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Bibliography

Curated references for Earth observation machine learning. All entries include DOIs or permanent links.

Foundational Deep Learning

  1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. DOI: 10.1038/nature14539

  2. He, K., et al. (2016). Deep Residual Learning for Image Recognition. CVPR. DOI: 10.1109/CVPR.2016.90

  3. Vaswani, A., et al. (2017). Attention Is All You Need. NeurIPS. arXiv:1706.03762

Segmentation

  1. Ronneberger, O., et al. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI. DOI: 10.1007/978-3-319-24574-4_28

  2. Chen, L.C., et al. (2018). Encoder-Decoder with Atrous Separable Convolution. ECCV. DOI: 10.1007/978-3-030-01234-2_49

Change Detection

  1. Daudt, R.C., et al. (2018). Fully Convolutional Siamese Networks for Change Detection. ICIP. DOI: 10.1109/ICIP.2018.8451652

GANs

  1. Goodfellow, I., et al. (2014). Generative Adversarial Nets. NeurIPS. arXiv:1406.2661

  2. Isola, P., et al. (2017). Image-to-Image Translation with Conditional Adversarial Networks. CVPR. DOI: 10.1109/CVPR.2017.632

  3. Zhu, J.Y., et al. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent GANs. ICCV. DOI: 10.1109/ICCV.2017.244

  4. Wang, X., et al. (2018). ESRGAN: Enhanced Super-Resolution GANs. ECCV Workshops. arXiv:1809.00219

Physics-Informed Neural Networks

  1. Raissi, M., et al. (2019). Physics-informed neural networks. Journal of Computational Physics, 378, 686-707. DOI: 10.1016/j.jcp.2018.10.045

  2. Lu, L., et al. (2021). DeepXDE: A deep learning library for solving differential equations. SIAM Review, 63(1), 208-228. DOI: 10.1137/19M1274067

Remote Sensing + ML

  1. Zhu, X.X., et al. (2017). Deep Learning in Remote Sensing. IEEE GRSM, 5(4), 8-36. DOI: 10.1109/MGRS.2017.2762307

Spectral Indices

  1. Tucker, C.J. (1979). Red and photographic infrared linear combinations. Remote Sensing of Environment, 8(2), 127-150. DOI: 10.1016/0034-4257(79)90013-0

Spatial Statistics

  1. Moran, P.A.P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 37(1/2), 17-23. DOI: 10.2307/2332142

  2. Roberts, D.R., et al. (2017). Cross-validation strategies for data with temporal, spatial structure. Ecography, 40(8), 913-929. DOI: 10.1111/ecog.02881

Datasets

  1. Helber, P., et al. (2019). EuroSAT: A Novel Dataset for Land Use Classification. IEEE JSTARS, 12(7). DOI: 10.1109/JSTARS.2019.2918242

Last updated: December 16, 2025