Source code for paper related to forest species mapping supported with machine learning using spectral, terrain and texture features

We observed the influence of various feature types, including values of initial bands, vegetation indices, terrain features and texture features on the performance of popular machine learning algorithms, namely Random Forest, Support Vector Machine, eXtreme Gradient Boosting and kNN

Map demonstrate classified forest map by Ridge Regression

Clone this repository

Install python packages

• matplotlib
• numpy
• seaborn
• sklearn
• pandas
• ipython
• jupyter

requirement - Docker 🐳

1. Clone repository to your local machine git clone https://github.com/mishagrol/MLForestMapping.git
2. Go to folder cd MLForestMapping
3. Run Jupyter in Docker bash run_in_docker.sh
4. Open Jupyter in browser at localhost:8890, token is SecretToken

Availdable by request - mikhail.gasanov[@]skoltech.ru

2019 year data Y.Disk

2020 year data Y.Disk

Freely available SRTM

Texture features generated by EO-learn package - https://github.com/sentinel-hub/eo-learn

Model_Train.ipynb - jupyter to conduct ML model training with satellite data, spectral, texture and terrain features

Inference.ipynb - jupyter to conduct inference of best models to recieve pixel-wise forest and UQ maps

SHAP.ipynb - jupyter to conduct SHAP-analysis of pretrained ML models

BarPlots, Maps and other visualization created with python.

To reproduce plots install python package

• SciencePlots

Distributed under the MIT license. See LICENSE for more information.