CropHarvest
An open-source remote sensing dataset and pipeline for agricultural land use classification, featuring 95,186 datapoints with satellite and climatology data.
What is CropHarvest?
CropHarvest is an open-source remote sensing dataset and processing pipeline specifically designed for agricultural applications. It aggregates diverse agricultural land use datasets and pairs them with satellite imagery and climatology data to create a standardized resource for training and benchmarking crop classification models. The project solves the problem of fragmented and inaccessible agricultural remote sensing data by providing a unified, ready-to-use dataset.
Researchers, data scientists, and agricultural technologists working on crop-type classification, land use mapping, and remote sensing applications. It is particularly useful for those developing machine learning models for agricultural monitoring.
Developers choose CropHarvest because it offers a large, curated dataset with integrated satellite and climatology data, along with a complete processing pipeline. Its open-source nature and benchmark support enable reproducible research and faster model development compared to assembling disparate data sources manually.
Overview
Open source remote sensing dataset with benchmarks
Use Cases
Best For
- Training machine learning models for crop vs. non-crop classification
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