URBAN LAND-USE AND LAND-COVER MAPPING BASED ON THE CLASSIFICATION OF TRANSPORT DEMAND AND REMOTE SENSING DATA

In the framework of land-use mapping in urban areas, this paper explores the potential of the fusion of remote sensing data with information from transport demand data. The role of transport demand data is discussed and a probabilistic fusion framework is developed to exploit remote sensing and transport data in the discrimination of land use classes and land cover classes in urban and surrounding areas. Within this framework, two methods are proposed, based on pixelwise decision fusion and on the combination with a region-based multiscale Markov random field. The methods are validated on a case study associated with the Italian city of Genoa.