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https://github.com/azavea/raster-vision
An open source library and framework for deep learning on satellite and aerial imagery.
https://github.com/azavea/raster-vision
classification computer-vision deep-learning geospatial machine-learning object-detection pytorch remote-sensing semantic-segmentation
Last synced: 20 days ago
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An open source library and framework for deep learning on satellite and aerial imagery.
- Host: GitHub
- URL: https://github.com/azavea/raster-vision
- Owner: azavea
- License: other
- Created: 2017-02-02T14:31:54.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-04-11T20:04:47.000Z (7 months ago)
- Last Synced: 2024-04-14T11:28:02.920Z (7 months ago)
- Topics: classification, computer-vision, deep-learning, geospatial, machine-learning, object-detection, pytorch, remote-sensing, semantic-segmentation
- Language: Python
- Homepage: https://docs.rastervision.io
- Size: 80.9 MB
- Stars: 1,988
- Watchers: 71
- Forks: 374
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
- -awesome-agriculture - Raster Vision - Deep learning for aerial/satellite imagery. (Remote Sensing and Imagery / Shapefiles)
README
[](https://pypi.org/project/rastervision/)
[](https://docs.rastervision.io/en/stable/?badge=stable)
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/azavea/raster-vision/actions/workflows/release.yml)
[](https://codecov.io/gh/azavea/raster-vision)
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](https://docs.rastervision.io/en/stable/setup/aws.html#running-on-aws-batch) as well as [AWS Sagemaker](https://docs.rastervision.io/en/stable/setup/aws.html#running-on-aws-sagemaker).
See the [documentation](https://docs.rastervision.io/en/stable/) for more details.
## Installation
*For more details, see the [Setup documentation](https://docs.rastervision.io/en/stable/setup/)*.
### Install via `pip`
You can install Raster Vision directly via `pip`.
```sh
pip install rastervision
```
### Use Pre-built Docker Image
Alternatively, you may use a Docker image. Docker images are published to [quay.io](https://quay.io/repository/azavea/raster-vision) (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*](https://docs.rastervision.io/en/stable/framework/quickstart.html) is a good entry-point into this. More advanced examples can be found on the [*Examples*](https://docs.rastervision.io/en/stable/framework/examples.html) page.
For **developers** and those looking to dive deeper or combine Raster Vision with their own code, the best starting point is [*Usage Overview*](https://docs.rastervision.io/en/stable/usage/overview.html), followed by [*Basic Concepts*](https://docs.rastervision.io/en/stable/usage/basics.html) and [*Tutorials*](https://docs.rastervision.io/en/stable/usage/tutorials/index.html).
## Contact and Support
You can ask questions and talk to developers (let us know what you're working on!) at:
* [Discussion Forum](https://github.com/azavea/raster-vision/discussions)
* [Mailing List](https://groups.google.com/forum/#!forum/raster-vision)
## Contributing
*For more information, see [Contributing](https://docs.rastervision.io/en/stable/CONTRIBUTING.html).*
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](https://docs.rastervision.io/en/stable/CONTRIBUTING.html) for instructions.
## Licenses
Raster Vision is licensed under the Apache 2 license. See license [here](./LICENSE).
3rd party licenses for all dependecies used by Raster Vision can be found [here](./THIRD_PARTY_LICENSES.txt).