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

Sahar S Matin1, Biswajeet Pradhan1,2,3,4

  • 1Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia.

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|July 2, 2021
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Summary
This summary is machine-generated.

Explainable AI (XAI) improves earthquake damage assessment. Using SHAP with a multilayer perceptron (MLP) identified key features for accurate building collapse classification from satellite imagery.

Keywords:
building-damage mappingexplainable AIfeature analysismachine learningremote sensing

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Area of Science:

  • Remote Sensing
  • Artificial Intelligence
  • Geospatial Analysis

Background:

  • Accurate post-earthquake building-damage mapping is crucial for disaster response.
  • Current AI frameworks for damage assessment lack reliability due to site-specific designs, model opacity, and data quality issues.
  • Explainable AI (XAI) offers a path to understand and improve AI model limitations.

Purpose of the Study:

  • To enhance the reliability of AI-based building-damage assessment using explainable AI (XAI).
  • To interpret the decision-making process of a multilayer perceptron (MLP) model for classifying building damage.
  • To identify and analyze the impact of feature descriptors on damage assessment accuracy.

Main Methods:

  • Application of SHAP (Shapley additive explanation) for interpreting MLP model outputs.
  • Utilizing post-event satellite imagery from the 2018 Palu earthquake for analysis.
  • Feature engineering and selection to refine the input data for the MLP model.

Main Results:

  • The MLP model achieved an overall accuracy of 84% in classifying collapsed and non-collapsed buildings after removing redundant features.
  • Spectral features were identified as more significant than texture features in distinguishing between collapsed and non-collapsed buildings.
  • SHAP analysis provided insights into feature importance, aiding in model refinement.

Conclusions:

  • Explainable AI (XAI) enhances the transparency and reliability of AI models for building-damage assessment.
  • Understanding feature importance is key to improving AI model performance and data quality.
  • Developing explainable AI models facilitates the creation of more transferable AI solutions for disaster management.