Grazing Detection using Deep Learning and Sentinel-2 Time Series Data
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914096348135424 |
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| author | Pirinen, Aleksis Yela, Delia Fano Chakraborty, Smita Källman, Erik |
| author_facet | Pirinen, Aleksis Yela, Delia Fano Chakraborty, Smita Källman, Erik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grazing Detection using Deep Learning and Sentinel-2 Time Series Data Pirinen, Aleksis Yela, Delia Fano Chakraborty, Smita Källman, Erik Computer Vision and Pattern Recognition 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. |
| title | Grazing Detection using Deep Learning and Sentinel-2 Time Series Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.14493 |