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https://github.com/dymaxionlabs/ap-latam
Detection of slums and informal settlements from satellite imagery
https://github.com/dymaxionlabs/ap-latam
keras machine-learning remote-sensing tensorflow
Last synced: about 1 month ago
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Detection of slums and informal settlements from satellite imagery
- Host: GitHub
- URL: https://github.com/dymaxionlabs/ap-latam
- Owner: dymaxionlabs
- License: bsd-3-clause
- Created: 2018-06-04T17:55:10.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-05-03T19:39:40.000Z (about 2 months ago)
- Last Synced: 2024-05-03T20:58:47.181Z (about 2 months ago)
- Topics: keras, machine-learning, remote-sensing, tensorflow
- Language: Jupyter Notebook
- Homepage: https://ap-latam.dymaxionlabs.com/
- Size: 8.49 MB
- Stars: 34
- Watchers: 7
- Forks: 24
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
Lists
- awesome-earthobservation-code - ap-latem - Detection of slums and informal settlements from satellite imagery `Python` (`Python` processing of optical imagery (non deep learning) / Case studies / Projects)
README
# AP Latam
[](https://travis-ci.org/dymaxionlabs/ap-latam)
[](https://codecov.io/gh/dymaxionlabs/ap-latam)
[](https://gitter.im/dymaxionlabs/ap-latam?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
This is the main repository of AP Latam project.
For more information on the website frontend, see the repository at
[https://github.com/dymaxionlabs/ap-latam-web](https://github.com/dymaxionlabs/ap-latam-web).
## Dependencies
* Python 3+
* GDAL
* Proj4
* libspatialindex
* Dependencies for TensorFlow with GPU support
## Install
### Quick install and usage: Docker image
If you have [Docker](https://www.docker.com/community-edition) installed on
your machine, with NVIDIA CUDA installed and configured, you can simply pull
our image and run the scripts for training and detection.
Otherwise, follow the steps in this
[tutorial](https://medium.com/google-cloud/jupyter-tensorflow-nvidia-gpu-docker-google-compute-engine-4a146f085f17)
to install Docker, CUDA and `nvidia-docker`. This has been tested on an Ubuntu
16.04 LTS instance on Google Cloud Platform.
For all scripts you will need to mount a data volume so that the scripts can
read the input rasters and vector files, and write the resulting vector file.
It is recommended that you first set an environment variable that points to the
data directory in your host machine, like this:
```
export APLATAM_DATA=$HOME/aplatam-data
```
Then, to use any of the scripts, you would have to run them using
`nvidia-docker` and mounting a volume to `$APLATAM_DATA` like this:
```
nvidia-docker run -ti -v $APLATAM_DATA:/data dymaxionlabs/ap-latam SCRIPT_TO_RUN [ARGS...]
```
where `SCRIPT_TO_RUN` is either `ap_train` or `ap_detect` and `[ARGS...]` the
command line arguments of the specified script. You can run with `--help` to
see all available options on each script.
For example, suppose you have the following files inside the `$APLATAM_DATA`
directory:
* Training rasters on `images/`
* A settlements vector file `settlements.geojson`
To prepare a dataset and train a model you would run:
```
nvidia-docker run -ti -v $APLATAM_DATA:/data dymaxionlabs/ap-latam \
ap_train /data/images /data/settlements.geojson /data/dataset
```
When using `[nvidia-]docker run` for the first time, it will pull the image
automatically for you, so it is not neccessary to do `[nvidia-]docker pull`
first.
#### `run_with_docker.sh`
You can also use `run_with_docker.sh` to do the same:
```
export APLATAM_DATA=$HOME/data/
./run_with_docker.sh ap_train /data/images /data/settlements.geojson /data/dataset
...
```
## Development
First you will need to install the following packages. On Debian-based distros
run:
```
sudo apt install libproj-dev gdal-bin build-essential libgdal-dev libspatialindex-dev python3-venv virtualenv
```
Clone the repository and run `python setup.py install` to install the package
with its dependencies. Add `--extras gpu` to install GPU dependencies
(TensorFlow for GPUs).
Run `make` to run tests and `make cov` to build a code coverage report. You can
run `make` to do both.
## Issue tracker
Please report any bugs and enhancement ideas using the GitHub issue tracker:
https://github.com/dymaxionlabs/ap-latam/issues
Feel free to also ask questions on our
[Gitter channel](https://gitter.im/dymaxionlabs/ap-latam), or by email.
## Help wanted
Any help in testing, development, documentation and other tasks is highly
appreciated and useful to the project.
For more details, see the file [CONTRIBUTING.md](CONTRIBUTING.md).
## License
Source code is released under a BSD-2 license. Please refer to
[LICENSE.md](LICENSE.md) for more information.