New Publication on Automated building characterization

New Publication on Automated building characterization

September 15, 2021

A new publication by Hannes Taubenböck and colleagues is online about “Automated building characterization for seismic risk assessment using street-level imagery and deep learning”. From the abstract: “Accurate seismic risk modeling requires knowledge of key structural characteristics of buildings. However, to date, the collection of such data is highly expensive in terms of labor, time and money and thus prohibitive for a spatially continuous large-area monitoring. This study quantitatively evaluates the potential of an automated and thus more efficient collection of vulnerability-related structural building characteristics based on Deep Convolutional Neural Networks (DCNNs) and street-level imagery such as provided by Google Street View. The proposed approach involves a tailored hierarchical categorization workflow to structure the highly heterogeneous street-level imagery in an application-oriented fashion. Thereupon, we use state-of-the-art DCNNs to explore the automated inference of Seismic Building Structural Types. These reflect the main-load bearing structure of a building, and thus its resistance to seismic forces. Additionally, we assess the independent retrieval of two key building structural parameters, i.e., the material of the lateral-load-resisting system and building height to investigate the applicability for a more generic structural characterization of buildings. Experimental results obtained for the earthquake-prone Chilean capital Santiago show accuracies beyond κ = 0.81 for all addressed classification tasks. This underlines the potential of the proposed methodology for an efficient in-situ data collection on large spatial scales with the purpose of risk assessments related to earthquakes, but also other natural hazards (e.g., tsunamis, or floods).”

read full article here:

P. Aravena Pelizari, C. Geiß, P. Aguirre, H. Santa María, Y. Merino Peña, and H. Taubenböck (2021) Automated building characterization for seismic risk assessment using street-level imagery and deep learning. ISPRS Journal of Photogrammetry and Remote Sensing

you may also like:

‘Super Test Site Würzburg’ project meeting

‘Super Test Site Würzburg’ project meeting

After the successful "Super Test Site Würzburg" measurement campaign in June (please see here: https://remote-sensing.org/super-test-site-wurzburg-from-the-idea-to-realization/ ), the core team from the University of Würzburg, the Karlsruhe Institute of Technology,...

EORC Talk: Geolingual Studies: A New Research Direction

EORC Talk: Geolingual Studies: A New Research Direction

On July 19th, Lisa Lehnen and Richard Lemoine Rodríguez, two postdoctoral researchers of the Geolingual Studies project, gave an inspiring presentation at the EORC talk series.   In the talk titled "Geolingual Studies – a new research direction", they...

EO support for UrbanPArt field work

EO support for UrbanPArt field work

From May to September, Karla Wenner, a PhD student at the Juniorprofessorship for Applied Biodiversity Science, will be sampling urban green spaces and semi-natural grasslands in Würzburg as part of the UrbanPArt project. Our cargo bikes support the research project...

Cinematic drone shots

Cinematic drone shots

We spend quite some time in the field conducting field work, from lidar measurements to vegetation samples in order to correlate it with remote sensing data to answer various research questions concerning global change. Field work is always a 24/7 work load and...

Sommer event at DLR EOC

Sommer event at DLR EOC

Some of our staff joined the DLR EOC summer event and spend the day talking with various colleagues from DLR as well as experiencing the newest developments such as the virtual reality experiences by the department of Nils Sparwasser. Beside various topical...

Media reports on our work

Media reports on our work

We recently reported on our study published in The LANCET Planetary Health Journal on the impact of urban configuration types on urban heat islands, air pollution, CO2 emissions, and mortality – please see here:...