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This repository offers a comprehensive overview of various deep learning techniques for analyzing satellite and aerial imagery, including architectures, models, and algorithms for tasks such as classification, segmentation, and object detection. These images pose unique challenges, such as large sizes and diverse object classes, which offer opportunities for deep learning researchers. 👉 👈 Introductionĭeep learning has transformed the way satellite and aerial images are analyzed and interpreted. Techniques for deep learning on satellite and aerial imagery. Terrain mapping, Disparity Estimation, Lidar, DEMs & NeRF 33. Self-supervised, unsupervised & contrastive learning 26. Autoencoders, dimensionality reduction, image embeddings & similarity search 20. Generative Adversarial Networks (GANs) 19. Single image super-resolution (SISR) 14.3. Multi image super-resolution (MISR) 14.2. Object detection - Oil storage tank detection 4.11. Object detection - Infrastructure & utilities 4.10. Object detection - Planes & aircraft 4.9. Object detection - Cars, vehicles & trains 4.8. Object detection - Buildings, rooftops & solar panels 4.6. Object detection enhanced by super resolution 4.4. Object detection with rotated bounding boxes 4.3. Segmentation - Buildings & rooftops 2.10. Segmentation - Fire, smoke & burn areas 2.5. Segmentation - Water, coastlines & floods 2.4. Segmentation - Vegetation, crops & crop boundaries 2.3. Segmentation - Land use & land cover 2.2.

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