Contextual classification using photometry and elevation data for damage detection after an earthquake event

Ewelina Rupnik (Corresponding Author), Francesco Nex, Isabella Toschi, Fabio Remondino

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
23 Downloads (Pure)

Abstract

This research presents a processing workflow to automatically find damaged building areas in an urban context. The input data requirements are high-resolution multi-view images, acquired from airborne platform. The elevations are derived from a dense surface model generated with photogrammetric methods. With the principal objective of rapid response in emergency situations, two different processing roadmaps are proposed, semi-supervised and unsupervised. Both of them follow a two-step workflow of building detection and building health estimation. Optionally, cadastral layers may serve as a-priori knowledge on building location. The semi-supervised approach involves a data training step, while the unsupervised approach exploits the similarities and dissimilarities between sets of features calculated over the detected buildings. The change detection task is formulated as a classification task defined over a conditional random field. The algorithms are evaluated using two datasets (Vexcel and Midas cameras) and results are compared with ground truth data and specific metrics.
Original languageEnglish
Pages (from-to)543-557
Number of pages15
JournalEuropean Journal of Remote Sensing
Volume51
Issue number1
DOIs
Publication statusPublished - 31 May 2018

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

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