CROPMAP is an automated national cloud engine that converts multi-temporal Earth observations into real-time crop layers. By combining public governance and data science, it replaces outdated survey records with dynamic agronomic mapping to support public policy. Introduction Agricultural ministries require frequent, wall-to-wall spatial datasets to guide market regulations and resource allocation. Traditional ground surveys and self-declarations are expensive, slow, and leave wide geographic gaps over time. Administrative registries also frequently represent land ownership limits rather than true agronomic boundaries. CROPMAP automatically updates these layers across 435,000 gross hectares of fragmented land. Methodologies The framework processes integrated Sentinel-1 SAR sequences, Sentinel-2 multispectral imagery, and high-resolution 0.2m aerial photography via two main deep learning branches: Delineation (U-Net): A polygon-conditioned semantic segmentation layout splits mixed administrative registry blocks into true management sub-fields, expanding boundary correctness from 75.16% to 86.37%. Classification: A multi-tier pipeline isolates main land uses (94% accuracy) and winter wheat zones (95% accuracy). Sub-pixel canopy variations are resolved by feeding satellite arrays and visual patches through a combined Swin Transformer attention network. Output An operational dashboard providing interactive tracking of shifting crop rotation patterns, land-use intensity, and active vegetation growth cycles. Updated national inventory maps are automatically outputted within 2 to 3 days of new raw satellite imagery availability. Field Crops: Secures an 81.15% multi-class accuracy, with top classification performance for Cotton (98.17%), Watermelon (97.5%), and Peanut (93.33%). Orchards: robust cross-year F1-scores on complex targets like Banana (0.988), Date palm (0.978), Wine Vineyard (0.963), and Olive lines (0.914). Impact The system simultaneously resolves sub-pixel perennial structures across 25 distinct orchard types and 17 dynamic open-field row crop typologies: What I can offer A proven, cross-validated approach for deep learning boundary refinement adaptable to European LPIS or US CLU databases. What I need Consortium Building: Matchmaking with European research entities and public agencies holding comparable multi-sensor datasets to build a collaborative consortium for the next AgData Call for Proposals. Methodological Dialogue: Collaborative development of processing fallback rules to preserve model validation accuracy during periods with limited high-resolution aerial coverage.

Source: Annual AgData Use Case Summit 2026 booklet.