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You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
The main features of avanetten/yolt are: Deep Learning Frameworks, Geospatial Machine Learning.
Projects with overlapping indexed features include: mapbox/robosat — Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads,… sshuair/torchsat — TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch. torchgeo/torchgeo — TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and… albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… andersbll/deeppy — Deep learning in Python. amznlabs/amazon-dsstne — Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with…
TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch.
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and training neural networks for tasks such as semantic segmentation, object detection, and change detection. It serves as a comprehensive pipeline for remote sensing, featuring specialized dataset loaders and multispectral image preprocessing tools. The library is distinguished by a dedicated remote sensing model zoo and extensive support for transfer learning, allowing users to integrate pre-trained weights optimized for specific satellite sensors. It also includes support for sel
Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates