kamalesh003/Land_Cover_Classification_Using_Satellite_Imagery
This project uses satellite imagery to classify different land cover types such as vegetation, water, and urban areas. It leverages machine learning techniques to automate the detection and mapping of land features.
Stars
—
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 18, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/kamalesh003/Land_Cover_Classification_Using_Satellite_Imagery"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
maja601/EuroCrops
The official repository for the EuroCrops dataset.
dida-do/eurocropsml
EuroCropsML is a ready-to-use benchmark dataset for few-shot crop type classification using...
langnico/global-canopy-height-model
This repository contains the code used in the paper: A high-resolution canopy height model of...
raoofnaushad/Land-Cover-Classification-using-Sentinel-2-Dataset
Application of deep learning on Satellite Imagery of Sentinel-2 satellite that move around the...
Orion-AI-Lab/S4A
Sen4AgriNet: A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification...