3D city models for urban mining: Point cloud based semantic enrichment for spectral variation identification in hyperspectral imagery

Urban mining aims at reusing building materials enclosed in our cities. Therefore, it requires accurate information on the availability of these materials for each separate building. While recent publications have demonstrated that such information can be obtained using machine learning and data fusion techniques applied to hyperspectral...

3D City Models in the Context of Urban Mining: A case study based on the CityGML model of Rotterdam

Recently, the application of machine learning and data fusion techniques on hyperspectral imagery have demonstrated potential for ground cover classification at material level. Hereby, specific locations of resources enclosed in cities (e.g. roof materials) can be identified, which is critically relevant within the field of urban mining. A...