README 未提及依赖版本限制;使用 Docker 需从 quay.io 拉取镜像或自行执行 docker/build 构建;云端实验需按文档单独配置 AWS Batch 或 SageMaker。

raster-vision
azaveaApache-2.0更新放缓面向卫星和航拍影像的深度学习库与框架,基于 PyTorch 提供遥感场景下的分类、目标检测和语义分割训练与推理能力。
An open source library and framework for deep learning on satellite and aerial imagery.
pip install rastervisionREADME 未说明具体显存、CPU 或 GPU 型号要求,仅说明后端使用 PyTorch。
面向卫星与航拍影像的深度学习库和低代码框架,覆盖从读数据到部署全流程
面向卫星和航拍影像的深度学习库与框架,基于 PyTorch 提供遥感场景下的分类、目标检测和语义分割训练与推理能力。
核心能力
- 内置芯片分类、目标检测、语义分割三类任务
- 基于 PyTorch 提供模型后端
- 读取地理参考数据并按地理参考格式输出预测
- 以低代码方式配置并重复执行完整训练流水线
- 支持 AWS Batch 与 SageMaker 云端跑实验
容易踩的坑
- v0.31.1Raster Vision v0.31.1最新正式版2024-08-30 · 2024-08-30 17:01 UTC
Changelog
Fixes:
- Restore compatibility with python<3.11 by only importing
typing.Selfunderif TPYE_CHECKING(#2221) - Remove
awsclias a dependency fromrastervision_aws_s3and make it a dev dependency instead (#2220) - Make
max_windowsa required argument forRandomWindowGeoDataset(#2223)
Maintenance:
- Update docker dependency: bump
jupyterlabfrom 4.1.8 to 4.2.5 (#2224)
Contributors
@keves1, @AdeelH
- Restore compatibility with python<3.11 by only importing
- v0.31.0Raster Vision v0.312024-08-07 · 2024-08-07 20:12 UTC
查看更新说明
Changelog
Full changelog: https://docs.rastervision.io/en/0.31/changelog.html#raster-vision-0-31-0
This release migrates Raster Vision to pydantic v2 (it will no longer work with v1) and makes several other improvements and fixes. There are a few breaking changes; see the migration guide.
Features:
- Add
MultiRasterSource.from_stac()constructor (#2156) - Use smarter default padding value for sliding windows (#2190)
- Use rasterio AWS session in
RasterioSourcewhen reading files on S3 (#2197) - Determine
num_channelsanddtypeinRasterSourcestatically without reading a chip (#2212)
Refactor:
Fixes:
- Fix bug in
ChipClassificationLabelSource.__getitem__()whenbboxis specified (#2193)
Maintenance:
- Update codecov action to v4 and use token (#2172)
- Type hint improvements (#2192, #2195, #2210)
- Update CLA instructions (#2196)
PyPI
pip install rastervision==0.31.0https://pypi.org/project/rastervision/0.31.0/
Docker image
docker pull quay.io/azavea/raster-vision:pytorch-0.31Contributors
@AdeelH @ZhipengXue97
- Add
- v0.30.1Raster Vision v0.30.12024-05-06 · 2024-05-06 21:18 UTC
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Changelog
Features:
- Allow passing pipeline configs as JSON files to the
rastervision runCLI cmd (#2136)- (as an alternative to the traditional method of passing a
.pyfile with aget_config()function)
- (as an alternative to the traditional method of passing a
Fixes:
- Add
rastervision_aws_sagemakerto API reference on RTD (#2117) - Let
RasterSource.get_chip_by_map_window()take allRasterSource.get_chip()args (#2136) - Make repr more concise for some configs (#2136)
- Misc config serialization fixes and improvements (#2136)
- Avoid double-wrapping model in
DistributedDataParallel(#2136) - Make
S3FileSystem.list_paths()only list direct children (#2136) - Add missing
RequestPayerarg to some s3-relatedboto3API calls (#2136) - Fix incorrect path in RV pipeline examples doc (#2138)
Maintenance:
- Allow passing pipeline configs as JSON files to the
- v0.30.0Raster Vision v0.302024-04-11 · 2024-04-11 14:09 UTC
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Changelog
- Full changelog: https://docs.rastervision.io/en/0.30/changelog.html#raster-vision-0-30
- Migration guide: https://docs.rastervision.io/en/0.30/migration/v0-21_to_v0-30.html
Highlights
- Easier installation: Raster Vision no longer requires exact versions of dependencies, which means it can more easily be installed alongside other packages.
- AWS SageMaker support: you can now run Raster Vision jobs on SageMaker via
You can even run training over multiple instances with multiple GPUs. See Running on AWS SageMaker for more details.rastervision run sagemaker ... - Distributed training support: Raster Vision Learners now support distributed training (both multi-node and multi-GPU) via PyTorch DDP. If your machine has multiple GPUs, Raster Vision will now automatically use them all during training.
- New CLI command, predict_scene, that allows greater configurability than the
predictcommand.rastervision predict_scene <model_bundle_uri> <scene_config_uri> [--predict_options_uri <predict_options_uri>] - New tutorial: Predicting with an ONNX model.
PyPI
pip install rastervision==0.30.0https://pypi.org/project/rastervision/0.30.0/
Docker image
docker pull quay.io/azavea/raster-vision:pytorch-0.30 - v0.21.3Raster Vision 0.21.32023-10-17 · 2023-10-17 20:02 UTC
查看更新说明
Changelog
Full changelog: https://docs.rastervision.io/en/0.21/changelog.html#raster-vision-0-21-3
Highlights
- Features:
- Allow reading pre-chipped datasets with non-RGB TIFF chips (#1932)
- Bug fixes:
- Normalize pixel values in the Spacenet Vegas examples (#1930)
- Account for bbox when saving predictions (#1931)
- Bug fix: ensure SS datasets always return label array with correct dtype (#1954)
- Fix bug in Visualizer when plotting temporal data w/ batch size 1 (#1958)
- Allow specifying
chip_szinStatsTransform.from_raster_sources()(#1933) - Misc. minor fixes (#1933)
- Docs:
- Update release instructions to simplify patch release process (#1934)
- Maintenance:
Pypi
pip install rastervision==0.21.3https://pypi.org/project/rastervision/0.21.3/
Notes
- The
pipinstallation is only guaranteed to work with Python 3.10.
Docker image
docker pull quay.io/azavea/raster-vision:pytorch-0.21 - Features:
用户点评
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Raster Vision is an open source Python library and framework for building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery).
It has built-in support for chip classification, object detection, and semantic segmentation with backends using PyTorch.
As a library, Raster Vision provides a full suite of utilities for dealing with all aspects of a geospatial deep learning workflow: reading geo-referenced data, training models, making predictions, and writing out predictions in geo-referenced formats.
As a low-code framework, Raster Vision allows users (who don't need to be experts in deep learning!) to quickly and repeatably configure experiments that execute a machine learning pipeline including: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment.

Raster Vision also has built-in support for running experiments in the cloud using AWS Batch as well as AWS Sagemaker.
See the documentation for more details.
Installation
For more details, see the Setup documentation.
Install via pip
You can install Raster Vision directly via pip.
pip install rastervision
Use Pre-built Docker Image
Alternatively, you may use a Docker image. Docker images are published to quay.io (see the tags tab).
We publish a new tag per merge into master, which is tagged with the first 7 characters of the commit hash. To use the latest version, pull the latest suffix, e.g. raster-vision:pytorch-latest. Git tags are also published, with the Github tag name as the Docker tag suffix.
Build Docker Image
You can also build a Docker image from scratch yourself. After cloning this repo, run docker/build, and run then the container using docker/run.
Usage Examples and Tutorials
Non-developers may find it easiest to use Raster Vision as a low-code framework where Raster Vision handles all the complexities and the user only has to configure a few parameters. The Quickstart guide is a good entry-point into this. More advanced examples can be found on the Examples page.
For developers and those looking to dive deeper or combine Raster Vision with their own code, the best starting point is Usage Overview, followed by Basic Concepts and Tutorials.
Contact and Support
You can ask questions and talk to developers (let us know what you're working on!) at:
Developing
To set up the development environment:
- For and clone the repo and navigate to it.
- Create and activate a new Python virtual environment via your environment manager of choice (
mamba,uv,pyenv, etc.). - Run
scripts/setup_dev_env.shto install all Raster Vision plugins in editable mode along with all the dependencies.
Contributing
For more information, see Contributing.
We are happy to take contributions! It is best to get in touch with the maintainers about larger features or design changes before starting the work, as it will make the process of accepting changes smoother.
Everyone who contributes code to Raster Vision will be asked to sign a Contributor License Agreement. See Contributing for instructions.
Licenses
Raster Vision is licensed under the Apache 2 license. See license here.
3rd party licenses for all dependecies used by Raster Vision can be found here.