A Spatio-Temporal Deep Learning-Based Crop Classification Model for Satellite Imagery

First place solution for Crop Detection from Satellite Imagery competition organized by CV4A workshop at ICLR 2020.

Getting Started

A summarized description of the approach can be found here.

Prerequisites

Firstly, you need to have

• Ubuntu 18.04
• Python3
• 20 GB RAM
• 11 GB GPU RAM

Secondly, you need to install the challenge data and sample submission file by the following the instructions here.

Thirdly, you need to install the dependencies by running:

pip3 install -r requirements.txt

Running

Dataset Preparation

python3 prepare_data.py --data_path ...

This step generates patches around each crop field in the data and saves all of them in a numpy matrix along side their ground truth labels.

Generating a Submission File

python3 main.py --data_path ...

This step trains an ensemble of 10 instances of the same DL model on different train/valid splits then generate a submission file with results on test set.

All augmentations are used except for Mixup augmentation. In order to use it, run

python3 main.py --data_path ... --mixup_augmentation True

However it uses a lot of RAM (~50 GB) so I wouldn't recommend using it.