The DOI matches its Crossref record.
Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cited by
Sorted by citations
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
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 353
“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 391
“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 340
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 222
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
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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