KEYWORDS: Spatial resolution, Satellites, Land cover, Image enhancement, Cements, Solar cells, Plastics, Asphalt pavements, Education and training, Data acquisition
The TUS:CAN project, part of the ASI program "Innovation for Downstream Preparation - Public Administrations," focuses on sustainable urban planning through the classification of man-made materials using multispectral and hyperspectral satellite data. The project aims at mapping the land cover of natural and artificial materials in urban and suburban areas using 10m-hypersharpened Sentinel-2 data with a Random Forest classifier.
PRISMA hyperspectral data further categorize artificial materials, with ground truth spectral signatures collected for accuracy. The classification accuracy is finally validated using aerial hyperspectral data from Itres CASI-1500 and SASI-600 sensors.
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