TY - GEN
T1 - Artificial Intelligence for the Automated Creation of Multi-scale Digital Twins of the Built World—AI4TWINNING
AU - Borrmann, André
AU - Biswanath, Manoj
AU - Braun, Alex
AU - Chen, Zhaiyu
AU - Cremers, Daniel
AU - Heeramaglore, Medhini
AU - Hoegner, Ludwig
AU - Mehranfar, Mansour
AU - Kolbe, Thomas H.
AU - Petzold, Frank
AU - Rueda, Alejandro
AU - Solonets, Sergei
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Building Information Modelling (BIM)
KW - Digital twin
KW - Point clouds
UR - http://www.scopus.com/inward/record.url?scp=85188270808&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43699-4_14
DO - 10.1007/978-3-031-43699-4_14
M3 - Conference contribution
AN - SCOPUS:85188270808
SN - 9783031436987
T3 - Lecture Notes in Geoinformation and Cartography
SP - 233
EP - 247
BT - Recent Advances in 3D Geoinformation Science - Proceedings of the 18th 3D GeoInfo Conference
A2 - Kolbe, Thomas H.
A2 - Donaubauer, Andreas
A2 - Beil, Christof
PB - Springer Science and Business Media Deutschland GmbH
T2 - International 3D GeoInfo Conference, 3DGeoInfo 2023
Y2 - 12 September 2023 through 14 September 2023
ER -