![]() ![]() □ □ Introductionĭeep learning has transformed the way satellite and aerial images are analyzed and interpreted. Techniques for deep learning on satellite and aerial imagery. □ □ Introduction Techniques Classification Segmentation Segmentation - Land use & land cover Segmentation - Vegetation, crops & crop boundaries Segmentation - Water, coastlines & floods Segmentation - Fire, smoke & burn areas Segmentation - Landslides Segmentation - Glaciers Segmentation - Other environmental Segmentation - Roads Segmentation - Buildings & rooftops Segmentation - Solar panels Segmentation - Other manmade Panoptic segmentation Instance segmentation Object detection Object detection with rotated bounding boxes Object detection enhanced by super resolution Salient object detection Object detection - Buildings, rooftops & solar panels Object detection - Ships & boats Object detection - Cars, vehicles & trains Object detection - Planes & aircraft Object detection - Infrastructure & utilities Object detection - Oil storage tank detection Object detection - Animals Object tracking in videos Object counting Regression Cloud detection & removal Change detection Time series Crop classification Crop yield Wealth and economic activity Disaster response Super-resolution Single image super-resolution (SISR) Multi image super-resolution (MISR) Pansharpening Image-to-image translation Data fusion Generative Adversarial Networks (GANs) Autoencoders, dimensionality reduction, image embeddings & similarity search Image retrieval Image Captioning Visual Question Answering Mixed data learning Few-shot learning Self-supervised, unsupervised & contrastive learning Weakly & semi-supervised learning Active learning Federated learning Transformers Adversarial ML Image registration Terrain mapping, Disparity Estimation, Lidar, DEMs & NeRF Thermal Infrared SAR NDVI - vegetation index General image quality Synthetic data README.md
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