Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences
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Hybrid models leveraging both deep learning and bio-physical models tend to be less data-intensive, avoid incoherent predictions, and are generally more interpretable than purely data-driven models.
“such hybrid models tend to be less data-intensive, avoid incoherent predictions, and are generally more interpretable than purely data-driven models.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [49]
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Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740 · see citing works
Self-citationCausal inference for time series
Runge, J., Gerhardus, A., Varando, G., Eyring, V., & Camps‐Valls, G. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00431-y · Cited by 296 · see citing works
“These (often heterogeneous) data have led to an increasing application of machine learning (ML) and statistical methods [3–6] for finding patterns and associations in Earth and environmental systems [7, 8].”
Artificial intelligence for modeling and understanding extreme weather and climate events
Camps‐Valls, G., Fernández‐Torres, M., Cohrs, K.-H., Höhl, A., Castelletti, A. F., Paçal, A., Robin, C. N., Martinuzzi, F., Papoutsis, I., Prapas, I., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56573-8 · Cited by 304 · see citing works
“For prediction and impact assessment, AI models are sensitive to initial conditions and may not capture long-term dependencies 91 .”
Forecast combinations: An over 50-year review
Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.11.005 · Cited by 304 · see citing works
Persello, C., Wegner, J. D., Hänsch, R., Tuia, D., Ghamisi, P., Koeva, M., & Camps‐Valls, G. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3136100 · Cited by 197 · see citing works
Wildfire Danger Prediction and Understanding With Deep Learning
Kondylatos, S., Prapas, I., Ronco, M., Papoutsis, I., Camps‐Valls, G., Piles, M., Fernández‐Torres, M., & Carvalhais, N. (2022) · Geophysical Research Letters · doi:10.1029/2022gl099368 · Cited by 174 · see citing works
“Particularly, Reichstein et al. (2019) and Camps-Valls et al. (2021) have suggested Deep Learning (DL) as a methodology well-suited to model the complex variable interactions which are ubiquitous in Earth system problems and especially wildfires.”
BackgroundRemote Sensing and Geospatial Analysis in the Big Data Era: A Survey
Dritsas, E., & Τρίγκα, Μ. (2025) · Remote Sensing · doi:10.3390/rs17030550 · Cited by 181 · see citing works
“Scalability Limitations [60–63] Overview of ML and AI integration in geospatial analysis, focusing on feature extraction and predictive modeling.”
Botifoll, M., Pinto-Huguet, I., & Arbiol, J. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00377e · Cited by 81 · see citing works
Sun, A. Y., Jiang, P., Yang, Z., Xie, Y., & Chen, X. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-26-5163-2022 · Cited by 123 · see citing works
Iterative integration of deep learning in hybrid Earth surface system modelling
Chen, M., Qian, Z., Boers, N., Jakeman, A. J., Kettner, A. J., Brandt, M., Kwan, M., Batty, M., Li, W., Zhu, R., et al. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00452-7 · Cited by 113 · see citing works
Self-Supervised Multisensor Change Detection
Saha, S., Ebel, P., & Zhu, X. X. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3109957 · Cited by 101 · see citing works
“The emergence of deep learning has seen many such problems solved that were thought to be very challenging in the past [13], [14].”
BackgroundClimateBench v1.0: A Benchmark for Data‐Driven Climate Projections
Watson‐Parris, D., Rao, Y. D., Olivié, D., Seland, Ø., Nowack, P. J., Camps‐Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., et al. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002954 · Cited by 104 · see citing works
“ANNs have recently been employed to tackle a variety of problems in earth system science (Camp-Valls et al., 2021).”
MethodOpening the Black Box: A systematic review on explainable artificial intelligence in remote sensing
Höhl, A., Obadić, I., Fernández‐Torres, M., Najjar, H., Oliveira, D. A. B., Akata, Z., Dengel, A. R., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3467001 · Cited by 103 · see citing works
Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model
Ross, A. S., Li, Z., Perezhogin, P. A., Fernandez‐Granda, C., & Zanna, L. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003258 · Cited by 80 · see citing works
Flood prediction with time series data mining: Systematic review
Hakim, D. K., Gernowo, R., & Nirwansyah, A. W. (2023) · Natural Hazards Research · doi:10.1016/j.nhres.2023.10.001 · Cited by 70 · see citing works
Sigopi, M., Shoko, C., & Dube, T. J. (2024) · Geocarto International · doi:10.1080/10106049.2024.2347935 · Cited by 52 · see citing works
“…using deep learning techniques demonstrates their effectiveness in handling large and complex multi-date satellite datasets while accounting for spectral and ground truth measurements, noise, and uncertainties (Sit et al. 2020; Camps-Valls et al. 2021; Shirmard et al. 2022; Tariq and Qin 2023).”
Ma, Z., Mei, G., & Xu, N. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10764-9 · Cited by 59 · see citing works
Chen, X., Yao, X., Zhou, Z., Liu, Y., Yao, C., & Ren, K. (2022) · Remote Sensing · doi:10.3390/rs14081848 · Cited by 51 · see citing works
Symbolic and subsymbolic GeoAI : Geospatial knowledge graphs and spatially explicit machine learning
Mai, G., Hu, Y., Gao, S., Cai, L., Martins, B., Scholz, J., Gao, J., & Janowicz, K. (2022) · Transactions in GIS · doi:10.1111/tgis.13012 · Cited by 50 · see citing works
“, 2021), land use classification (Camps-Valls et al., 2021), geographic question answering and question answering over Earth observation products (Coelho et al.”
BackgroundMethodInfluentialImproving air quality assessment using physics-inspired deep graph learning
Li, L., Wang, J., Franklin, M., Yin, Q., Wu, J., Camps‐Valls, G., Zhu, Z., Wang, C., Ge, Y., & Reichstein, M. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00475-3 · Cited by 46 · see citing works
A process-driven deep learning hydrological model for daily rainfall-runoff simulation
Li, H., Zhang, C., Chu, W. P., et al. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131434 · Cited by 45 · see citing works
Causally‐Informed Deep Learning to Improve Climate Models and Projections
Iglesias‐Suarez, F., Gentine, P., Fernández, B. S., Beucler, T., Pritchard, M. S., Runge, J., & Eyring, V. (2024) · Journal of Geophysical Research Atmospheres · doi:10.1029/2023jd039202 · Cited by 45 · see citing works
“Future work will test this approach in more challenging and realistic setups (e.g., historical simulations with varying forcings and a real topography), as well as extend this method to integrate causality with state‐of‐the‐art advances in deep learning approaches (Camps‐Valls et al., 2021).”
MethodCollaboration between artificial intelligence and Earth science communities for mutual benefit
Chen, M., Qian, Z., Boers, N., Creutzig, F., Camps‐Valls, G., Hubacek, K., Claramunt, C., Wilson, J. P., Nativi, S., Jakeman, A. J., et al. (2024) · Nature Geoscience · doi:10.1038/s41561-024-01550-x · Cited by 44 · see citing works
“Drawing on extensive discussions of the contributions of AI to ES 1–6 , in Table 1 we summarize AI’s transformative role in ES in data management — including collection, curation, and processing — as well as in modelling, prediction, and in enhancing the understanding of processes.”
Computer Vision-Based Wood Identification: A Review
Silva, J., Bordalo, R., Pissarra, J., & de Palacios, P. (2022) · Forests · doi:10.3390/f13122041 · Cited by 37 · see citing works
Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review
Ye, Q., Wang, Y., Liu, L., Guo, L., Zhang, X., Dai, L., Zhai, L., Hu, Y., Ali, N., Ji, X., et al. (2024) · Remote Sensing · doi:10.3390/rs16101709 · Cited by 36 · see citing works
Estopinan, J., Servajean, M., Bonnet, P., Munoz, F., & Joly, A. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.839327 · Cited by 33 · see citing works
Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications
Mena, F., Arenas, D., Nuske, M., & Dengel, A. R. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3361556 · Cited by 35 · see citing works
“Camps-Valls et al. [22] recognized three phases of research in the evolution of the application of NN models for EO [aligning with the research trend we observe in Figs.”
Currenti, G., Allegra, M., Cannavò, F., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-31779-2 · Cited by 33 · see citing works
Beyond Grid Data: Exploring graph neural networks for Earth observation
Zhao, S., Chen, Z., Xiong, Z., Shi, Y.-L., Saha, S., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3493972 · Cited by 36 · see citing works
Exploring Generative Artificial Intelligence Research: A Bibliometric Analysis Approach
Dwivedi, R., & Elluri, L. (2024) · IEEE Access · doi:10.1109/access.2024.3450629 · Cited by 33 · see citing works
“Generative AI tools and techniques for image processing, image synthesis, and knowledge discovery from data have found applications in earth science, remote sensing, and climate science [125], [126], [127], [128].”
BackgroundA Framework for Four-Dimensional Variational Data Assimilation Based on Machine Learning
Dong, R., Leng, H., Zhao, J., Song, J., & Liang, S. (2022) · Entropy · doi:10.3390/e24020264 · Cited by 31 · see citing works
“The ML method has achieved rich research results in the physical process simulation [17,18], parameter estimation, and DA [19].”
MethodAI perceives like a local: predicting citizen deprivation perception using satellite imagery
Abascal, Á., Vanhuysse, S. G., Grippa, T., et al. (2024) · npj Urban Sustainability · doi:10.1038/s42949-024-00156-x · Cited by 25 · see citing works
Evolution of machine learning in environmental science—A perspective
Hsieh, W. W. (2022) · Environmental Data Science · doi:10.1017/eds.2022.2 · Cited by 25 · see citing works
ADVANCING DEEP LEARNING FOR EARTH SCIENCES: FROM HYBRID MODELING TO INTERPRETABILITY
Camps‐Valls, G., Reichstein, M., Zhu, X. X., & Tuia, D. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323558 · Cited by 26 · see citing works
Chen, C., Fu, R., Ai, X., et al. (2022) · Remote Sensing · doi:10.3390/rs14236023 · Cited by 23 · see citing works
SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution
Aybar, C., Montero, D., Contreras, J., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-04214-y · Cited by 21 · see citing works
Sharma, H., Shrivastava, M., & Singh, B. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00353-y · Cited by 20 · see citing works
EarthNets: Empowering artificial intelligence for Earth observation
Xiong, Z., Zhang, F., Wang, Y., Shi, Y., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3466998 · Cited by 88 · see citing works
Hah, D., Quilty, J., & Sikorska‐Senoner, A. E. (2022) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2022.105474 · Cited by 18 · see citing works
A Light-Weighted Fusion Vision Mamba for Multimodal Remote Sensing Data Classification
He, X. Z., Han, X., Chen, Y., & Huang, L. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3598755 · Cited by 18 · see citing works
“R EMOTE sensing is a crucial technology for observing the physical properties of the Earth and its environment from space [1].”
Gong, A., Li, R., Pan, B., et al. (2023) · Remote Sensing · doi:10.3390/rs15133306 · Cited by 16 · see citing works
Algorithms applied for monitoring pelagic Sargassum
Lazcano‐Hernández, H. E., Arellano‐Verdejo, J., & Rodríguez-Martínez, R. E. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1216426 · Cited by 16 · see citing works
Liao, J., Huang, Y., Li, Z., & Niu, S. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.166472 · Cited by 16 · see citing works
Meta-learning to address diverse Earth observation problems across resolutions
Rußwurm, M., Wang, S., Kellenberger, B., Roscher, R., & Tuia, D. (2024) · Communications Earth & Environment · doi:10.1038/s43247-023-01146-0 · Cited by 15 · see citing works
Mezouary, L. E., Hadri, A., Kharrou, M. H., Fakır, Y., Elfarchouni, A., Bouchaou, L., & Chehbouni, A. (2024) · Applied Water Science · doi:10.1007/s13201-024-02162-x · Cited by 21 · see citing works
“By propagating signals through stacked nonlinear transformations, deep learning can uncover subtle geospatial relationships unattainable to classical methods (Camps-Valls et al. 2021).”
Learning extreme vegetation response to climate drivers with recurrent neural networks
Martinuzzi, F., Mahecha, M. D., Camps‐Valls, G., Montero, D., Williams, T., & Mora, K. (2024) · Nonlinear processes in geophysics · doi:10.5194/npg-31-535-2024 · Cited by 16 · see citing works
“However, employing NDVI presents drawbacks (Camps-Valls et al., 2021a).”
OpenForest: a data catalog for machine learning in forest monitoring
Ouaknine, A., Kattenborn, T., Lalibert'e, E., & Rolnick, D. (2023) · Environmental Data Science · doi:10.1017/eds.2024.53 · Cited by 33 · see citing works
“Given the complexity of these ecosystem processes, deep learning is assumed to greatly enlarge our capabilities to exploit local flux towers and globally available remote sensing data to spatially and temporally extrapolate and understand forest ecosystem process [147, 82, 247, 40].”
InfluentialEstimating carbon fluxes over North America using a physics-constrained deep learning model
Fan, B., Zhang, H. K., Li, Z. B., Xiao, J., Che, X., Liu, Z., Camps‐Valls, G., & Chen, J. M. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.06.033 · Cited by 18 · see citing works
Chaudhari, S. S., Sardar, V. S., & Ghosh, P. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01379-4 · Cited by 15 · see citing works
Predicting the spatiotemporal characteristics of atmospheric rivers: A novel data-driven approach
Meghani, S., Singh, S. H., Kumar, N., et al. (2023) · Global and Planetary Change · doi:10.1016/j.gloplacha.2023.104295 · Cited by 15 · see citing works
All in One: A Unified Network for Hyperspectral Image Fusion
Liu, Y., Cheng, X., Li, H., Luo, S., & Yang, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3414438 · Cited by 14 · see citing works
Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)
Hickmon, N. L., Varadharajan, C., Hoffman, F. M., Wainwright, H. M., & Collis, S. (2022) · doi:10.2172/1888810 · Cited by 15 · see citing works
Applications of Machine Learning Methods in Sustainable Forest Management
Espíndola, R. P., Picanço, M. M., de Andrade, L. P., & Ebecken, N. F. F. (2025) · Climate · doi:10.3390/cli13080159 · Cited by 18 · see citing works
Zhu, Q., Yang, Y., Sun, X., & Guo, M. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3143886 · Cited by 13 · see citing works
Learning Crop-Type Mapping From Regional Label Proportions in Large-Scale SAR and Optical Imagery
Rosa, L. E. C. L., Oliveira, D. A. B., & Ghamisi, P. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3321156 · Cited by 13 · see citing works
“…(DL) algorithms have proved to be effective for learning relevant nonlinear features directly from data and have become the state-of-the-art in many EO image analysis tasks, including land use and land cover classification, object detection, change detection, and domain adaptation [2], [3], [4].”
BackgroundA multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022
Xu, Q., Li, L., Wei, Z., et al. (2025) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2025.110645 · Cited by 12 · see citing works
A parsimonious methodological framework for short-term forecasting of groundwater levels
Collados‐Lara, A., Pulido‐Velazquez, D., Ruiz, L., et al. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.163328 · Cited by 12 · see citing works
Self‐Supervised Classification of Weather Systems Based on Spatiotemporal Contrastive Learning
Wang, L., Li, Q., & Lv, Q. (2022) · Geophysical Research Letters · doi:10.1029/2022gl099131 · Cited by 12 · see citing works
Lunga, D., Hu, Y., Newsam, S. D., Gao, S., Martins, B., Yang, H. L., & Deng, X. (2021) · SIGSPATIAL Special · doi:10.1145/3578484.3578491 · Cited by 11 · see citing works
“[4] provide a comprehensive review of remote sensing, climate science, and geosciences' open research problems that have been impacted by deep learning advances.”
BackgroundDeep Learning for Flash Drought Detection: A Case Study in Northeastern Brazil
Barbosa, H. A., Buriti, C., & Kumar, T. V. L. (2024) · Atmosphere · doi:10.3390/atmos15070761 · Cited by 11 · see citing works
Zhu, S., Xu, J., Zeng, J., He, P., Wang, Y., Bao, S., Ma, J., & Shi, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3439333 · Cited by 11 · see citing works
“In this case, the utilization of computer vision-based NNs is limited in fully exploiting their capabilities when applied to a small tabular dataset [25], [29].”
Jougla, R., & Leconte, R. (2022) · Water · doi:10.3390/w14040552 · Cited by 11 · see citing works
Bindas, T., Tsai, W., Liu, J., et al. (2022) · doi:10.1002/essoar.10512512.1 · Cited by 11 · see citing works
Applying Machine Learning in Numerical Weather and Climate Modeling Systems
Krasnopolsky, V. M. (2024) · Climate · doi:10.3390/cli12060078 · Cited by 9 · see citing works
A random forest approach to quality-checking automatic snow-depth sensor measurements
Blandini, G., Avanzi, F., Gabellani, S., Ponziani, D., Stevenin, H., Ratto, S. M., Ferraris, L., & Viglione, A. (2023) · The Cryosphere · doi:10.5194/tc-17-5317-2023 · Cited by 9 · see citing works
“In recent years, deep learning has proven successful in dealing with many complex tasks (Camps-Valls et al., 2021).”
BackgroundJi, C., Fincke, T., Benson, V., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-04447-5 · Cited by 9 · see citing works
Identifying Climate Patterns Using Clustering Autoencoder Techniques
Kurihana, T., Mastilovic, I., Wang, L., et al. (2024) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-23-0035.1 · Cited by 8 · see citing works
“For climate science applications, DNNs have been increasingly used to identify hidden patterns in climate studies from the large quantities of climate simulation datasets (Camps-Valls et al. 2021; Watson-Parris et al. 2022).”
MethodCrafting the Future: Machine learning for ocean forecasting
Heimbach, P., O’Donncha, F., Smith, T. A., García-Valdecasas, J. M., Arnaud, A., & Wan, L. (2025) · State of the Planet · doi:10.5194/sp-5-opsr-22-2025 · Cited by 8 · see citing works
Hidalgo, J. J. P., & Centeno, J. A. S. (2022) · Journal of Applied Security Research · doi:10.1080/19361610.2022.2111184 · Cited by 8 · see citing works
PARS - Path recycling and sorting for efficient cloud tomography
Czerninski, I., & Schechner, Y. Y. (2023) · Intelligent Computing · doi:10.34133/icomputing.0007 · Cited by 8 · see citing works
“Thus, advancing climate research gains interest in the computer vision community [1–5] as well as other communities that use computer vision, such as remote sensing [6–12] and geophysics [6,13–16].”
Probabilistic machine learning for noisy labels in Earth observation
Kondylatos, S., Bountos, N. I., Prapas, I., Zavras, A., Camps‐Valls, G., & Papoutsis, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-19781-2 · Cited by 8 · see citing works
“Supervised DL models, trained on annotated datasets from these sources, have demonstrated remarkable performance in addressing several EO and geoscientific problems [8], [9], showcasing great results across different applications [10], [11].”
Multioutput Feature Selection for Emulation and Sensitivity Analysis
Vicent, J., Martino, L., Verrelst, J., et al. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3358231 · Cited by 7 · see citing works
“Examples of intrinsic methods are automatic relevance determination (ARD) applied to Gaussian processes (GPs) [13], [14], neural networks [15], and random forests [16].”
MethodEarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography
Berton, G., Goletto, G., Trivigno, G., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00430 · Cited by 7 · see citing works
Deep Learning With Noisy Labels for Spatiotemporal Drought Detection
Cortés-Andrés, J., Fernández‐Torres, M., & Camps‐Valls, G. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3504340 · Cited by 7 · see citing works
Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover
Rußwurm, M., Wang, S., & Tuia, D. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9884691 · Cited by 7 · see citing works
“The question of how to transfer any existing prior knowledge to new unseen tasks in machine learning remains an active field of research [1] and a highly relevant question in Earth observation [2, 3].”
BackgroundZhu, Y., & Zhang, R. (2023) · Atmospheric and Oceanic Science Letters · doi:10.1016/j.aosl.2023.100351 · Cited by 7 · see citing works
Haghighi, A. R., Salehi, H., Banikhedmat, A., Gharechelou, S., Mirabbasi, R., Pham, Q. B., & Haghighi, A. T. (2025) · Modeling Earth Systems and Environment · doi:10.1007/s40808-025-02665-9 · Cited by 7 · see citing works
“They are also quicker and work well for tracking water in real time (Camps-Valls et al. 2021 ; Ley et al. 2023).”
Jamaat, A., Song, Y., Rahmani, F., et al. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.134137 · Cited by 5 · see citing works
The AIDE Toolbox: Artificial intelligence for disentangling extreme events [Software and Data Sets]
Gonzalez-Calabuig, M., Cortés-Andrés, J., Williams, T., et al. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3382544 · Cited by 5 · see citing works
“DL has pushed the boundaries of anomaly detection, demonstrating remarkable abilities in extracting non-linear interactions from high-dimensional, temporal, spatial, and graph data to detect, forecast, and characterize anomalies throughout the Earth system [5], [6], [7], [8], [9], [10].”
Zou, H., Chen, J., Shao, C., et al. (2022) · Land · doi:10.3390/land11010087 · Cited by 5 · see citing works
Emulation of Forward Modeled Top-of-Atmosphere MODIS-Based Spectral Channels Using Machine Learning
Gonzalez, J., Dipu, S., Sourdeval, O., et al. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3507692 · Cited by 6 · see citing works
“Its growing influence in Earth sciences is evident from a comprehensive literature review by Virts et al., [16] and Camps-Valls et al., [17], highlighting its widespread application over the past decade.”
Temporal and geographic extrapolation of soil moisture using machine learning algorithms
Chrysanthopoulos, E., & Kallioras, A. (2025) · CATENA · doi:10.1016/j.catena.2025.109156 · Cited by 5 · see citing works
Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes
Chung, J., Manga, M., Kneafsey, T. J., Mukerji, T., & Hu, M. (2025) · Communications Earth & Environment · doi:10.1038/s43247-025-02644-z · Cited by 6 · see citing works
“Data-driven approaches—particularly machine learning—have emerged as powerful complements or alternatives to traditional frameworks including both physics-based and statistical approaches, in a range of geoscientific applications [1,5,8,11,24,40,45,46,52,55,70,73,75].”
ClimateBench: A benchmark dataset for data-driven climate projectionsPreprint
Watson‐Parris, D., Rao, Y. D., Olivié, D., Seland, Ø., Nowack, P. J., Camps‐Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., et al. (2022) · doi:10.1002/essoar.10509765.2 · Cited by 5 · see citing works
Bindas, T., Tsai, W., Liu, J., et al. (2023) · doi:10.22541/essoar.168500246.67971832/v1 · Cited by 5 · see citing works
Monitoring earth’s glacial lakes from space with machine learning
Tom, M., Odermatt, D., David, C. H., Cerbelaud, A., Wade, J., & Frey, H. (2025) · Science of Remote Sensing · doi:10.1016/j.srs.2025.100277 · Cited by 6 · see citing works
Mena, F., Ienco, D., Dantas, C. F., Interdonato, R., & Dengel, A. R. (2025) · Machine Learning · doi:10.1007/s10994-025-06903-0 · Cited by 4 · see citing works
Arsenoaia, V. N., Denis Constantin, T. O. P. A., Rațu, R. N., et al. (2026) · Agronomy · doi:10.3390/agronomy16050564 · Cited by 4 · see citing works
“While these practices are not yet widespread in agronomic UAV research, they represent a clear pathway towards more reliable evidence and faster translation into practice [81].”
Hidalgo, J. J. P., & Silva-Centeno, J. A. (2022) · Revista de teledetección: Revista de la Asociación Española de Teledetección · doi:10.4995/raet.2022.15710 · Cited by 4 · see citing works
“…estos obstáculos diversos investigadores han realizado estudios relacionados con la genera-ción de conjuntos de datos de imágenes satelitales y fotografías aéreas, con la finalidad de contribuir con el desarrollo de aplicaciones específicas de GeoAI (Schmitt et al., 2021; Camps-Valls et al., 2021).”
BackgroundHerzfeld, U. C., Hessburg, L. J., Trantow, T. M., et al. (2024) · Remote Sensing · doi:10.3390/rs16111854 · Cited by 4 · see citing works
Zheng, Z., Fiore, A. M., Westervelt, D. M., Milly, G. P., Goldsmith, J., Karambelas, A., Curci, G., Randles, C. A., Paiva, A., Wang, C., et al. (2022) · doi:10.31223/x51d0v · Cited by 4 · see citing works
Dynamics of Masked Image Modeling in Hyperspectral Image Classification
Ma, C., Li, H., Jiang, J., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3558432 · Cited by 3 · see citing works
“These advancements have profoundly influenced a wide range of downstream tasks in RS, including but not limited to land cover classification [2], [3], object detection [4], [5], environmental mapping [6], [7], climate analysis [8], etc.”
FDA-Recon: Faithful and Data-Adaptive Reconstruction for Limited-Angle CBCT
Liu, Y., Wang, Y., Zhang, Y., et al. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3679175 · Cited by 3 · see citing works
“UDA has gained widespread application in the field of natural language processing [28], time-series prediction [29], climate science [30], as well as computer vision [31].”
An Optimized Precision in Tea Leaf Disease Identification using Faster R-CNN Model
Deebika, R., Gopinath, V., Hemalatha, S., et al. (2025) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs65191.2025.10985625 · Cited by 3 · see citing works
“Besides, it showed some promise in reducing detection time by 11.8%, thus ensuring real-time efficiency in monitoring tea bud infection [17].”
InfluentialChappuis, C., Walt, E., Mendez, V., Lobry, S., Saux, B. L., & Tuia, D. (2025) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2025.3564401 · Cited by 7 · see citing works
“Facilitating the extraction of information that is useful for applications in environmental monitoring, agriculture, or urban planning could increase the benefits derived from the abundance of data sensed [1], [2].”
Deo, R., Webster, J. M., Salles, T. B., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3341156 · Cited by 3 · see citing works
“MLP forms the basis of deep learning models that are prominent for various applications in health, Earth sciences, and multimedia applications [60], [61], [62], [63].”
BackgroundPiekarczyk, M., & Hachaj, T. (2024) · Sensors · doi:10.3390/s24061835 · Cited by 3 · see citing works
“…economy and healthcare (fraud detection) [20–25], industry (fault diagnosis, damage detection) [26–31], medicine (medical diagnosis, disease out-break detection) [32–36], earth sciences (event detection) [37–40], bioinformatics [36,41–43], genetics [44,45], physics [46–50] or astronomy [51–56].”
GlobalBuildingMap — Unveiling the mystery of global buildings
Zhu, X. X., Li, Q., Shi, Y., Wang, Y., Stewart, A. J., Prexl, J., & Zhang, F. (2026) · Scientific Data · doi:10.1038/s41597-026-06578-9 · Cited by 3 · see citing works
“Since the distribution of the pixel value is primarily heavily tailed, the clipping range was set from 0 to Q 3 + 1.5 ⋅ IQR in our radiometric calibration, meaning only extremely large values were clipped.”
InfluentialSemantic Segmentation of Coffee Plantations from Sentinel-2 Time Series
Pišl, J., Lenczner, G., Tuia, D., & de Morsier, F. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10642247 · Cited by 2 · see citing works
“Deep learning-based approaches [3, 8] have also been proposed.”
BackgroundMena, F., Ienco, D., Dantas, C. F., Interdonato, R., & Dengel, A. R. (2025) · IEEE Access · doi:10.1109/access.2025.3568706 · Cited by 4 · see citing works
“In the EO domain, there has been a recent increase in methods using multi-sensor data [1], [3].”
- Venue
- Wiley eBooks
- Publisher
- Wiley
- Published
- 2021-08-20
- OpenAlex
- W4243053434
- Cited by
- 175
- References
- 0
- Citations by year
- 2026: 26 · 2025: 44 · 2024: 40 · 2023: 22 · 2022: 39 · 2021: 3 · 2020: 1
- FWCI
- 36.17
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Wiley eBooks (publishedVersion)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- deep learning, earth sciences, geoscience applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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