Project-AgML/sentinel_SAR_NDVI
Sentinel Sar Ndvi This dataset provides real satellite imagery of agricultural fields, captured using the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical sensors from 2017 to 2024. It offers complementary radar and optical (RGB) data streams collected over natural field environments, enabling multi-modal analysis for land monitoring applications. The collection supports computer vision research requiring synchronized multi-sensor observations in real-world… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/sentinel_SAR_NDVI.
Sentinel Sar Ndvi
This dataset provides real satellite imagery of agricultural fields, captured using the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical sensors from 2017 to 2024. It offers complementary radar and optical (RGB) data streams collected over natural field environments, enabling multi-modal analysis for land monitoring applications. The collection supports computer vision research requiring synchronized multi-sensor observations in real-world agricultural settings. The dataset contains 2,200 images with no classification, segmentation, or bounding-box annotations.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{cardonamesa2024dataset,
title={Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imagery},
author={Cardona-Mesa, Ahmed Alejandro and Vásquez-Salazar, Rubén Darío and Gómez, Luis and Travieso-González, Carlos M. and Garavito-González, Andrés F. and Vásquez-Cano, Esteban and Díaz-Paz, Jean Pierre},
journal={Data in Brief},
volume={57},
pages={111160},
year={2024},
publisher={Elsevier}
}Diaz, Jean; Vasquez, Ruben; Garavito-Gonzalez, Andrés F.; Vásquez-Cano, Esteban (2024), “Dataset of Sentinel-1 SAR and Sentinel-2 NDVI Imagery”, Mendeley Data, V3, doi: 10.17632/xjcr5k4c9t.3
This dataset was reformatted from its original format to match HuggingFace standards.