3D CITY MODELS FOR URBAN MINING: POINT CLOUD BASED SEMANTIC ENRICHMENT FOR SPECTRAL VARIATION IDENTIFICATION IN HYPERSPECTRAL IMAGERY

P A Ruben,G Agugiaro,R Sileryte

doi:10.5194/isprs-annals-v-4-2020-223-2020

Abstract

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.

Highlights

Recent circular economy initiatives, such as urban mining create a strong need for information about the materials currently composing the built environment
While new buildings increasingly start using material passports or Building Information Models (BIM) to keep a record of such information, huge stocks of materials are already available in older constructions without being documented
This paper proposes a priori identification of such pixels using a 3D city model which was previously enriched by means of a point cloud

Summary

Introduction

Recent circular economy initiatives, such as urban mining (i.e. the reuse of building materials) create a strong need for information about the materials currently composing the built environment. The construction of new buildings and the retrofitting of existing ones create a need for more sustainable approaches. One of these is the sourcing of materials already enclosed in the cities and released relatively close-by at the end of their life cycle (Baccini, Brunner, 2012). While new buildings increasingly start using material passports or Building Information Models (BIM) to keep a record of such information, huge stocks of materials are already available in older constructions without being (digitally) documented

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