xinluo2018/Tibet-Water-2020
Monthly surface water mapping in Tibet plateau based on deep learning method and Sentinel-1 image.
This project provides detailed monthly maps of surface water across the Tibet Plateau, derived from radar satellite imagery. It takes raw Sentinel-1 radar images as input and produces precise monthly surface water extent maps. Environmental scientists, hydrologists, and climate researchers studying water resources in mountainous regions would find this useful.
No commits in the last 6 months.
Use this if you need to accurately track the monthly changes in surface water bodies across the Tibet Plateau for environmental monitoring or hydrological research.
Not ideal if you need to map surface water in a different geographical area, require daily or weekly water extent updates, or are working with optical satellite imagery.
Stars
14
Forks
3
Language
Jupyter Notebook
License
—
Category
Last pushed
Apr 15, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/xinluo2018/Tibet-Water-2020"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
DPIRD-DMA/OmniWaterMask
Python library for high-accuracy water segmentation in satellite and aerial imagery, combining...
prs-eth/Popcorn
[RSE 2024] 🍿POPCORN: High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2 🌍🛰️
xinluo2018/WatNetv2
A new WatNet version which further extends the data applicability from Sentinel-2 image to...
sei-latam/WETSAT_v2
Wetlands flooding extent and trends using SATellite data and Machine Learning v2.0
sidgan/ETCI-2021-Competition-on-Flood-Detection
Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and...