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Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences
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“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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Items 101–171 of 171
Integrating Deep Learning and IoT for Enhanced Monitoring and Sustainable Mining Practices
Rahman, M. N., & Lo, K. (2025) · Springer eBooks · doi:10.1007/978-3-031-84583-3_5 · Cited by 2
Feng, D., Tan, Z., Lin, Z., Xu, D., Yu, C., & He, Q. (2025) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2025jh000758 · Cited by 4
Towards Modeling and Predicting the Resilience of Ecosystems
Sousa, T. (2023) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1109/models-c59198.2023.00042 · Cited by 2
Reddy, K. V. V. K., & Vimal, V. R. (2024) · doi:10.1109/icait61638.2024.10690378 · Cited by 2
“Widely used in such fields as environment control, traffic observation, and city planning, this form of analysis is used in a multitude of applications [1].”
BackgroundMulti-sensor deep learning for glacier mapping
Diaconu, C., Heidler, K., Bamber, J. L., & Zekollari, H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-326484-9.00024-5 · Cited by 6
Higher Emissions Scenarios Increase Wildland–Urban Interface Fire Hazard in China
Gong, D. (2025) · Sustainability · doi:10.3390/su17167409 · Cited by 3
On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?
Mena, F., Arenas, D., Miranda, M., & Dengel, A. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11242446 · Cited by 2
“The diversity and availability of numerous data sources has allowed researchers to build more complex and effective models for different applications [1, 2].”
Huynh, N. N. T., Garambois, P., Renard, B., Colléoni, F., Monnier, J., & Roux, H. (2024) · doi:10.22541/au.170709054.44271526/v1 · Cited by 3
Huynh, N. N. T., Garambois, P., Renard, B., Colléoni, F., Monnier, J., & Roux, H. (2024) · doi:10.22541/au.170709054.44271526/v2 · Cited by 4
Soil Moisture Retrieval Using in Situ and Data Simulation Regularized Deep Learning Models
Tsagkatakis, G., Moghaddam, M., & Tsakalides, P. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10640561 · Cited by 1
“Recently, a new paradigm in remote sensing observation analysis considers data-driven approaches based on machine learning models for the retrieval of various geophysical characteristics, ranging from land cover and surface temperature to water quality, among others [12].”
BackgroundAutomated Mapping of Braided Palaeochannels From Optical Images With Deep Learning Methods
Vanzani, F., Carbonneau, P., & Fontana, A. (2025) · Journal of Geophysical Research Earth Surface · doi:10.1029/2024jf008051 · Cited by 3
“…(Kussul et al., 2017), urban scenes segmentation (Cordts et al., 2016), crop studies (Jha et al., 2019), sedimentary geology (Zhang, S. et al., 2020), landslides detection and monitoring (Huang et al., 2020), and many more applications (e.g., Camps‐Valls et al., 2021; Ma et al., 2019).”
DACSA: deformable average channel and spatial attention model for wildfire prediction and drivers
Yin, K., Shu, L., Cheng, P., Wang, M., & Huang, Y. J. (2025) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-025-03088-9 · Cited by 1
“Recent studies have found that this problem can be addressed using both classical machine learning (Jain et al. 2020 ) and emerging deep learning methods (Camps-Valls et al. 2021 ; Gao and Cheng 2021 ).”
Loganathan, E., Naveen, S., Yoganathan, A. P., et al. (2025) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds65426.2025.11166962 · Cited by 1
“GRU produces a better mean F1-score value of 0.86 than that of SVM at 0.75 while predicting[17]. the critical climatic variables, i.e., temperature, precipitation, and carbon emissions.”
InfluentialLamri, M., Sahour, A., Boumehrez, F., et al. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-1113-6_13 · Cited by 1
Altuwaijri, H. A., Kafy, A. ‐. A., & Rahman, M. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-025-05954-6 · Cited by 1
On the Generalization of Representation Uncertainty in Earth Observation
Kondylatos, S., Bountos, N. I., Michail, D., Zhu, X. X., Camps‐Valls, G., & Papoutsis, I. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00618 · Cited by 4
Sivakumar, P., Raj, B. U. N., Jyotsna, C., et al. (2026) · doi:10.1109/aiei69164.2026.11497211 · Cited by 1
Verrelst, J., & Reyes-Muñoz, P. (2026) · Remote Sensing · doi:10.3390/rs18142371 · Cited by 1
“The resulting models encode statistical relationships that approximate the observation–process–response chain, but without explicitly representing the underlying mechanisms or enforcing dynamical consistency (e.g., [31,106]).”
Supporting the Common Agricultural Policy with Sentinel-2 data and deep recurrent networks
Campos‐Taberner, M., Garcı́a-Haro, F. J., Martínez, B., Sánchez-Ruíz, S., & Navarro, M. A. G. (2021) · SPIE Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology · doi:10.1117/12.2598320 · Cited by 1
Deep learning modeling framework for multi-resolution streamflow generation
do Carmo, F. C. P., John, J., Sushama, L., et al. (2024) · Journal of Water and Climate Change · doi:10.2166/wcc.2024.706 · Cited by 1
Prado, J. L., Montariol, S., Castillo-Navarro, J., Tuia, D., & Bosselut, A. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10641080 · Cited by 1
“Implementing Artificial Intelligence (AI) methods in Remote Sensing (RS) has the potential to enhance performance and enable new applications for various downstream tasks in environmental and geo-sciences, such as damage assessment, agricultural yield prediction, and land cover monitoring [1].”
BackgroundVojteková, J., Janizadeh, S., Vojtek, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-82290-1 · Cited by 1
A Multimodal Machine Learning Fused Global 0.1° Daily Evapotranspiration Dataset from 1950-2022Preprint
Xu, Q., Li, L., Wei, Z., Lee, X., & Dai, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4797287 · Cited by 1
Feng, D., Beck, H. E., Lawson, K. E., et al. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-cc1
Shen, C. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-ac1
Shen, C. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-ac2
Shen, C. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-ac3
Feng, D., Beck, H. E., Lawson, K. E., et al. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-rc1
Feng, D., Beck, H. E., Lawson, K. E., et al. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-rc2
Shen, C. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-ac4
Shen, C. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-ac5
Feng, D., Beck, H. E., Lawson, K. E., et al. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-rc3
Shen, C. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-ac7
Shen, C. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-ac6
Hu, Y., Jiang, Y., Yao, H., Chen, Y., Wu, X., & Li, X. (2023) · Research Square · doi:10.2139/ssrn.4620903
Syam, N. S. S., Sunil, A., Pichuka, S., & MANDAL, A. (2024) · Meteorologiya i Gidrologiya · doi:10.52002/0130-2906-2024-1-86-97
Assessing the Causal Impact of Humanitarian Aid on Food Security
Cerdà-Bautista, J., Tárraga, J. M., Sitokonstantinou, V., & Camps‐Valls, G. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10642230 · Cited by 2
“Since 2014, DL applied to Earth Observation has grown exponentially, triggered by the extensive and highly available data sources, and the methodological advancements in DL [31].”
BackgroundNeural Network Downscaling to Obtain Local Precipitation Scenarios in the Italian Alps: A Case Study
Iacomino, C., & Pasini, A. (2024) · Climate · doi:10.3390/cli12090147
An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
Miranda, M., Mena, F., & Dengel, A. R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91398-3_29
“Recently, Deep Learning (DL) models have been widely used in the Earth Obser-vation (EO) field to find optimal data-driven solutions in different applications [3].”
Enhancing Weather Forecast Accuracy Using Deep Learning Models
Kumar, N., Dash, Y., Abraham, A., et al. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-81083-1_54
A Tale from the Trenches: Applying Metamorphic and Differential Testing to Bioinformatics Software
Marsh, A. L., Cohen, M. B., & Cottingham, R. W. (2025) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst62969.2025.10988959 · Cited by 1
“For instance, machine learning (ML) brings a new age of predictive power to answer previously unanswered questions in climatology, astronomy, chemistry, and biology [8]–[11].”
Song, Y., Ai, P., Xiong, C., Li, J., & Gong, S. (2025) · Water · doi:10.3390/w17132020
Dinesh, S., & Bharathi, P. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5220-4_52
Twickler, S., Herzfeld, U. C., & Trantow, T. M. (2025) · Frontiers in Earth Science · doi:10.3389/feart.2025.1572982 · Cited by 1
Advancing cryospheric studies: a historical perspective on radio-echo soundgram analysis techniques
Awati, A., Sirvi, A., Charan Gowda, B. M., et al. (2025) · Earth Science Informatics · doi:10.1007/s12145-025-01996-6
“Follow-up works (Yari et al. 2020, 2021) explored multi-scale temporal transfer learning using synthetic radargrams, enhancing performance across time-series sequences.”
Balancing Innovation and Accountability: Threats and Challenges of AI in Remote Sensing Applications
Khan, M. N., Suhail, M., Batyrova, O., Ali, D., & Jafri, S. M. W. H. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-05548-4_21 · Cited by 1
Extending Model-Agnostic XAI Methods for Regression Tasks in Spatio-Temporal Domains
Salis, M., Sartor, G., Pellegrino, M., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05461-6_20
A Multi-modal Co-learning Model with Shared and Specific Features for Land-cover Classification
Mena, F., Ienco, D., Dantas, C. F., et al. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11243286
“Multi-modal data is intrinsic in the Earth Observation (EO) domain, where different remote sensing sensors are constantly collecting information on the Earth’s surface [1].”
Climate Modelling Challenges and Prospects in the Southern Hemisphere
Abiodun, B. J., Pinto, I., Stone, D. A., Omar, S. A., Bopape, M.-J. M., Solman, S. A., Meque, A., Maoyi, M. L., Lennard, C., Evans, J. P., et al. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009352680.015
Zhang, Y., Xue, Y., Lan, C., et al. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2600881
Dulcey, M. G. Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6278088
CGFNet: Frequency-Domain Causal Discovery and Dual-Path Spectral Filtering for Wildfire Prediction
Du, H., Su, D., Bai, L., Jiang, G., Bai, L., & Wang, W. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402223
“In the domain of forest wildfire prediction research, the co-pious amount of data available lends itself well to sophisticated deep learning (DL) approaches [3] [4], with Convolutional Neural Network (CNN)-based and Recurrent Neural Network (RNN)-based models achieving strong performance.”
Hou, J., Zhang, Y., Dou, P., et al. (2026) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2026.105218
AI-Based Anomaly Detection for Extreme Event Attribution: An Analysis of European Heatwaves
Zaninelli, P. G., Barriopedro, D., Pérez-Aracil, J., & Drouard, M. (2026) · Earth Systems and Environment · doi:10.1007/s41748-026-01122-6
A predictive approach to determining the joint conservation status of species
Estopinan, J. (2023) · theses.fr (ABES) · doi:10.70675/fc734692z3098z402ez9cb5za4e234160edb
Modelling coastal evolution with machine learning
Najar, M. A. (2023) · theses.fr (ABES) · doi:10.70675/5f260cf6zb572z4f99zb280z45190cf6712d
Hybrid GeoAI Framework for Multi-Hazard Prediction, Damage Assessment, and Resource Optimization
Dr. R. Swathi, V. Y., & Harshith, N. S. T. J. H. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-32730
Wang, X.-J., Wei, Z.-L., Fan, X.-M., et al. (2026) · Landslides · doi:10.1007/s10346-026-02756-3
Afrifa‐Yamoah, E., Awuah-Mensah, Y. K., Nuakoh, B. W., Tan, W. H., Fouedjio, F., & Arya, E. (2026) · Research Square · doi:10.21203/rs.3.rs-9340991/v1
Reliable Tool Use in Remote Sensing Agents through Case Evolution and Interactive Feedback
Zhu, M., BoyiShangguan, Zhu, Z., Chen, Q., Feng, P., & He, G. (2026) · China Aerospace Information Technology Conference (CAIT) · doi:10.1109/cait70489.2026.11553889
“The integration and joint analysis of such heterogeneous data have become essential for a wide range of applications, such as urban monitoring, disaster assessment, and maritime surveillance [1-3].”
Mann, M. L., Colson, L., Nealon, R., et al. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3708028
“RNNs and its variants allow the integration of time-series imagery, substantially improving crop type classification outcomes especially in data rich environments [8], [12].”
Green AI Paradigms for Urban Hazard Detection and Resilience
Imran, M. T., Alam, T., Nadeem, S., Antuley, U., Shahid, M. F., & Tanveer, M. H. (2026) · The Springer series in applied machine learning · doi:10.1007/978-3-032-28672-7_9
Ghetiya, R. L., Nigam, R., & Kalola, A. D. (2026) · International Journal of Remote Sensing · doi:10.1080/01431161.2026.2702069
“In this study, the Radial Basis Function (RBF) kernel was employed to model the nonlinear relationship between hyperspectral reflectance and vegetation biophysical parameters (Camps-Valls et al. 2021).”
Accuracy Versus Efficiency in Model Selection for Remote Sensing Scientific Workflows
Iseni, D. S., Bader, J., Mena, F., et al. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00157
Jörges, C., & Moody, A. (2026) · Remote Sensing · doi:10.3390/rs18152589
Keramioti, E., Hafiane, A., & Oikonomou, E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7243392 · Cited by 1
Evidence before prediction: The Joint Evidence-Aware Network for trustworthy and transferable GeoAIPreprint
Jean, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7309697
Bridging the weather and climate divide with artificial intelligence
Camps‐Valls, G., Carrassi, A., Viríssimo, F. D. M., Debeire, K., Fernández‐Torres, M., Hess, P., Heuer, H., Mankovich, N., Montero, D., Bonet, E. R., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-75787-y
“They have also enhanced climate model parameterization,downscaling,andemulation 4,5 ,effectivelyaddressingthe multi-scale, heterogeneous, and nonlinearnature of Earth ’ s systems 6 – 8 .”
Rahmaniar, W., & Faricha, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7460113
An RTE-guided mixture-of-experts framework for AMSR2 passive microwave soil moisture retrieval
Wu, Y., Mao, K., Shi, J., et al. (2026) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2026.105597
Metadata
- 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
- FWCI
- 36.17
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Wiley eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › 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
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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.
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