Crop Detection from Satellite Imagery using Deep Learning

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