Using high-resolution satellite imagery to provide a relief priority map after earthquake

Ranjbar, Hamid Reza; Ardalan, Alireza A.; Dehghani, Hamid; Saradjian, Mohammad Reza

doi:10.1007/s11069-017-3085-y

Cited by 13 publications

(7 citation statements)

References 64 publications

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“…By selecting the most relevant features to distinguish between collapsed and intact buildings, there is a higher likelihood of achieving a good result even without using an advanced machine learning model and hyper-parameter tuning. Therefore, feature selection is a critical pre-processing step in classification studies [ 53 ].…”

Section: Methodsmentioning

confidence: 99%

Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)

Matin

Pradhan

2021

Sensors

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Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)—a machine learning model—and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model’s decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model.

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Section: Methodsmentioning

confidence: 99%

Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)

Matin

Pradhan

2021

Sensors

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“…These methods generally focus on optical high-resolution imagery. Although many research efforts have proposed a number of damage detection algorithms and applied them to optical high-resolution imagery and LiDAR data, many limitations remain, such as: (1) some of these methods are based on change detection and classification methods that require training data and prior knowledge or ground truth [25]; (2) the high-resolution remote sensing imagery has a low spectral resolution and sometimes, due to some conditions, the pre-event imagery is unavailable [26,27]; (3) the high-resolution imagery suffers from low spectral resolution, so the detection of damaged areas becomes exceedingly difficult and/or suffers from a high false alarm rate due to, e.g., the shadows of buildings; (4) a separation between damage and shadows using high-resolution imagery is extraordinarily difficult; (5) the high-resolution imagery covers a low scale area and has more commercial aspects; (6) the building subsidence is not detected by optical data, unless a hole happens to open in the roof while the SAR (Synthetic Aperture Radar) imagery is being recorded (Figure 1) [7,10]; and (7) optical data are often limited by cloud cover. Airborne LiDAR systems, on the other hand, allow for the fast and extensive acquisition of precise height (altitude) data which can be used to detect some specific damage types [8].…”

Section: Introductionmentioning

confidence: 99%

Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery

et al. 2021

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Earth, as humans’ habitat, is constantly affected by natural events, such as floods, earthquakes, thunder, and drought among which earthquakes are considered one of the deadliest and most catastrophic natural disasters. The Iran-Iraq earthquake occurred in Kermanshah Province, Iran in November 2017. It was a 7.4-magnitude seismic event that caused immense damages and loss of life. The rapid detection of damages caused by earthquakes is of great importance for disaster management. Thanks to their wide coverage, high resolution, and low cost, remote-sensing images play an important role in environmental monitoring. This study presents a new damage detection method at the unsupervised level, using multitemporal optical and radar images acquired through Sentinel imagery. The proposed method is applied in two main phases: (1) automatic built-up extraction using spectral indices and active learning framework on Sentinel-2 imagery; (2) damage detection based on the multitemporal coherence map clustering and similarity measure analysis using Sentinel-1 imagery. The main advantage of the proposed method is that it is an unsupervised method with simple usage, a low computing burden, and using medium spatial resolution imagery that has good temporal resolution and is operative at any time and in any atmospheric conditions, with high accuracy for detecting deformations in buildings. The accuracy analysis of the proposed method found it visually and numerically comparable to other state-of-the-art methods for built-up area detection. The proposed method is capable of detecting built-up areas with an accuracy of more than 96% and a kappa of about 0.89 in overall comparison to other methods. Furthermore, the proposed method is also able to detect damaged regions compared to other state-of-the-art damage detection methods with an accuracy of more than 70%.

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“…(1) Designing specific survey methods to assess people's understanding of earthquake rescue factors through questionnaires and analytic hierarchy process (AHP) [10][11][12]. (2) rough the consideration of land use types, combined with the damage degree of buildings, land use index, population density, and other indicators, a standard database for building damage after earthquakes was developed [13,14]. (3) By investigating the uncertainty of natural disaster information, a dynamic task allocation method based on contract network protocol (CNP) is established [15,16].…”

Section: Introductionmentioning

confidence: 99%

Construction of Earthquake Rescue Model Based on Hierarchical Voronoi Diagram

Pan

2020

Mathematical Problems in Engineering

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Traditional earthquake rescue methods have many problems, such as the impact of road damage on earthquake rescue, the rational allocation of disaster relief materials, and the active division of rescue areas. According to the characteristics of earthquake relief, a new earthquake rescue model is designed from three aspects: high-altitude rescue, road transport relief supplies, and street human resources rescue. In this paper, the hierarchical Voronoi diagram is applied to the earthquake rescue problem, the earthquake rescue model is constructed, and the area affected by the earthquake is reasonably divided. We discussed the earthquake intensity and the proportion of the population in the disaster area and the number of disaster relief materials and other parameters and conducted an in-depth analysis of the model to form a useful earthquake rescue model. Visual simulation results have been obtained by computer simulation. Compared with the traditional earthquake rescue plan, the model proposed in this paper is more effective and scientific in improving the material distribution, personnel rescue, and postdisaster reconstruction after the earthquake, and it also provides a new idea for the rescue of other natural disasters. It has practical social value and broad application prospects. Besides, the models and conclusions given in this paper have been recognized by the Seismological Bureau of Hainan Province and the Mathematics Research Center of Hainan Province.

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Using high-resolution satellite imagery to provide a relief priority map after earthquake

Cited by 13 publications

References 64 publications

Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)

Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)

Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery

Construction of Earthquake Rescue Model Based on Hierarchical Voronoi Diagram

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