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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni huang Wei Zhao Nan Li Chaolei Zheng Yang {1. Changping huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials science Engineering Program Cooperating {1. Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of geography and Remote Sensing Hohai UniversityNanjing 211100China Department of geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

Spatial pattern and mechanisms of farmland abandonment in Agricultural and Pastoral Areas of Qingzang Plateau

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geography and Sustainability 2021年第3期2卷 139-150页

作者： Yuling Li Tao zhou Guanghui Jiang Guangyong Li Dingyang zhou Yu Luo State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijing 100875China School of Natural resources Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China National Geomatics Center of china Beijing 100830China

With the rapid development of social economy and urban−rural integration,the phenomenon of farmland aban-donment worldwide has proved to be one of the main trends of land use and land cover change(LUCC),and profoundly affected the rural landscape and regional ecological *** by the natural environ-ment,economic development and backward agricultural technology,the phenomenon of farmland abandonment is also common in the Qingzang Plateau(QP).Therefore,this paper adopted the spatial autocorrelation method to analyze the spatial pattern of abandonment in the agricultural and pastoral areas of the Qingzang Plateau(APA−QP)in 2.1.,and the geographically weighted regression(GWR)model to explore the effects of geograph-ical {1.,socio-economic development and location conditions on farmland *** study found that:1.From 2.1. to 2.1.,the abandoned farmland area in the APA−QP was approximately 1..2.×1.^(4)ha,with an overall abandonment rate of 1..1.%.On the whole,it showed the distribution characteristics of“strong in the south and weak in the north,strong in the east and weak in the west”.2.There were positive spatial correlation between both abandoned area and abandonment rate in the APA−QP,showing“concave”and“convex”patterns,respectively,mainly concentrated in the huang−shui Valley and the Southeast Tibet;while in the western and northern regions,the degree of abandonment was relatively low.3.Farmland abandonment in the APA−QP was mainly driven by the geographical environment changes and farmers’decision−making on farmland *** was significant spatial heterogeneity on farmland abandonment associated with the impact of geographical {1.,socio−economics and location *** geographical resource factors had a positive impact on the abandonment,and were strongly constrained by natural geographic conditions such as altitude and *** farmland {1. in the Qingzang Plateau are limited,but are of strategic signifi

关键词： Farmland abandonment Spatial pattern Formation mechanism Geographically weighted regression Qingzang Plateau,Agricultural and pastoral areas

人文与经济地理学的传承与创新：青年学者的责任

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地理研究 2018年第3期37卷 449-472页

作者：陈明星周素红何深静叶超汪明峰孙威王开泳刘艳军朱晟君马海涛姚华松马仁锋张华余建辉吴康王丰龙黄耿志赵荣钦郝庆刘春腊郑捷刘海猛中国科学院地理科学与资源研究所区域可持续发展分析与模拟院重点实验室北京100101 中山大学地理科学与规划学院广州510275 香港大学城市规划与设计系中国香港999077 华东师范大学地理科学学院上海200241 华东师范大学城市与区域科学学院上海200241 东北师范大学地理科学学院长春130024 北京大学城市与环境学院北京100871 广州大学广州发展研究院广州510405 宁波大学昂热大学联合学院地理与空间信息技术系宁波315211 环境遥感与数字城市北京市重点实验室北京师范大学地理科学学部北京100875 首都经济贸易大学城市经济与公共管理学院北京100070 华东师范大学城市发展研究院上海200062 广州地理研究所广州510070 华北水利水电大学资源与环境学院郑州450045 中国国土资源经济研究院北京101149 湖南师范大学资源与环境科学学院长沙410081 嘉兴港区开发建设管理委员会嘉兴314201

2.1.年1.2.-2.日,人文与经济地理学青年论坛暨第二届中国科学院区域可持续发展分析与模拟重点实验室青年论坛在北京国家会议中心成功举办。青年论坛以"人文与经济地理学的传承与创新:青年学者的责任与行动"为主题。莅临会场的院士、前... 详细信息

2.1.年1.2.-2.日,人文与经济地理学青年论坛暨第二届中国科学院区域可持续发展分析与模拟重点实验室青年论坛在北京国家会议中心成功举办。青年论坛以"人文与经济地理学的传承与创新:青年学者的责任与行动"为主题。莅临会场的院士、前辈和主管部门领导纷纷表示鼓励、支持和关怀青年人才的更快更好的成长。在这样的背景下,以这次青年论坛主题为题,在新时代传承和创新人文与经济地理学,青年学者责无旁贷,追寻地理学家的精神,集中探讨青年学者的责任与行动,展示人文与经济地理青年学者的思考和风采。

关键词：人文与经济地理学青年学者责任行动传承创新新时代

Spatio-temporal evolution and the influencing factors of PM_(2.5) in{1. between 2.00 and 2.1.

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Journal of Geographical sciences 2019年第2期29卷 253-270页

作者： zhou Liang zhou Chenghu YANG Fan CHE Lei WANG Bo SUN Dongqi faculty of Geomatics Lanzhou Jiaotong University Institute of Geographic sciences and Natural {1. Research CAS School of Geographic and Oceanographic sciences Nanjing University College of geography and Environment {1.s Northwest Normal University Department of geography The University of Hong Kong

High concentrations of PM_(2.5) are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_(2.5) concentrations for regional air quality control and management. In this study, PM_(2.5) data from 2.00 to 2.15 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_(2.5) concentration in china were evaluated. The main results are as follows.(1) In general, the average concentration of PM_(2.5) in china increased quickly and reached its peak value in 2.06; subsequently, concentrations remained between 2..84 and 3..08 μg/m3.(2. PM_(2.5) is strikingly heterogeneous in china, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_(2.5) concentrations are primarily in four regions, the huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_(2.5).(3. The center of gravity of PM_(2.5) has generally moved northeastward, which indicates an increasingly serious haze in eastern china. High-value PM_(2.5) concentrations have moved eastward, while low-value PM_(2.5) has moved westward.(4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The "High-High" PM_(2.5) agglomeration areas are distributed in the huang-Huai-Hai Plain, Fenhe-Weihe River Basin, sichuan Basin, and Jianghan Plain regions. The "Low-Low" PM_(2.5) agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands.(5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial va

关键词： air pollution PM_(2.5) haze spatio-temporal evolution environmental influence china

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青海省草原火灾时空格局及成因调查研究

地理科学进展 2023年第10期42卷 1973-1983页

作者：黄永生夏兴生周强潘耀忠陈英明李宏达青海师范大学高原科学与可持续发展研究院西宁810016 北京师范大学地理科学学部遥感科学国家重点实验室北京100875 青海师范大学地理科学学院西宁810016 青海省地理空间信息技术与应用重点实验室西宁810008 青海省草原总站西宁810008

了解青藏高原草原火灾时空分布以及火灾成因对于维护高原地区的生态屏障功能、畜牧业的可持续发展和灾害风险管理具有十分重要的意义。论文以青海省为研究区,基于历史火灾资料记录和2.02.2.2.年的MODIS火烧迹地产品MCD64A1.运用核密度... 详细信息

了解青藏高原草原火灾时空分布以及火灾成因对于维护高原地区的生态屏障功能、畜牧业的可持续发展和灾害风险管理具有十分重要的意义。论文以青海省为研究区,基于历史火灾资料记录和2.02.2.2.年的MODIS火烧迹地产品MCD64A1.运用核密度估计方法等对草原火灾时空分布规律以及发生原因进行了分析。结果显示:(1.青海省草原火灾呈现东多西少的格局,且泽库县、贵南县、河南县是发生草原火灾次数最多的3.县;草地类型和人类活动与草原火灾的空间分布相关性最为显著,且草地类型起主导作用,而人类活动是重要的诱发因素,即道路和居民点1.km范围内的高寒草甸是草原火灾的高发区。(2.随着野外火源的增多,2.02.2.2.年遥感和统计调查的青海省草原火灾频次呈波动上升趋势,但平均过火面积在逐步减小,说明灭火能力在不断提升;草原火灾发生的频次在季节上具有明显的双峰型特征,即深秋和冬末火灾频次最多,春季次之,夏季则比较鲜见。(3.高压线碰火、放牧取暖和燃放鞭炮是诱发青海省草原火灾的主要人为因素。研究结果可为摸清青海省草原火灾的底数和分析高原草原火灾的社会、生态影响,制定区域内有效的草原火灾预防管理体系提供一定的支持。

关键词：草原火灾时空特征起火原因青海省

黄淮海平原县域城镇化对乡村人—地—业的影响

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经济地理 2019年第5期39卷 181-190页

作者：程明洋李琳娜刘彦随周扬北京师范大学地理科学学部中国北京100875 中国科学院地理科学与资源研究所中国北京100101 中国科学院大学中国北京100101

中国长期的城乡二元结构使得城镇化未能有效带动乡村发展,并造成严重的乡村病问题。现有关于城镇化对乡村人-地-业影响的研究较少,关于影响机制的探讨更显匮乏。文章基于黄淮海平原县域数据,刻画县域乡村系统与城镇化发展的时空格局,利... 详细信息

中国长期的城乡二元结构使得城镇化未能有效带动乡村发展,并造成严重的乡村病问题。现有关于城镇化对乡村人-地-业影响的研究较少,关于影响机制的探讨更显匮乏。文章基于黄淮海平原县域数据,刻画县域乡村系统与城镇化发展的时空格局,利用地理探测器模型探析城镇化对乡村的影响,并探讨其影响机制。研究发现:①乡村人口发展空间分异不明显,而乡村土地、产业发展空间分异明显,其高值区主要分布于山东省、环渤海湾周边地区等;②黄淮海平原县域城镇化率呈逐年上升趋势,且空间分异明显,高值区主要分布于山东省、京广铁路沿线周边地区、环渤海湾周边地区等;③城镇化对乡村土地的影响不显著,同时,土地城镇化对乡村的影响不显著,乡村人口和产业的发展主要受人口城镇化和经济城镇化的影响,城镇化交互作用能够增强对乡村的影响力;④合理的城镇化发展可有效促进乡村人-业发展,有助于实现城乡融合和乡村振兴。

关键词：城镇化乡村人-地-业农民兼职化就近就业城乡融合乡村振兴黄淮海平原

Mapping essential urban land use categories in china(EULUC-{1.):preliminary results for 2.1.

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science Bulletin 2020年第3期65卷 182-187页

作者： Peng Gong Bin Chen Xuecao Li Han Liu Jie Wang Yuqi Bai Jingming Chen Xi Chen Lei Fang Shuailong Feng Yongjiu Feng Yali Gong Hao Gu Huabing huang Xiaochun huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong Li Xiaoting Li Xun Li Yuechen Li Zhilin Li Zhongde Li Chong Liu Chunxia Liu Maochou Liu Shuguang Liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi Yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun Lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang Jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang Feng Zhao Yichen Zheng Qiming {2. Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric sciences Iowa State UniversityAmesIA 50011USA State Key Laboratory of Remote Sensing science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth Laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumchi 830011China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering Laboratory for Applied Technology in Forestry&Ecology in South china College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China Beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China College of Earth sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1.2..However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[{1.4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,china and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-china 土地利用类型