Gerald Baier is a postdoctoral researcher at RIKEN Center for Advanced Intelligence Project's Geoinformatics Unit.
His research interests are digital signal processing and machine learning for synthetic aperture radar interferometry and polarimetry, and high-performance computing.
|2018 Sep||-||Present||Postdoctoral researcher, RIKEN AIP, Japan|
|2014 Apr||-||2018 Jul||Ph.D. at the Remote Sensing Technology Institute of the German Aerospace Center (DLR) and the Technical University of Munich, Germany|
|2010 Sep||-||2012 Nov||M.Sc. in Information and Communication Technologies, Université catholique de Louvain, Belgium and Karlsruhe Institute of Technology, Germany|
|2007 Oct||-||2010 Aug||B.Sc. in Electrical Engineering, Karlsruhe Institute of Technology, Germany|
B. Adriano, J. Xia, G. Baier, N. Yokoya, S. Koshimura,
Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia
Remote Sensing, vol. 11, no. 7, pp. 886, 2019.
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Abstract: This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from SAR and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, DEM- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy > 90%, average accuracy > 67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 hours after acquiring all raw datasets.