A Knowledge-Driven Automatic Change Detection System for Disaster Monitoring using Satellite Data

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  • HASHIMOTO Shutaro
    Graduate School of Information Science and Technology, Hokkaido University
  • TADONO Takeo
    Graduate School of Information Science and Technology, Hokkaido University Earth Observation Research Center, Japan Aerospace Exploration Agency
  • ONOSATO Masahiko
    Graduate School of Information Science and Technology, Hokkaido University
  • HORI Masahiro
    Graduate School of Information Science and Technology, Hokkaido University Earth Observation Research Center, Japan Aerospace Exploration Agency

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Other Title
  • 衛星データによる災害状況把握のための知識駆動型自動変化検出システムの提案
  • エイセイ データ ニ ヨル サイガイ ジョウキョウ ハアク ノ タメ ノ チシキ クドウガタ ジドウ ヘンカ ケンシュツ システム ノ テイアン

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Abstract

Change detection using satellite data is frequently used in cases of natural disasters to identify the location and extent of the damage. However, current practical analysis for disaster monitoring largely depends on manual analysis by experts, creating a bottleneck of data that hinders effective and prompt response. This paper proposes an automatic change detection system that can execute entire processes of change detection without the intervention of human experts. The proposed system employs a knowledge-driven system based on ontology. The system stores knowledge modules, each of which can extract information or make an inference. At the time of change detection, the system selects the appropriate knowledge modules and constructs a Bayesian network for the inference of target change by interpreting the semantics of the target change and knowledge modules using ontology. The system extracts information and makes an inference, yielding probability images that indicate the confidence degree of the inference of the target change. This paper presents a demonstration of the proposed detection system by applying it without any modifications in the system to two cases of disaster monitoring: a mudslide and a flood.

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