techniques and Satellite_Imagery_Analysis
The first is a comprehensive techniques reference repository while the second is a specific application implementation, making them complements—one teaches methodologies for satellite ML while the other demonstrates practical end-to-end analysis using those approaches.
About techniques
satellite-image-deep-learning/techniques
Techniques for deep learning with satellite & aerial imagery
Comprehensive reference covering 17+ specialized task categories—from cloud removal and change detection to SAR processing and foundational models—with curated links to implementations and research across PyTorch, TensorFlow, and domain-specific architectures. Addresses satellite imagery's unique challenges like high-resolution gigapixel processing, multi-temporal analysis, and sparse labeled data through techniques including self-supervised learning, few-shot approaches, and vision-language models. Integrates with standard remote sensing datasets (Sentinel, EuroSAT, UC Merced) and modern frameworks from foundational models to explainable AI.
About Satellite_Imagery_Analysis
syamkakarla98/Satellite_Imagery_Analysis
Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.
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