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

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Abstract

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 imagery, challenges still persist. One of these is the so-called 'salt-And-pepper noise', i.e.The oversensitivity to the presence of several materials within one pixel (e.g. chimneys, roof windows). For the specific case of identifying roof materials, this research demonstrates the potential of 3D city models to identify and filter out such unreliable pixels beforehand. As, from a geometrical point of view, most available 3D city models are too generalized for this purpose (e.g. in CityGML Level of Detail 2), semantic enrichment using a point cloud is proposed to compensate missing details. So-called deviations are mapped onto a 3D building model by comparing it with a point cloud. Seeded region growing approach based on distance and orientation features is used for the comparison. Further, the results of a validation carried out for parts of Rotterdam and resulting in KHAT values as high as 0.7 are discussed.

Original languageEnglish
Pages (from-to)223-230
Number of pages8
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume5
Issue number4
DOIs
Publication statusPublished - 2020
Event2020 24th ISPRS Congress - Technical Commission IV on Spatial Information Science - Nice, Virtual, France
Duration: 31 Aug 20202 Sep 2020

Keywords

  • CityGML
  • Enrichment
  • Point cloud
  • Semantic 3D city models
  • Urban mining

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