Segment Geospatial 是一个 Python 工具包,用于使用 Segment Anything Model (SAM) 隔离遥感影像中的地理特征。它作为一个遥感图像处理器,将地图瓦片转换为地理参考格式,从而从卫星数据中生成分割掩码。
opengeos/segment-geospatial 的主要功能包括:Geospatial Imagery Segmentation, SAM-Based Implementations, Automatic Mask Generators, Raster Export, Geospatial Adaptations, Prompt-Based Semantic Segmentations, Remote Sensing Machine Learning, Prompt-Based Segmentations。
opengeos/segment-geospatial 的开源替代品包括: torchgeo/torchgeo — TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and… chaoningzhang/mobilesam — MobileSAM is a lightweight image segmenter and promptable vision model designed for fast object isolation on… ant-research/magicquill — MagicQuill is a suite of interactive tools for image segmentation, diffusion-based editing, layered composition, and… bowang-lab/medsam — MedSAM is a deep learning framework designed for automating the segmentation of anatomical structures in 2D and 3D… opengeos/leafmap — Leafmap is a Python geospatial visualization library designed for creating interactive maps and performing geospatial… mapbox/robosat — Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads,…
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
MobileSAM is a lightweight image segmenter and promptable vision model designed for fast object isolation on resource-constrained hardware. It functions as an automatic image masking tool capable of detecting and isolating distinct objects across an entire image without manual input. The system enables prompt-based object masking using coordinate points or bounding boxes to generate precise masks. It also supports all-object image segmentation through object-aware prompt sampling to identify every distinct object in a scene. To facilitate mobile and edge deployment, the model is compatible w
MagicQuill is a suite of interactive tools for image segmentation, diffusion-based editing, layered composition, and prompt-guided visual synthesis. It functions as a diffusion model image editor and a layered visual composition tool, enabling the addition, removal, and recoloring of image elements through a combination of sketches and text prompts. The system features a prompt-guided image generator that predicts editing instructions by analyzing user drawings to automatically populate text prompts. It allows for visual style control by swapping generative model weights to shift outputs betw
MedSAM is a deep learning framework designed for automating the segmentation of anatomical structures in 2D and 3D medical imagery. It provides specialized tools for fine-tuning pretrained segmentation weights on custom medical datasets and evaluating the accuracy of those predictions against ground truth labels. The project focuses on adapting the Segment Anything Model architecture for medical use, enabling the isolation of specific anatomical structures through prompt-guided methods such as bounding boxes and point prompts. The system covers a full medical AI workflow, including data engi