Mr. June Alexis Santos
April 21, 2026
CITY OF SAN FERNANDO, PAMPANGA — The SARAI Regional Hub of DOST Central Luzon highlights a major achievement in geospatial technology through its DOST-SINAG initiative, unveiling a high-precision digital resource designed to identify annual crop lands and create an AI-based annual crop mask for its seven partner LGUs. The system has demonstrated a remarkable 91% F1-score in identifying annual crop fields, providing a level of accuracy that is critical for real-world agricultural planning and land-use verification.
The innovation centers on a customized data-processing pipeline that transforms hyperdimensional satellite embeddings into clear, actionable farm boundaries. By employing advanced feature distillation techniques, the system is able to isolate the unique "spectral fingerprints" of croplands from a massive pool of satellite-derived data points. This process allows the model to ignore environmental noise and focus on the distinct temporal signatures of annual crops at a sharp 10-meter resolution. The result is a highly refined digital mask that can systematically delineate farm parcels across diverse terrains.
This initiative is specifically designed to alleviate the intensive Geographic Information System (GIS) workloads of partner LGUs, including: Candaba in Pampanga, Iba in Zambales, Science City of Muñoz in Nueva Ecija, Dingalan in Aurora, City of Balanga in Bataan, San Miguel in Bulacan, Tarlac City in Tarlac.
Traditionally, identifying and verifying farm boundaries was a labor-intensive task requiring manual survey and digitization. By integrating this automated delineation tool into the partners' process pipeline, SARAI Central Luzon provides local GIS officers with a "ready-to-use" agricultural layer, significantly reducing the time spent on manual mapping.
For the partner municipalities, this project represents more than just technical efficiency; it is a vital tool for data-driven governance. By providing an accurate and automated inventory of farm parcels, DOST-Central Luzon ensures that site-specific interventions, crop insurance, and resource distribution are delivered more effectively to the farming community. This milestone reaffirms the SARAI Regional Hub’s commitment to bridging the gap between advanced data science and practical, local-level agricultural support.
Souce: FAO Land Tenure and GIS-based land administration guidelines
The SINAG TEAM of DOST Central Luzon led by Regional Director, Dr. Julius Caesar V. Sicat, CESO III.
Traditionally, identifying and verifying farm boundaries was a labor-intensive task requiring manual survey and digitization.
This helps our 7 partner LGU's in the process of intensive GIS-based land parcelization.
The system has demonstrated a remarkable 91% F1-score in identifying annual crop fields, providing a level of accuracy that is critical for real-world agricultural planning and land-use verification. But ground validation to ensure its accuracy is needed.
AI-powered land analysis that breaks terrain into parcels, identifies crop types, measures coverage, and tracks changes over time—delivering faster, more consistent, and more accurate mapping than traditional methods.
Principal Component Analysis (PCA) is a technique in Statistics and Machine Learning used to simplify complex data by reducing its number of variables while keeping as much important information as possible.
Alpha Earth Embeddings are a type of vector representation of geographic locations on Earth, designed to encode rich environmental, spatial, and human-related information into numerical form so machines can understand and analyze the planet more effective