This project is devoted to the cloud and snow semantic segmentation using multispectral satellite images, received from a multizone scanning instrument (MSU-GS) used for hydrometeorological support and installed on the Russian satellite Electro-L No. 2. As the additional information, geographical information (latitude, longitude and altitude) is used. The main difficulty of snow and cloud discrimination using Electro-L No. 2 data is the absence of SWIR channels in the range 1300-1600 nm, which are necessary for accurate snow & cloud distinction (NDSI tests). The repo includes a self-collected dataset with Electro-L No. 2 imagery, cloud masks for it from the geostationary satellites GOES-16 & Meteosat-10 and snow masks from Terra/MODIS. We also attach a trained Multi-Scale Attention Network (MANet) segmentation model weights (from SMP: https://smp.readthedocs.io/en/latest/models.html), able to do accurate segmentation of cloud ansd snow for MSU-GS data. The developed algorithm works in any season of the year during the daytime and is able to perform cloud and snow segmentation in real time mode for Electro-L No.2 and GOES-R data.
Cite: https://doi.org/10.1016/j.asr.2024.03.019
- This project has 2 folders: RES_4_KM & RES_1_KM as NN need to be fitted for each resolution from scratch
- Both RES_4_KM & RES_1_KM provide INFERENCE.ipynb, data for it, model's weights and instructions of usage in README.MD
- GOES-16 L2 products used for cloud masks were obtained from Amazon:
- Meteosat-10 L2 products used for cloud masks were obtained from Eumetsat Store:
- Terra/MODIS daily snow maps for snow masks were obtained from:
- Electro-L №2 data can be obtained from Siberian Center of the Federal State Budgetary Institution Scientific Research Center of Space Hydrometeorology "Planeta". The data is available from the author upon request and with permission from https://en.rcpod.ru/.
The INFERENCE.ipynb and other notebooks from both folders mentioned above have been run with python 3.9.7 on Windows 10 OS with NVIDIA CUDA supported (Adapt all needed packages versions accroding your Python version)
- It's recomended to use
Pytorchversion with CUDA support! To install pytorch with cuda run appropriate command in your console from here:- https://pytorch.org/get-started/locally/
- (We used this version of cudann:
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116)
- There are seperated requrements.txt for each .ipynb in every folder in this repo! Be careful!
- Just run in your .ipynb this cell:
!pip install -r requirements.txt
Work is greatly supported by Non-commercial Foundation for the Advancement of Science and Education INTELLECT
Also thanks a lot to https://github.com/skolpin & https://github.com/VasilievArtyom for a huge assist for creating this project!