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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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 Zhou 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 Faculty 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

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基于RS和gis的黄河三角洲主要人工地物时空特征分析

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遥感技术与应用 2022年第3期37卷 751-762页

作者：徐鹤侯西勇李东韩美刘玉斌王晓利樊超中国科学院烟台海岸带研究所山东烟台264003 中国科学院大学北京100049 中国科学院海岸带环境过程与生态修复重点实验室山东烟台264003 山东省海岸带环境过程重点实验室山东烟台264003 山东师范大学地理与环境学院山东济南250358

油井、风机和道路是黄河三角洲区域的主要人工地物,明确其时空特征对保障黄河三角洲的生态安全具有重要意义。利用Landsat、SPOT、高分二号等中、高分辨率卫星影像,提取并建立2000年和2015年黄河三角洲主要人工地物分布数据集,使用核密... 详细信息

油井、风机和道路是黄河三角洲区域的主要人工地物,明确其时空特征对保障黄河三角洲的生态安全具有重要意义。利用Landsat、SPOT、高分二号等中、高分辨率卫星影像,提取并建立2000年和2015年黄河三角洲主要人工地物分布数据集,使用核密度法、平均最近邻法、加权分析法、克里金插值法和空间自相关分析法分析2000~2015年黄河三角洲人工地物的时空特征。结果表明:(1) 2015年风机空间分布具有不均衡性,呈现两大核心区为主、3个小集聚区为辅的空间分布格局,总体呈西北—东南走向,空间集聚特征明显;(2) 2000年和2015年油井空间分布具有不均衡性且存在时间变异特征,2000年油井呈现两大核心为主,团块成片分布为辅的空间分布格局,2015年油井呈现三大核心区为主、核心区周围油井呈鞍状分布的空间格局特征;(3) 2000~2015年黄河三角洲路网密度不断提高,空间集聚特征明显,路网密度高值主要分布在黄河三角洲中部地区,并呈现向西部和南部扩散趋势。总的来说,黄河三角洲地区人工地物数量大、分布范围广、增长速度快,对自然保护区的生态安全造成了巨大威胁。通过识别和分析黄河三角洲人工地物及其时空特征,可为该地区湿地生态系统保护提供科学依据和决策参考。

关键词： RS gis 黄河三角洲人工地物时空特征

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虚拟地理环境中沉浸式多人协同交互技术研究及实现

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地球信息科学学报 2018年第8期20卷 1055-1063页

作者：袁帅陈斌易超徐丙立北京大学遥感与地理信息系统研究所北京100871 中国人民武装警察部队警种学院北京102202 陆军装甲兵工程学院北京100072

在VGE的发展过程中,自然高效的人机交互一直是其研究的核心内容之一,但在当前众多VGE应用案例中,用户仍然是基于传统的人机交互界面与VGE进行交流,这导致了:(1)用户的沉浸感不高;(2)多人在VGE中协同交互的需求无法得到有效满足。因此,... 详细信息

在VGE的发展过程中,自然高效的人机交互一直是其研究的核心内容之一,但在当前众多VGE应用案例中,用户仍然是基于传统的人机交互界面与VGE进行交流,这导致了:(1)用户的沉浸感不高;(2)多人在VGE中协同交互的需求无法得到有效满足。因此,为了实现VGE中的沉浸式多人协同交互,本文首先综述了VGE中人机交互技术研究和应用现状,并简要阐述了动作捕捉技术发展历程、基本原理及其在提高VGE用户交互沉浸感上的优势。在此基础上,对VGE中沉浸式多人协同交互系统进行了重点设计,主要包括分析了基于动作捕捉技术的沉浸式多人协同交互模式及其对应的交互动作语义,提出了多人协同冲突控制机制,以及着重介绍了VGE中沉浸式多人协同交互系统的结构和功能。最后,以北京大学为例开发了虚拟燕园辅助规划设计原型系统,并进行了实验。研究结果表明,沉浸式多人协同交互技术的交互效果受到用户认可,它在提高化身之间及化身与VGE之间的协同交互方面具有明显的优势,可应用于地学教育、城市规划、应急救援和军事指挥等领域,具有良好的应用前景。

关键词：虚拟地理环境动作捕捉人机交互多人协同沉浸感

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Mapping essential urban land use categories in china(EULUC-china):preliminary results for 2018

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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 Zhou 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[3,4],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

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