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A transferable remote sensing approach to classify building structural types for seismic risk analyses: the case of Val d’Agri area (Italy)

Liuzzi, Mariangela and Aravena Pelizari, Patrick and Geiß, Christian and Masi, Angelo and Tramutoli, Valerio and Taubenböck, Hannes (2019) A transferable remote sensing approach to classify building structural types for seismic risk analyses: the case of Val d’Agri area (Italy). Bulletin of Earthquake Engineering, pp. 1-29. Springer. DOI: 10.1007/s10518-019-00648-7 ISSN 1570-761X

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Official URL: https://link.springer.com/article/10.1007/s10518-019-00648-7

Abstract

This study proposes a methodology based on machine learning (ML) algorithms for rapid and robust classification of building structural types (STs) in multispectral remote sensing imagery aiming to assess buildings’ seismic vulnerability. The seismic behavior of buildings is strongly affected by the ST, including material, age, height, and other main structural features. Previous works deployed in situ data integrated with remote sensing information to statistically infer STs through supervised ML methods. We propose a transferable methodology with specific focus on situations with imbalanced in situ data (i.e., the number of available labeled samples for model learning differs largely between different STs). We learn a transferable model by selecting features from an exhaustive set. The transferability relies on deploying geometric features characterizing individual buildings; thus, the model is less sensitive to domain adaption problems frequently induced by e.g., changes in acquisition parameters of remotely sensed imagery. Thereby, we show that few geometry features enable generalization capabilities similar to models learned with a large number of features describing spectral, geometrical or contextual building properties. We rely on an extensive geodatabase containing almost 18,000 building footprints. We follow a Random Forest (RF)-based feature selection strategy to objectively identify most valuable features for prediction. Furthermore, the problem of unbalanced classes is addressed by adopting two approaches: downsampling the majority class and modifying the classifier internally (weighted RF). The implemented model is transferred on the challenging urban morphology of the Val d’Agri area (Italy). Results confirm the statistical robustness of the model and the importance of the geometry features, allowing for reliable identification of STs.

Item URL in elib:https://elib.dlr.de/127901/
Document Type:Article
Title:A transferable remote sensing approach to classify building structural types for seismic risk analyses: the case of Val d’Agri area (Italy)
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Liuzzi, Mariangelamariangelaliuzzi88 (at) gmail.comhttps://orcid.org/0000-0002-1413-8655
Aravena Pelizari, PatrickPatrick.AravenaPelizari (at) dlr.deUNSPECIFIED
Geiß, ChristianChristian.Geiss (at) dlr.deUNSPECIFIED
Masi, AngeloSchool of Engineering, Università degli Studi della BasilicataUNSPECIFIED
Tramutoli, ValerioSchool of Engineering, Università degli Studi della BasilicataUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Date:15 June 2019
Journal or Publication Title:Bulletin of Earthquake Engineering
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1007/s10518-019-00648-7
Page Range:pp. 1-29
Publisher:Springer
ISSN:1570-761X
Status:Published
Keywords:machine learning, remote sensing, seismic vulnerability, building structural type, building inventory, class imbalance
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience, R - Vorhaben Sicherheitsrelevante Erdbeobachtung
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Aravena Pelizari, Patrick
Deposited On:05 Aug 2019 10:16
Last Modified:05 Aug 2019 10:16

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