Glacier Mapping From Satellite Imagery

Overview

The goal of this project is to use computer vision to automatically segment debris and ice glaciers from satellite images

Requirements

🐍 Python3.6

addict==2.2.1
Cython==0.29.15
GDAL==2.1.3
joblib==0.14.1
numpy==1.18.1
pandas==0.25.3
Pillow-SIMD==7.0.0.post3
python-dateutil==2.8.1
pytz==2019.3
PyYAML==5.3
six==1.14.0
torch==1.4.0
torchvision==0.5.0

Data Description

Raw training data are 7000x7000px 12 channel sentinel-2 tiff images from the Hindu-Kush-Himalayan region. We consider the region of Bhutan and Nepal. Shapefile labels of the glaciers are provided by ICIMOD

Pipeline

Data Preprocessing:

Slicing: We slice the 7000x7000 input tiffs into 512x512 tiles. The resulting tiles along with corresponding shapefile labels are stored. Metadata of the slices are stored in a geojson file" slicemetadata.geojson To slice, run: python3 src/slice.py
Transformation: For easy processing, we convert the input image and labels into multi-dimensional numpy .npy files.
Masking: The input shapefiles are transformed into masks. The masks are needed for use as labels. This involves transforming the label as multi-channel images with each channel representing a label class ie. 0 - Glacier, 1 debris etc To run transformation and masking: python3 src/mask.py

Data PostProcessing

Filtering: Returns the paths for pairs passing the filter criteria for a specific channel. Here we filter by the percentage of 1's in the filter channel.
Random Split: The final dataset is saved in three folders: train/ test/ dev/
Reshuffle: Shuffle the images and masks in the output directory
Generate stats: Generate statistics of the input image channels: returns a dictionary with keys for means and standard deviations accross the channels in input images.
Normalization: We normalize the final dataset based on the means and standard deviations calclualted.

Imputation: Given and input, we check for missing values (NaNs) and replace with 0

To Run all PostProcessing:

python3 -m src.post_process.py
--slice_dir=/path_to_glacier_slices/
--slice_meta=/path_to_slice_metadata.geojson

Model Training

Model: Unet To train: python3 train.py

vector data sources

Labels : ICIMOD

(2000, Nepal): Polygons older/newer than 2 years from 2000 are filtered out. Original collection contains few polygons from 1990s
(2000, Bhutan): Used as it's
(2010, Nepal): Polygons older/newer than 2 years from 2010 are filtered out. Original collection is for 1980-2010
(2010, Bhutan): Used as it's

Borders:

Natural Earth: Admin-0 details

Test Data:

Dudh Koshi sub basin (provided directly from ICIMOD)

ak3ra/glacier_mapping