for mapping land covers that have low separability in spectral and

关键词： Urban construction site (UCS) SDGSAT-1 Geospatial data National-scale mapping Urbanization

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Artificial and Natural Water Bodies Change in China, 2000-2020

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WATER 2022年第11期14卷 1756-1756页

作者： Wang, Yong Lu, Shanlong Zi, Feng Tang, Hailong Li, mingyang Li, Xinru Fang, Chun Ikhumhen, Harrison Odion Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Hunan Univ Sci & Technol Sch Earth Sci & Spatial Informat Engn Xiangtan 411201 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Sun Yat Sen Univ Sch Geospatial Engn & Sci Zhuhai 519082 Peoples R China Xiamen Univ Coll Environm & Ecol Key Lab Minist Educ Coastal Wetland Ecosyst Xiamen 361102 Peoples R China

Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems;they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China's surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration.

关键词： artificial water body natural water body reservoir surface water water-related ecosystem

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Spatiotemporal dynamics of global population and heat exposure (2020-2100): based on improved SSP-consistent population projections

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ENVIRONMENTAL RESEARCH LETTERS 2022年第9期17卷 094007-094007页

作者： Li, Mengya Zhou, Bing-Bing Gao, minyi Chen, Yimin Hao, ming Hu, Guohua Li, Xia East China Normal Univ Sch Geog Sci Minist Nat Resources Key Lab Spatial Temporal Big Data Anal & Applicat 500 Dongchuan Rd Shanghai 200241 Peoples R China Ocean Univ China Sch Int Affairs & Publ Adm 238 Songling Rd Qingdao 266100 Peoples R China Sun Yat Sen Univ Sch Geog & Planning 135 West Xingang Rd Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Key Lab Urbanizat & Geosimulat 135 West Xingang Rd Guangzhou 510275 Peoples R China East China Normal Univ Sch Geog Sci Minist Educ Key Lab Geog Informat Sci 500 Dongchuan Rd Shanghai 200241 Peoples R China

To address future environmental change and consequent social vulnerability, a better understanding of future population (FPOP) dynamics is critical. In this regard, notable progress has been made in producing FPOP projections that are consistent with the Shared Socioeconomic Pathways (SSPs) at low resolutions for the globe and high resolutions for specific regions. Building on existing endeavors, here we contribute a new set of 1 km SSP-consistent global population projections (FPOP in short for the dataset) under a machine learning framework. Our approach incorporates a recently available SSP-consistent global built-up land dataset under the Coupled Model intercomparison Project 6, with the aim to address the misestimation of future built-up land dynamics underlying existing datasets of future global population projections. We show that the overall accuracy of our FPOP outperforms five existing datasets at multiple scales and especially in densely-populated areas (e.g. cities and towns). Followingly, FPOP-based assessments of future global population dynamics suggest a similar trend by population density and a spatial Matthew effect of regional population centralization. Furthermore, FPOP-based estimates of global heat exposure are around 300 billion person-days in 2020 under four SSP-Representative Concentration Pathway (RCPs), which by 2100 could increase to as low as 516 billion person-days under SSP5-RCP4.5 and as high as 1626 billion person-days under SSP3-RCP8.5-with Asia and Africa contributing 64%-68% and 21%-25%, respectively. While our results shed lights on proactive policy interventions for addressing future global heat hazard, FPOP will enable future-oriented assessments of a wide range of environmental hazards, e.g. hurricanes, droughts, and flooding.

关键词： gridded global population projections Shared Socioeconomic Pathways (SSPs) extreme heat climate variability and global change sustainable development

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Early Crop Mapping Based on Sentinel-2 Time-Series data and the Random Forest Algorithm

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REMOTE SENSING 2023年第13期15卷 3212-3212页

作者： Wei, Peng Ye, Huichun Qiao, Shuting Liu, Ronghao Nie, Chaojia Zhang, Bingrui Song, Lijuan Huang, Shanyu Taiyuan Univ Technol Coll Water Resources Sci & Engn Taiyuan 030024 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China China Univ Min & Technol Coll Geosci & Surveying Engn Beijing 100083 Peoples R China Heilongjiang Acad Agr Sci Inst Agr Remote Sensing & Informat Harbin 150086 Peoples R China Heilongjiang Univ Sci & Technol Sch Management Harbin 150022 Peoples R China Acad Agr Planning & Engn Beijing 100125 Peoples R China

Early-season crop mapping and information extraction is essential for crop growth monitoring and yield prediction, and it facilitates agricultural management and rapid response to agricultural disasters. However, training classifiers by remote sensing classification features for early crop prediction can be challenging, as early-season mapping can only use remote sensing image data during part of the crop growth period. In order to overcome this limitation, this study takes the Sanjiang Plain as an example to investigate the earliest identification time of rice, maize and soybean based on Sentinel-2 time-series data and the random forest classification algorithm. Crop information extraction was then performed. Following the analysis of the remote sensing classification features by the random forest importance approach and the subsequent normalization, the optimal features greater than or equal to 0.5 have yielded quite results in early crop mapping, and their overall accuracy was the highest in early-season mapping. The overall accuracy was observed to improve by 5% for 10 to 20 days of delay. In addition, rice, maize, and soybean were mapped at the irrigation transplanting period (10 May), jointing stage (9 July) and flowering (29 July), with an overall accuracy of 90.4%, 90.0% and 90.9%, respectively. This study shows that features suitable for early crop classification can be selected by random forest importance analysis as well as the ability of remote sensing to extract crop acreage information within the reproductive period.

关键词： crops feature selection Sentinel-2 earliest identifiable timing crop mapping

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