Enabling Component Reuse From Existing Buildings Using Machine Learning-Using Google Street View to Enhance Building Databases

Deepika Raghu, Areti Markopoulou, Mathilde Marengo, Iacopo Neri, Angelos Chronis, Catherine De Wolf

Publikation: Beitrag in Buch oder TagungsbandBeitrag in Tagungsband ohne Präsentation


Intense urbanization has led us to rethink construction and demolition practices on a global scale. There is an opportunity to respond to the climate crisis by moving towards a circular built environment. Such a paradigm shift can be achieved by critically examining the possibility of reusing components from existing buildings. This study investigates approaches and tools needed to analyse the existing building stock and methods to enable component reuse. Ocular observations were conducted in Google Street View to analyse two building-specific haracteristics: (1) façade material and (2) reusable components (window, doors, and shutters) found on building facades in two cities: Barcelona and Zurich. Not all products are equally suitable for reuse and require an evaluation metric to understand which components can be reused effectively. Consequently, tailored reuse strategies that are defined by a priority order of waste prevention are put forth. Machine learning shows promising potential to visually collect building-specific characteristics that are relevant for component reuse. The data collected is used to create classification maps that can help define protocols and for urban planning. This research can upscale limited information in countries where available data about the existing building stock is insufficient.
TitelProceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2022
PublikationsstatusVeröffentlicht - 2022

Research Field

  • Ehemaliges Research Field - Integrated Digital Urban Planning


  • Machine Learning; Component Reuse; Google Street View; Material Banks; Building Databases; SDG 11; SDG 12


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