Artificial Intelligence for the Automated Creation of Multi-scale Digital Twins of the Built World—AI4TWINNING

André Borrmann, Manoj Biswanath, Alex Braun, Zhaiyu Chen, Daniel Cremers, Medhini Heeramaglore, Ludwig Hoegner, Mansour Mehranfar, Thomas H. Kolbe, Frank Petzold, Alejandro Rueda, Sergei Solonets, Xiao Xiang Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The AI4TWINNING project aims at the automated generation of a system of inter-related digital twins of the built environment spanning multiple resolution scales providing rich semantics and coherent geometry. To this end, an interdisciplinary group of researchers develops a multi-scale, multi-sensor, multi-method approach combining terrestrial, airborne, and spaceborne acquisition, different sensor types (visible, thermal, LiDAR, Radar) and different processing methods integrating top-down and bottom-up AI approaches. The key concept of the project lies in intelligently fusing the data from different sources by AI-based methods, thus closing information gaps and increasing completeness, accuracy and reliance of the resulting digital twins. To facilitate the process and improve the results, the project makes extensive use of informed machine learning by exploiting explicit knowledge on the design and construction of built facilities. The final goal of the project is not to create a single monolithic digital twin, but instead a system of interlinked twins across different scales, providing the opportunity to seamlessly blend city, district and building models while keeping them up-to-date and consistent. As testbed and demonstration scenario serves a urban zone around the city campus of TUM, for which large data sets from various sensors are available.

Original languageEnglish
Title of host publicationRecent Advances in 3D Geoinformation Science - Proceedings of the 18th 3D GeoInfo Conference
EditorsThomas H. Kolbe, Andreas Donaubauer, Christof Beil
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783031436987
StatePublished - 2024
EventInternational 3D GeoInfo Conference, 3DGeoInfo 2023 - Munich, Germany
Duration: 12 Sep 202314 Sep 2023

Publication series

NameLecture Notes in Geoinformation and Cartography
ISSN (Print)1863-2246
ISSN (Electronic)1863-2351


ConferenceInternational 3D GeoInfo Conference, 3DGeoInfo 2023


  • Artificial intelligence
  • Building Information Modelling (BIM)
  • Digital twin
  • Point clouds


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