pagraf/MagicBathyNet
Quick start guide for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification using Remote Sensing imagery.
This project helps oceanographers, marine biologists, and coastal managers accurately map shallow water depths and classify seabed habitats using satellite and aerial imagery. You input remote sensing image patches (like from Sentinel-2, SPOT-6, or aerial sources), and it outputs detailed bathymetry maps and pixel-based classifications of the seabed (e.g., seagrass, rock, sand).
Use this if you need to precisely determine shallow water depths and categorize seabed types from various remote sensing images for environmental monitoring or research.
Not ideal if you are looking for tools to process sonar data or deep-water bathymetry, as this focuses specifically on shallow coastal areas and imagery.
Stars
47
Forks
7
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 02, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/pagraf/MagicBathyNet"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
satellite-image-deep-learning/techniques
Techniques for deep learning with satellite & aerial imagery
DPIRD-DMA/OmniCloudMask
OmniCloudMask is a Python library for fast, accurate cloud and cloud shadow segmentation in...
developmentseed/label-maker
Data Preparation for Satellite Machine Learning
NRCan/geo-deep-learning
Deep learning applied to georeferenced datasets
satellite-image-deep-learning/software
Software for working with satellite & aerial imagery ML datasets