Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification

Abstract

Previous applications of machine learning in remote sensing for the identification of damaged buildings in the aftermath of a large-scale disaster have been successful. However, standard methods do not consider the complexity and costs of compiling a training data set after a large-scale disaster. In this article, we study disaster events in which the intensity can be modeled via numerical simulation and/or instrumentation. For such cases, two fully automatic procedures for the detection of severely damaged buildings are introduced. The fundamental assumption is that samples that are located in areas with low disaster intensity mainly represent nondamaged buildings. Furthermore, areas with moderate to strong disaster intensities likely contain damaged and nondamaged buildings. Under this assumption, a procedure that is based on the automatic selection of training samples for learning and calibrating the standard support vector machine classifier is utilized. The second procedure is based on the use of two regularization parameters to define the support vectors. These frameworks avoid the collection of labeled building samples via field surveys and/or visual inspection of optical images, which requires a significant amount of time. The performance of the proposed method is evaluated via application to three real cases: the 2011 Tohoku-Oki earthquake-tsunami, the 2016 Kumamoto earthquake, and the 2018 Okayama floods. The resulted accuracy ranges between 0.85 and 0.89, and thus, it shows that the result can be used for the rapid allocation of affected buildings.

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

“…While transfer learning remains difficult for complex deep-learning models, the transfer of simpler models, or the use of supervised expert knowledge within unsupervised frameworks, remains an interesting research lead. In this regard, the work of Moya et al on fragility functions and the demand parameter depending on the disaster intensity and partial field information seems to be promising [14,15].…”

“…In [15], the authors use the same combination of a fragility function with a machine learning method, this time with Support Vector Machines. They also introduce the demand parameter using various thresholding techniques and field information from in situ sensors, which allow to better define high-priority areas.…”

Brief communication: Radar images for monitoring informal urban settlements in vulnerable zones in Lima, Peru

“…Remote sensing data have been used to extract information from urban and rural areas, such as land cover classification (Geiß et al, 2020), urban growth (Shi et al, 2019), and detection of damaged buildings (Moya et al, 2021). Regarding informal settlements, a comprehensive study of its spatial morphology can be found in Taubenböck et al (2018).…”

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

“…The diversity of RS Earth observation imagery and its free availability has meant that monitoring of changes following disasters has turned into a hot topic for research [21][22][23][24][25][26][27][28]. Indeed, we are witnessing many BAM products on a global scale that differ in terms of spatial resolution and reliability of the burned areas mapped.…”