{"data":{"full_name":"amberwalker-ds/u-net_semantic_segmentation","name":"u-net_semantic_segmentation","description":"This project demonstrates the use of a U-Net neural network for segmenting building footprints from aerial images. It explores data preprocessing, model architecture, training, and evaluation while achieving high performance (94.7% accuracy and a Dice score of 76.7%). Note: A GPU is required to run the model.","stars":4.0,"forks":1.0,"language":"Jupyter Notebook","license":"MIT","archived":0.0,"subcategory":"segment-anything-applications","last_pushed_at":"2024-11-19T11:06:07+00:00","pypi_package":null,"npm_package":null,"downloads_monthly":0.0,"dependency_count":0.0,"commits_30d":null,"reverse_dep_count":0.0,"maintenance_score":0.0,"adoption_score":3.0,"maturity_score":16.0,"community_score":12.0,"quality_score":31.0,"quality_tier":"emerging","risk_flags":"['stale_6m', 'no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-12T08:55:37.192998+00:00"}}