Bruno Adriano is currently a postdoctoral researcher at Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Japan. His research is focused on the fusion of remote sensing technologies and high-performance numerical simulation for disaster management.
He is a member of the Japan Society of Civil Engineers JSCE (2013) and the IEEE (2014).
|2018 Jun||-||Present||Postdoctoral Researcher, RIKEN AIP, Japan|
|2016 Apr||-||2018 Mar||JSPS Research Fellow, Tohoku University, Japan|
|2013 Apr||-||2016 Mar||Ph.D. in Civil Engineering, Tohoku University, Japan|
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 (accepted for publication), 2019.
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.
Y. Endo, B. Adriano, E. Mas, and S. Koshimura,
New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images
Remote Sensing, vol. 10, no. 12, pp. 2059, 2018.
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Abstract: The fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies have shown that computing changes over a moving window that clusters neighboring pixels is effective in identifying damaged buildings. Unfortunately, classifying tsunami-induced building damage into detailed damage classes remains a challenge. The purpose of this paper is to present a novel multiclass classification model that considers a high-dimensional feature space derived from several sizes of pixel windows and to provide guidance on how to define a multiclass classification scheme for detecting tsunami-induced damage. The proposed model uses a support vector machine (SVM) to determine the parameters of the discriminant function. The generalization ability of the model was tested on the field survey of the 2011 Great East Japan Earthquake and Tsunami and on a pair of TerraSAR-X images. The results show that the combination of different sizes of pixel windows has better performance for multiclass classification using SAR images. In addition, we discuss the limitations and potential use of multiclass building damage classification based on performance and various classification schemes. Notably, our findings suggest that the detectable classes for tsunami damage appear to differ from the detectable classes for earthquake damage. For earthquake damage, it is well known that a lower damage grade can rarely be distinguished in SAR images. However, such a damage grade is apparently easy to identify from tsunami-induced damage grades in SAR images. Taking this characteristic into consideration, we have successfully defined a detectable three-class classification scheme.
S. Karimzadeh, M. Matsuoka, M. Miyajima, B. Adriano, A. Fallahi, and J. Karashi,
Sequential SAR Coherence Method for the Monitoring of Buildings in Sarpole-Zahab, Iran
Remote Sensing, vol. 10, no. 8, pp. 1255, 2018.
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Abstract: In this study, we used fifty-six synthetic aperture radar (SAR) images acquired from the Sentinel-1 C-band satellite with a regular period of 12 days (except for one image) to produce sequential phase correlation (sequential coherence) maps for the town of Sarpole-Zahab in western Iran, which experienced a magnitude 7.3 earthquake on 12 November 2017. The preseismic condition of the buildings in the town was assessed based on a long sequential SAR coherence (LSSC) method, in which we considered 55 of the 56 images to produce a coherence decay model with climatic and temporal parameters. The coseismic condition of the buildings was assessed with 3 later images and normalized RGB visualization using the short sequential SAR coherence (SSSC) method. Discriminant analysis between the completely collapsed and uncollapsed buildings was also performed for approximately 700 randomly selected buildings (for each category) by considering the heights of the buildings and the SSSC results. Finally, the area and volume of debris were calculated based on a fusion of a discriminant map and a 3D vector map of the town.