Grazing shapes both agricultural production and biodiversity, yet scalable monitoring of where grazing occurs remains limited. We study seasonal grazing detection from Sentinel-2 L2A time series: for each polygon-defined field boundary, April-October imagery is used for binary prediction (grazed / not grazed). We train an ensemble of CNN-LSTM models on multi-temporal reflectance features, and achieve an average F1 score of 77 percent across five validation splits, with 90 percent recall on grazed pastures. Operationally, if inspectors can visit at most 4 percent of sites annually, prioritising fields predicted by our model as non-grazed yields 17.2 times more confirmed non-grazing sites than random inspection. These results indicate that coarse-resolution, freely available satellite data can reliably steer inspection resources for conservation-aligned land-use compliance. Code and models have been made publicly available.
@article{arxiv.2510.14493,
title = {Grazing Detection using Deep Learning and Sentinel-2 Time Series Data},
author = {Aleksis Pirinen and Delia Fano Yela and Smita Chakraborty and Erik Källman},
journal= {arXiv preprint arXiv:2510.14493},
year = {2025}
}
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Code and models: https://github.com/aleksispi/pib-ml-grazing