ID:
technologies-26
Name:
Underwater Aquatic Vegetation Mapping Tool
Technology:
Machine Learning (Logistic Regression, Random Forest, Segment Anything Model)
Type:
Classification
Data used as input:
Airborne and spaceborne images
Produced datasets (openly available):
Maps of underwater aquatic vegetation
Demo (video if available):
No
Paper (if available):
Mapping underwater aquatic vegetation using foundation models with air- and space-borne images: the case of Polyphytos Lake
Paper DOI (if available):
10.3390/rs15164001
Project ID (if available):
101004157
Project Acronym (if available):
WQeMS
Service description:
Mapping underwater aquatic vegetation using air- and space-borne images.
