In this AI for Earth project, we worked with the Wildlife Conservation Society Colombia (WCS Colombia) to create up-to-date land cover maps of the Orinoquía region in Colombia. This natural region encompasses a high diversity of ecosystems, from seasonally flooded savanna to rainforest. In recent years, agricultural production has expanded, causing changes in these ecosystems. It is therefore critically important to present information on land use to policy makers so that they may balance the need for agricultural development and conserving the biodiversity and ecological functions of the region.

Landscape near Sabanas, Colombia. Photo credit Wildlife Conservation Society.

Specifically, we used a land use and land cover (LULC) map that was manually produced using satellite imagery and field data from 2011-2012 to train a semantic segmentation model for 12 land cover classes. The model can be applied to composites of Landsat 8 imagery collected in subsequent years to enable ecological analysis.

This repo contains the scripts and configuration files used to train the land cover model using a median composite of Landsat 8 images from 2013-2014. We will be evaluating the result of applying the model to 2019-2020 imagery and releasing the resulting maps in the coming months.

Category color map:

Labels based on imagery in 2011-2012, mapped to the 12 land cover classes: Basemap © OpenStreetMap contributors

Model predictions on validation tiles from the 2013-2014 median composite of Landsat 8 imagery: Basemap © OpenStreetMap contributors

Model prediction on 2019-2020/04 median composite, the updated land cover map: Basemap © OpenStreetMap contributors

At the root directory of this repo, use environment.yml to create a conda virtual environment called wcs:

conda env create --file environment.yml

If you need additional packages, add them in environment.yml and update the environment:

conda env update --name wcs --file environment.yml --prune

You need to add the ai4eutils repo to the PYTHONPATH, as we make use of the geospatial module there.

We need to set up a separate conda environment for using Solaris. Instructions are available on https://github.com/CosmiQ/solaris.

We do not want to install the Solaris pip package inside the wcs environment because it requires versions of PyTorch/TensorFlow that we might not want.

There are two ways to set up Solaris:

1. To install Solaris using their published package on PyPI, first create a conda environment called solaris, which will make the next pip installation step go smoothly:

   conda env create --file environment_solaris.yml
   pip install solaris==0.2.2
   

2. If installing from source:

   Once we clone the repo, add some more dependencies to its environment.yml so that Jupyter Notebook ran from our wcs environment can use the Solaris kernel to run certain notebooks:

     - nb_conda_kernels
     - ipykernel
     - humanfriendly
   

Our fork of Solaris is here: https://github.com/yangsiyu007/solaris

Upstream: https://github.com/CosmiQ/solaris

It seems that in our existing environment, rasterio is not compatible with GDAL>=3.1, which is required for creating Cloud Optimized GeoTIFFs (COGs). We create a separate environment for GDAL to run such commands in:

conda env create --name gdalnew --channel conda-forge python gdal==3.1.2

See Satellite data terminology.

The interactive Land Cover Mapping tool; also see finetuning

https://github.com/microsoft/landcover

Collection of utilities used in this repo

https://github.com/microsoft/ai4eutils

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

This repository is licensed with the MIT license. See LICENSE.