GEE LULC Multi-year Crop Classification A comprehensive land use and crop classification workflow that utilizes machine learning for multi-year analysis using satellite imagery from both Sentinel-2 and Landsat. This workflow implements a Random Forest classifier trained on a single year's ground data to perform land use and crop classification across two decades of satellite data. The model leverages seasonal median composites and multiple vegetation indices to achieve robust classification performance. The classification workflow requires the following inputs / configuration: - Surface reflectance data: from Sentinel-2 (2018 - 2023) and Land
El Hazdour, Ikram, Le Page, Michel, Jarlan, Lionel, Montginoul, Marielle, hanich, lahoucine
The software is referred to as assumPtIon geneRation approach for CPS (EPIcuRus). It automatically synthesizes environment assumptions for a component under analysis (i.e., conditions on the component inputs under which the component is guaranteed to satisfy its requirements). EPIcuRus combines search-based testing, machine learning and model checking. The core of EPIcuRus is a decision tree algorithm that infers environment assumptions from a test suite including test cases and their verdicts. The test cases are generated using search-based testing, and the assumptions inferred by decision trees are validated through model checking. In order
