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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-4/W2-2022
https://doi.org/10.5194/isprs-annals-X-4-W2-2022-129-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-X-4-W2-2022-129-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Oct 2022
| 14 Oct 2022
A FRAMEWORK TO INTEGRATE BIM WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING-BASED PROPERTY VALUATION METHODS
P. Jafary
Building 4.0 CRC, Caulfield East, Victoria, Australia
Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia
D. Shojaei
Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia
A. Rajabifard
Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia
T. Ngo
Building 4.0 CRC, Caulfield East, Victoria, Australia
Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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Floods are frequent natural hazards in Italy, triggering significant adverse consequences on the economy every year. Their impact is expected to worsen in the near future due to socio-economic development and climate variability. To be able to reduce the probability and magnitude of expected economic losses, flood risk managers need to be correctly informed about the potential damage from flood hazards. In this study, we have developed a new and accurate model for Italian residential buildings.
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Short summary
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In this paper, a newly derived flood loss function for Australian residential structures (FLFArs) is presented and calibrated by using historic data collected from an extreme event in Queensland, Australia, that occurred in 2013. Afterwards, the performance of FLFArs has been compared with the observed damage data collected from a 2012 flood event in Maranoa, Queensland. Based on this analysis, validation of FLFArs has been performed in terms of Australian geographical conditions.
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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