qingtian-k / forest_change_detection

Forest Change Detection

Description

This is a source code repository for DEEP LEARNING FOR REGULAR CHANGE DETECTION IN UKRAINIAN FOREST ECOSYSTEM WITH SENTINEL-2 (Kostiantyn Isaienkov, Mykhailo Yushchuk, Vladyslav Khramtsov, Oleg Seliverstov), 2020.

• Paper (arXiv, Journal)
• Data
• Deforestation monitoring system

Repository structure info

• baseline - scripts for deforestation masks predictions with baseline models
• time-dependent - scripts for forest change segmentation with time-dependent models, including Siamese, UNet-LSTM, UNet-diff, UNet3D models

Setup

All dependencies are given in requirements.txt Main setup configuration:

• python 3.6
• pytorch==1.4.0
• catalyst==19.05
• segmentation_models==0.1.0

Tested with Ubuntu + Nvidia GTX1060 with Cuda==10.2. CPU mode also should work, but not tested.

Dataset

You can download our datasets directly from Google drive for the baseline and time-dependent models. The image tiles from Sentinel-2, which were used for our research, are listed in tiles folder.

The data include *.geojson polygons:

• baseline: 2318 polygons, 36UYA and 36UXA, 2016-2019 years;
• time-dependent: 36UYA (two sets of separated annotations, 278 and 123 polygons -- for spring and summer seasons respectively, 2019 year) and 36UXA (1404 polygons, 2017-2018 years). The files contain the following columns: tileID (ID of a tile, which was annotated), img_date (the date, at which the tile was observed), and geometry (polygons of deforestation regions).

Also, we provide the set of images and masks prepared for training segmentation models as the Kaggle dataset.

Training

Reproduce results

To reproduce the results, presented in our paper, run the pipeline (download data, prepare images, train the models), as described in README files inbaseline and time-dependent folders.

Training with new data

To train the models with the new data, you have to create train/valid/test (*.csv) files with specified location of images and masks, and make a minor changes in Dataset classes (for more information about location of these classes, see README files in baseline and time-dependent folders).

Citation

If you use our code and/or dataset for your research, please cite our paper:

K. Isaienkov, M. Yushchuk, V. Khramtsov, O. Seliverstov, Deep learning for regular change detection in Ukrainian forest ecosystem with Sentinel-2, 2020

Questions

If you have questions after reading README, please email to [e-mail].