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TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
TorchGeo is a PyTorch library providing datasets, samplers, transforms, and pre-trained models specifically designed for geospatial data, enabling researchers and developers to apply deep learning techniques to satellite and aerial imagery, and other spatial data types.
Python
Added on 2025年5月11日3,393
Stars437
ForksPython
LanguageProject Introduction
Summary
TorchGeo is an open-source Python library built on top of PyTorch, offering essential building blocks for applying deep learning to geospatial data. It simplifies the process of loading, processing, and augmenting satellite and aerial imagery, as well as other vector and raster data types.
Problem Solved
Applying deep learning to geospatial data often involves complex challenges like handling diverse data formats, managing large file sizes, implementing spatial sampling strategies, and adapting models to multi-band imagery. TorchGeo addresses these issues by providing standardized tools and datasets within the familiar PyTorch framework.
Core Features
Diverse Geospatial Datasets
Access to a growing collection of publicly available geospatial datasets, including satellite imagery, aerial photography, and derived products, formatted for easy use with PyTorch.
Advanced Data Samplers
Specialized sampling methods for large geospatial rasters, such as patch-based sampling, to efficiently train deep learning models.
Geospatial Transforms
Geospatial-specific data transformations and augmentations, including multi-spectral band handling, projection changes, and spatial cropping.
Pre-trained Models
A collection of pre-trained deep learning models adapted for geospatial tasks, facilitating transfer learning and reducing training time.
Tech Stack
Python
PyTorch
GDAL
Rasterio
Fiona
Shapely
Use Cases
TorchGeo is designed to support a wide range of applications in geospatial artificial intelligence and remote sensing analysis, including but not limited to:
Land Cover and Land Use Mapping
Details
Train models to classify different types of land cover (e.g., forest, water, urban) using satellite or aerial imagery datasets provided or easily loaded within TorchGeo.
User Value
Enables rapid development and training of accurate land cover classification models.
Object Detection in Remote Sensing Imagery
Details
Detect and delineate specific objects like buildings, roads, vehicles, or crops within high-resolution aerial or satellite images using pre-trained models and specialized transforms.
User Value
Simplifies the process of applying advanced object detection techniques to large imagery datasets.
Change Detection and Environmental Monitoring
Details
Analyze multi-temporal datasets to identify and map changes over time, such as deforestation, urban expansion, or disaster impact assessment.
User Value
Provides tools to handle and process time-series geospatial data for monitoring environmental dynamics.
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