Orion-AI-Lab/S4A
Sen4AgriNet: A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
This project provides a ready-to-use dataset and tools for analyzing satellite imagery from Sentinel-2 to understand what's being grown in agricultural fields. It takes raw Sentinel-2 satellite data over multiple years and countries and outputs processed images and corresponding crop labels for different parcels. Farmers, agricultural consultants, and environmental scientists can use this to monitor land use and crop health over large regions.
110 stars. No commits in the last 6 months.
Use this if you need pre-processed, high-quality Sentinel-2 data and corresponding crop labels for training machine learning models to classify and segment agricultural fields.
Not ideal if you need a user-friendly application for direct crop monitoring without engaging in deep learning model development.
Stars
110
Forks
21
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 06, 2024
Commits (30d)
0
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