URBAN LAND-USE AND LAND-COVER MAPPING BASED ON THE CLASSIFICATION OF TRANSPORT DEMAND AND REMOTE SENSING DATA
| Autor: | Chiara Tacconi, Nicola Sacco, Gabriele Moser, Maria Pia Tuscano |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
data fusion Markov random field 010504 meteorology & atmospheric sciences Land use Computer science transport zones 05 social sciences Probabilistic logic Land cover Image segmentation Urban land use mapping data fusion Markov random fields transport zones transport demand 01 natural sciences Urban land use mapping Data modeling Markov random fields Remote sensing (archaeology) 0502 economics and business transport demand 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | IGARSS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|