#remote sensing

TorchGeo provides specialized resources including datasets, samplers, and models for geospatial data processing. It bridges the gap between machine learning and geospatial data, facilitating tasks such as image classification and semantic segmentation. Integrated with PyTorch, it allows seamless incorporation into workflows, while its support for pre-trained weights accommodates multispectral imaging needs. Compatibility with tools like Lightning enhances its use in reproducible and experimental workflows, positioning TorchGeo as an essential tool for geospatial deep learning.

Open-CD is an open-source toolbox for facilitating advanced change detection tasks across various models and datasets. It supports models such as FC-EF, STANet, and SNUNet, and datasets like LEVIR-CD and WHU-CD, making it adaptable for diverse applications in remote sensing. Regular updates introduce new features, enhancing its utility for developers and researchers. The toolbox provides thorough documentation and user-friendly tools for model training, testing, and inference, ensuring smooth integration into workflows.

The UniMatch framework provides innovative techniques for semi-supervised semantic segmentation across diverse scenarios such as natural, remote sensing, and medical image contexts. With an official PyTorch implementation, UniMatch delivers robust performance on standard benchmarks including Pascal VOC 2012, Cityscapes, and COCO by utilizing architectures like DeepLabv3+ with ResNet and Xception backbones. It surpasses both traditional supervised and other semi-supervised methods through optimizing confidence thresholds and output stride settings. Discover its effectiveness in remote sensing change detection and medical image segmentation.

The segment-geospatial Python package facilitates geospatial data analysis with the Segment Anything Model (SAM), reducing the need for extensive coding. This package is available on PyPI and conda-forge. It allows for downloading map tiles, segmenting GeoTIFF files, and creating interactive markers for remote sensing imagery. Users can visualize output, save data in multiple vector formats, and utilize text and box prompts. The package offers straightforward installation and optional GPU acceleration, making it a valuable resource for researchers in earth observation using deep learning.

Explore semantic segmentation with this project using the Unicom model pre-trained on 400 million images. Achieve efficient results with just four training images. Start quickly with 'train_one_gpu.py' or optimize performance with 'train_multi_gpus.py' for multi-GPU support. Follow setup guides for seamless configuration. Connect via QQ group 679897018 for discussions.