We developed a semantic-segmentation model that separates satellite imagery into six classes such as buildings, roads and vegetation, achieving high pixel-level accuracy with a U-Net architecture.
Structured close to production quality, this R&D project demonstrates geospatial data capability applicable to urban planning, agriculture and disaster analysis.