pavelsyomin/crop-classification

Master's Thesis on Crop Classification with Remote Sensing

Application of Machine Learning for Early Crop Classification Using Satellite Imagery

My Master's thesis on HSE MDS program.

Based on this paper:

Marc Rußwurm, Nicolas Courty, Remi Emonet, Sebastien Lefévre, Devis Tuia, and Romain Tavenard (2023). End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping. ISPRS Journal of Photogrammetry and Remote Sensing. 196. 445-456. https://doi.org/10.1016/j.isprsjprs.2022.12.016

Dependencies

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Getting Started

Training, models comparison, inference, robustness check are in Crop Clasification notebook.

Slides

Thesis in PDF

CLI (may not work for some datasets)

Look at the code of train.py to see all the options. Russian dataset is private.

Monitor training visally (optional)

Start visdom server for visual training progress.

❯ visdom
Checking for scripts.
It's Alive!
INFO:root:Application Started
You can navigate to http://localhost:8097

Navigate to http://localhost:8097/ in the browser of your choice.

Start training loop

To start the training loop, run the following command.

❯ python train.py
Setting up a new session...
epoch 100: trainloss 1.70, testloss 1.97, accuracy 0.87, earliness 0.48. classification loss 7.43, earliness reward 3.48: 100%|███| 100/100 [06:34<00:00,  3.95s/it]

The BavarianCrops dataset is automatically downloaded. Additional options (e.g., --alpha, --epsilon, --batchsize) are available with python train.py --help.

Other examples

python train.py --dataroot /data/sustainbench --dataset ghana
python train.py --dataroot /data/sustainbench --dataset southsudan --epochs 500

Docker

It is also possible to install dependencies in a docker environment

docker build -t elects .

and run the training script

docker run elects python train.py