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Marine Debris and Oil Spill (MADOS) is a marine pollution dataset based on Sentinel-2 remote sensing data, focusing on marine litter and oil spills. Other sea surface features that coexist with or have been suggested to be spectrally similar to them have also been considered. MADOS formulates a challenging semantic segmentation task using sparse annotations.
In order to download MADOS go to https://doi.org/10.5281/zenodo.10664073.
conda create -n mados python=3.8.12
conda activate mados
conda install -c conda-forge gdal==3.3.2
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu113 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11/index.html
conda install pytables==3.7.0
To evaluate MariNeXt, place the MADOS dataset under the data
folder, download the pretrained models (5 different runs) from here and place them under the marinext/trained_models
folder and then run the following:
python marinext/evaluation.py --path ./data/MADOS --model_path marinext/trained_models/1
To train MariNeXt from scratch, run the following:
python marinext/train.py --path ./data/MADOS
To stack the image patches to form multispectral images, run the following:
python utils/stack_patches.py --path ./data/MADOS
To extract the spectal signatures of MADOS dataset (after stacking) and store them in a HDF5 Table file (DataFrame-like) run the following:
python utils/spectral_extraction.py --path ./data/MADOS_nearest
Alternatively, you can download the dataset.h5
file from here.
To load the dataset.h5
, run in a python cell the following:
import pandas as pd
hdf = pd.HDFStore('./data/dataset.h5', mode = 'r')
df_train = hdf.select('Train')
df_val = hdf.select('Validation')
df_test = hdf.select('Test')
hdf.close()
This implementation is mainly based on MARIDA, SegNeXt, mmsegmentaion, Segformer and Enjoy-Hamburger.
If you find this repository useful, please consider giving a star ⭐ and citation:
Kikaki K., Kakogeorgiou I., Hoteit I., Karantzalos K. Detecting Marine Pollutants and Sea Surface Features with Deep Learning in Sentinel-2 Imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2024.