![]() ![]() 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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