Earthquakes are among the most catastrophic natural disasters to affect mankind. One of the critical problems after an earthquake is building damage assessment. The area, amount, rate, and type of the damage are essential information for rescue, humanitarian and reconstruction operations in the disaster area. Remote sensing techniques play an important role in obtaining building damage information because of their non-contact, low cost, wide field of view, and fast response capacities. Now that more and diverse types of remote sensing data become available, various methods are designed and reported for building damage assessment. This paper provides a comprehensive review of these methods in two categories: multi-temporal techniques that evaluate the changes between the pre- and post-event data and mono-temporal techniques that interpret only the post-event data. Both categories of methods are discussed and evaluated in detail in terms of the type of remote sensing data utilized, including optical, LiDAR and SAR data. Performances of the methods and future efforts are drawn from this extensive evaluation.

A comprehensive review of earthquake-induced building damage detection with remote sensing techniques

Understanding an urbanizing planet: Strategic directions for remote sensing

Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.

Big Data in Natural Disaster Management: A Review

Over the past 15 years, scientists and disaster responders have increasingly used satellite-based Earth observations for global rapid assessment of disaster situations. We review global trends in satellite rapid response and emergency mapping from 2000 to 2014, analyzing more than 1000 incidents in which satellite monitoring was used for assessing major disaster situations. We provide a synthesis of spatial patterns and temporal trends in global satellite emergency mapping efforts and show that satellite-based emergency mapping is most intensively deployed in Asia and Europe and follows well the geographic, physical, and temporal distributions of global natural disasters. We present an outlook on the future use of Earth observation technology for disaster response and mitigation by putting past and current developments into context and perspective.

https://science.sciencemag.org/content/sci/353/6296/247.full.pdf

Global trends in satellite-based emergency mapping

Automatically detecting objects with complex appearance and arbitrary orientations in remote sensing imagery (RSI) is a big challenge. To explore a possible solution to the problem, this paper develops an object detection framework using a discriminatively trained mixture model. It is mainly composed of two stages: model training and object detection. In the model training stage, multi-scale histogram of oriented gradients (HOG) feature pyramids of all training samples are constructed. A mixture of multi-scale deformable part-based models is then trained for each object category by training a latent Support Vector Machine (SVM), where each part-based model is composed of a coarse root filter, a set of higher resolution part filters, and a set of deformation models. In the object detection stage, given a test imagery, its multi-scale HOG feature pyramid is firstly constructed. Then, object detection is performed by computing and thresholding the response of the mixture model. The quantitative comparisons with state-of-the-art approaches on two datasets demonstrate the effectiveness of the developed framework.

Object detection in remote sensing imagery using a discriminatively trained mixture model

To address the disadvantage of traditional methods providing only two-dimensional (2D) damage change of the collapsed buildings, this paper presents an approach for the detection of the buildings that have collapsed in an earthquake based on 3D geometric changes, particularly height change of the buildings, using pre- and post-seismic IKONOS stereo image pairs. One of the critical issues – the accuracy of the 3D ground point determination from high-resolution satellite imagery (HRSI) – is first addressed employing a bias-compensation model based on the rational function polynomial coefficient bundle adjustment. With the refined 3D ground coordinates, two ways of detecting the collapsed buildings are proposed: (1) detection of an individual collapsed building by comparing the height differences at the corner points of the building calculated from the pre- and post-seismic IKONOS stereo pairs and (2) determination of the region of collapsed buildings by calculating the difference between the pre- and post-seismic digital elevation models (DEMs) generated again from the pre- and post-seismic stereo images. At the same time, a pre-seismic DEM based on a topographical map is generated for detection comparison in the study. The experiment results for Dujiangyan using two IKONOS stereo pairs before and after the Wenchuan earthquake demonstrated the following. (1) Accuracy of better than 1.1 m in planimetry and 1.5 m in height can be achieved from the pre- and post-seismic IKONOS stereo image pairs using the affine bias compensation model. This accuracy guarantees the feasibility of detecting the 3D geometric changes of the earthquake-induced building collapses from pre- and post-seismic HRSI stereo images. (2) Using the refined 3D coordinates of the ground points computed from the pre- and post-seismic IKONOS stereo pairs, the status (i.e., totally collapsed, partially collapsed or not collapsed) and the number of collapsed storeys can be estimated for an individual building being assessed. (3) The region of collapsed buildings can be determined by differencing the pre- and post-seismic DEMs created from the pre- and post-seismic IKONOS stereo pairs. An overall accuracy of better than 90% is achieved for the detection of the collapsed buildings based on the difference DEM using the pixel- and object-based assessment methods.

Zhengyuan Li

Papers

Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake