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Zhao T, Wang S, Ouyang C, Chen M, Liu C, Zhang J, Yu L, Wang F, Xie Y, Li J, Wang F, Grunwald S, Wong BM, Zhang F, Qian Z, Xu Y, Yu C, Han W, Sun T, Shao Z, Qian T, Chen Z, Zeng J, Zhang H, Letu H, Zhang B, Wang L, Luo L, Shi C, Su H, Zhang H, Yin S, Huang N, Zhao W, Li N, Zheng C, Zhou Y, Huang C, Feng D, Xu Q, Wu Y, Hong D, Wang Z, Lin Y, Zhang T, Kumar P, Plaza A, Chanussot J, Zhang J, Shi J, Wang L. Artificial intelligence for geoscience: Progress, challenges, and perspectives. Innovation (N Y) 2024; 5:100691. [PMID: 39285902 PMCID: PMC11404188 DOI: 10.1016/j.xinn.2024.100691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 08/17/2024] [Indexed: 09/19/2024] Open
Abstract
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 techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In 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 knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.
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Affiliation(s)
- Tianjie Zhao
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Sheng Wang
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Chaojun Ouyang
- State Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
| | - Chenying Liu
- Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany
| | - Jin Zhang
- The National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
| | - Long Yu
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Fei Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Xie
- School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jun Li
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Fang Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Department of Chemistry, Technical University of Munich, 85748 Munich, Germany
| | - Sabine Grunwald
- Soil, Water and Ecosystem Sciences Department, University of Florida, PO Box 110290, Gainesville, FL, USA
| | - Bryan M Wong
- Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy, University of California, California, Riverside, CA 92521, USA
| | - Fan Zhang
- Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Zhen Qian
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
| | - Yongjun Xu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chengqing Yu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Han
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Tao Sun
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Zezhi Shao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tangwen Qian
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhao Chen
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Jiangyuan Zeng
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Huai Zhang
- Key Laboratory of Computational Geodynamics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Husi Letu
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Bing Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Li Wang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Lei Luo
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
| | - Chong Shi
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Hongjun Su
- College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China
| | - Hongsheng Zhang
- Department of Geography, The University of Hong Kong, Hong Kong 999077, SAR, China
| | - Shuai Yin
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Ni Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Wei Zhao
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Nan Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing 210044, China
- School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Chaolei Zheng
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Yang Zhou
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Changping Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Defeng Feng
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qingsong Xu
- Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany
| | - Yan Wu
- Key Laboratory of Vertebrate Evolution and Human Origins of Chinese Academy of Sciences, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Danfeng Hong
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenyu Wang
- Department of Catchment Hydrology, Helmholtz Centre for Environmental Research - UFZ, Halle (Saale) 06108, Germany
| | - Yinyi Lin
- Department of Geography, The University of Hong Kong, Hong Kong 999077, SAR, China
| | - Tangtang Zhang
- Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Prashant Kumar
- Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
- Institute for Sustainability, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Antonio Plaza
- Hyperspectral Computing Laboratory, University of Extremadura, 10003 Caceres, Spain
| | - Jocelyn Chanussot
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Jiabao Zhang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiancheng Shi
- National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Lizhe Wang
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
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Yordanov M, d'Andrimont R, Martinez-Sanchez L, Lemoine G, Fasbender D, van der Velde M. Crop Identification Using Deep Learning on LUCAS Crop Cover Photos. SENSORS (BASEL, SWITZERLAND) 2023; 23:6298. [PMID: 37514593 PMCID: PMC10383911 DOI: 10.3390/s23146298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023]
Abstract
Massive and high-quality in situ data are essential for Earth-observation-based agricultural monitoring. However, field surveying requires considerable organizational effort and money. Using computer vision to recognize crop types on geo-tagged photos could be a game changer allowing for the provision of timely and accurate crop-specific information. This study presents the first use of the largest multi-year set of labelled close-up in situ photos systematically collected across the European Union from the Land Use Cover Area frame Survey (LUCAS). Benefiting from this unique in situ dataset, this study aims to benchmark and test computer vision models to recognize major crops on close-up photos statistically distributed spatially and through time between 2006 and 2018 in a practical agricultural policy relevant context. The methodology makes use of crop calendars from various sources to ascertain the mature stage of the crop, of an extensive paradigm for the hyper-parameterization of MobileNet from random parameter initialization, and of various techniques from information theory in order to carry out more accurate post-processing filtering on results. The work has produced a dataset of 169,460 images of mature crops for the 12 classes, out of which 15,876 were manually selected as representing a clean sample without any foreign objects or unfavorable conditions. The best-performing model achieved a macro F1 (M-F1) of 0.75 on an imbalanced test dataset of 8642 photos. Using metrics from information theory, namely the equivalence reference probability, resulted in an increase of 6%. The most unfavorable conditions for taking such images, across all crop classes, were found to be too early or late in the season. The proposed methodology shows the possibility of using minimal auxiliary data outside the images themselves in order to achieve an M-F1 of 0.82 for labelling between 12 major European crops.
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Affiliation(s)
| | | | | | - Guido Lemoine
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
| | - Dominique Fasbender
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
- Walloon Institute of Evaluation, Foresight and Statistics (IWEPS), 5001 Namur, Belgium
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Visztra GV, Frei K, Hábenczyus AA, Soóky A, Bátori Z, Laborczi A, Csikós N, Szatmári G, Szilassi P. Applicability of Point- and Polygon-Based Vegetation Monitoring Data to Identify Soil, Hydrological and Climatic Driving Forces of Biological Invasions-A Case Study of Ailanthus altissima, Elaeagnus angustifolia and Robinia pseudoacacia. PLANTS (BASEL, SWITZERLAND) 2023; 12:855. [PMID: 36840203 PMCID: PMC9965585 DOI: 10.3390/plants12040855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/07/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Invasive tree species are a significant threat to native flora. They modify the environment with their allelopathic substances and inhibit the growth of native species by shading, thus reducing diversity. The most effective way to control invasive plants is to prevent their spread which requires identifying the environmental parameters promoting it. Since there are several types of invasive plant databases available, determining which database type is the most relevant for investigating the occurrence of alien plants is of great importance. In this study, we compared the efficiency and reliability of point-based (EUROSTAT Land Use and Coverage Area Frame Survey (LUCAS)) and polygon-based (National Forestry Database (NFD)) databases using geostatistical methods in ArcGIS software. We also investigated the occurrence of three invasive tree species (Ailanthus altissima, Elaeagnus angustifolia, and Robinia pseudoacacia) and their relationships with soil, hydrological, and climatic parameters such as soil organic matter content, pH, calcium carbonate content, rooting depth, water-holding capacity, distance from the nearest surface water, groundwater depth, mean annual temperature, and mean annual precipitation with generalized linear models in R-studio software. Our results show that the invasion levels of the tree species under study are generally over-represented in the LUCAS point-based vegetation maps, and the point-based database requires a dataset with a larger number of samples to be reliable. Regarding the polygon-based database, we found that the occurrence of the invasive species is generally related to the investigated soil and hydrological and climatic factors.
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Affiliation(s)
- Georgina Veronika Visztra
- Department of Physical Geography and Geoinformatics, University of Szeged, Egyetem utca 2, H-6722 Szeged, Hungary
| | - Kata Frei
- Department of Ecology, University of Szeged, Közép fasor 52, H-6726 Szeged, Hungary
| | | | - Anna Soóky
- Department of Ecology, University of Szeged, Közép fasor 52, H-6726 Szeged, Hungary
| | - Zoltán Bátori
- Department of Ecology, University of Szeged, Közép fasor 52, H-6726 Szeged, Hungary
| | - Annamária Laborczi
- Department of Soil Mapping and Environmental Informatics, Institute for Soil Sciences, Centre for Agricultural Research, H-1022 Budapest, Hungary
| | - Nándor Csikós
- Department of Soil Mapping and Environmental Informatics, Institute for Soil Sciences, Centre for Agricultural Research, H-1022 Budapest, Hungary
| | - Gábor Szatmári
- Department of Soil Mapping and Environmental Informatics, Institute for Soil Sciences, Centre for Agricultural Research, H-1022 Budapest, Hungary
| | - Péter Szilassi
- Department of Physical Geography and Geoinformatics, University of Szeged, Egyetem utca 2, H-6722 Szeged, Hungary
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Wolf S, Mahecha MD, Sabatini FM, Wirth C, Bruelheide H, Kattge J, Moreno Martínez Á, Mora K, Kattenborn T. Citizen science plant observations encode global trait patterns. Nat Ecol Evol 2022; 6:1850-1859. [PMID: 36266458 DOI: 10.1038/s41559-022-01904-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 09/09/2022] [Indexed: 12/15/2022]
Abstract
Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportunistic citizen science data can help map plant functional traits globally. Here we show that we can map global trait patterns by complementing vascular plant observations from the global citizen science project iNaturalist with measurements from the plant trait database TRY. We evaluate these maps using sPlotOpen, a global collection of vegetation plot data. Our results show high correlations between the iNaturalist- and sPlotOpen-based maps of up to 0.69 (r) and higher correlations than to previously published trait maps. As citizen science data collections continue to grow, we can expect them to play a significant role in further improving maps of plant functional traits.
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Affiliation(s)
- Sophie Wolf
- Remote Sensing Centre for Earth System Research, Leipzig University, Leipzig, Germany.
| | - Miguel D Mahecha
- Remote Sensing Centre for Earth System Research, Leipzig University, Leipzig, Germany
- Remote Sensing Centre for Earth System Research, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Francesco Maria Sabatini
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- BIOME Lab, Department of Biological, Geological and Environmental Sciences (BiGeA), Alma Mater Studiorum University of Bologna, Bologna, Italy
- Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Christian Wirth
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Systematic Botany and Functional Biodiversity, Leipzig University, Leipzig, Germany
- Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Helge Bruelheide
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Jens Kattge
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Max Planck Institute for Biogeochemistry, Jena, Germany
| | | | - Karin Mora
- Remote Sensing Centre for Earth System Research, Leipzig University, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Teja Kattenborn
- Remote Sensing Centre for Earth System Research, Leipzig University, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
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