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.

v0.31.1发布于 2024-08-30 17:01 UTC2,245star399fork同步于 2026-11-07 07:30 UTC

怎么上手

由 README 提炼,执行前请确认
pip install rastervision
硬件要求按 README 说明

README 未说明具体显存、CPU 或 GPU 型号要求,仅说明后端使用 PyTorch。

下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

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

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。

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

最近发版

5 个版本v0.31.1
  1. 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.Self under if TPYE_CHECKING (#2221)
    • Remove awscli as a dependency from rastervision_aws_s3 and make it a dev dependency instead (#2220)
    • Make max_windows a required argument for RandomWindowGeoDataset (#2223)

    Maintenance:

    • Update docker dependency: bump jupyterlab from 4.1.8 to 4.2.5 (#2224)

    Contributors

    @keves1, @AdeelH

  2. 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 RasterioSource when reading files on S3 (#2197)
    • Determine num_channels and dtype in RasterSource statically without reading a chip (#2212)

    Refactor:

    • Migrate to pydantic v2 (#2178)
    • Compute RasterStats from transformed RasterSource (#2211)

    Fixes:

    • Fix bug in ChipClassificationLabelSource.__getitem__() when bbox is 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==0.31.0
    

    https://pypi.org/project/rastervision/0.31.0/

    Docker image

    docker pull quay.io/azavea/raster-vision:pytorch-0.31
    

    Contributors

    @AdeelH @ZhipengXue97

  3. v0.30.1Raster Vision v0.30.12024-05-06 · 2024-05-06 21:18 UTC
    查看更新说明

    Changelog

    Features:

    • Allow passing pipeline configs as JSON files to the rastervision run CLI cmd (#2136)
      • (as an alternative to the traditional method of passing a .py file with a get_config() function)

    Fixes:

    • Add rastervision_aws_sagemaker to API reference on RTD (#2117)
    • Let RasterSource.get_chip_by_map_window() take all RasterSource.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 RequestPayer arg to some s3-related boto3 API calls (#2136)
    • Fix incorrect path in RV pipeline examples doc (#2138)

    Maintenance:

    • Update docker dependencies (#2116, #2122)
    • Tweak coverage report generation (#2136)
  4. v0.30.0Raster Vision v0.302024-04-11 · 2024-04-11 14:09 UTC
    查看更新说明

    Changelog

    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
      rastervision run sagemaker ...
      
      You can even run training over multiple instances with multiple GPUs. See Running on AWS SageMaker for more details.
    • 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 predict command.
      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==0.30.0
    

    https://pypi.org/project/rastervision/0.30.0/

    Docker image

    docker pull quay.io/azavea/raster-vision:pytorch-0.30
    
  5. 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_sz in StatsTransform.from_raster_sources() (#1933)
      • Misc. minor fixes (#1933)
    • Docs:
      • Update release instructions to simplify patch release process (#1934)
    • Maintenance:
      • Bump pillow to address CVE-2023-4863 (#1952)
      • Update CI and release workflows to free up disk space before building docker image (#1953, #1959)

    Pypi

    pip install rastervision==0.21.3
    

    https://pypi.org/project/rastervision/0.21.3/

    Notes

    • The pip installation is only guaranteed to work with Python 3.10.

    Docker image

    docker pull quay.io/azavea/raster-vision:pytorch-0.21
    

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    README 原文

    未翻译

    Raster Vision Logo  

    Pypi Documentation Status License Build Status codecov

    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. Overview of Raster Vision workflow

    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.sh to 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.