Grazing Detection using Deep Learning and Sentinel-2 Time Series Data

2025-10-16

AI Summary

Pasture monitoring faces limitations in spatial coverage and low efficiency due to reliance on labor-intensive field inspections. Method: This study proposes a scalable pasture monitoring framework leveraging Sentinel-2 Level-2A time-series imagery and deep learning. We design an end-to-end CNN-LSTM model that fuses multi-temporal surface reflectance features to perform field-level binary classification (grazed vs. ungrazed). Critically, the framework substitutes costly conventional remote sensing or ground surveys with freely available, open-source satellite data, enabling automated, large-area assessment. Results: Five-fold cross-validation yields an average F1-score of 77% and a grazing-class recall of 90%. Prioritizing field inspections for plots predicted as “ungrazed” increases违规 detection rate by 17.2×, markedly improving regulatory resource allocation. The framework establishes a low-cost, highly scalable intelligent monitoring paradigm for agricultural compliance and ecological conservation.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Detecting grazing activities using satellite time series data
• Monitoring agricultural compliance through deep learning models
• Optimizing inspection resources with automated grazing detection

Innovation

• CNN-LSTM ensemble models for time series analysis
• Sentinel-2 satellite imagery for grazing detection
• Prioritizing inspections using model predictions