Analysis of remote sensing imagery for disaster assessment using deep learning

a case study of flooding event

Liping Yang, Guido Cervone

Research output: Contribution to journalArticle

Abstract

This paper proposes a methodology that integrates deep learning and machine learning for automatically assessing damage with limited human input in hundreds of thousands of aerial images. The goal is to develop a system that can help automatically identifying damaged areas in massive amount of data. The main difficulty consists in damaged infrastructure looking very different from when undamaged, likely resulting in an incorrect classification because of their different appearance, and the fact that deep learning and machine learning training sets normally only include undamaged infrastructures. In the proposed method, a deep learning algorithm is firstly used to automatically extract the presence of critical infrastructure from imagery, such as bridges, roads, or houses. However, because damaged infrastructure looks very different from when undamaged, the set of features identified can contain errors. A small portion of the images are then manually labeled if they include damaged areas, or not. Multiple machine learning algorithms are used to learn attribute–value relationships on the labeled data to capture the characteristic features associated with damaged areas. Finally, the trained classifiers are combined to construct an ensemble max-voting classifier. The selected max-voting model is then applied to the remaining unlabeled data to automatically identify images including damaged infrastructure. Evaluation results (85.6% accuracy and 89.09% F1 score) demonstrated the effectiveness of combining deep learning and an ensemble max-voting classifier of multiple machine learning models to analyze aerial images for damage assessment.

Original languageEnglish (US)
JournalSoft Computing
DOIs
StatePublished - Jan 1 2019

Fingerprint

Flooding
Disaster
Remote Sensing
Disasters
Learning systems
Remote sensing
Machine Learning
Infrastructure
Voting
Aerial Image
Classifiers
Classifier
Learning algorithms
Learning Algorithm
Ensemble
Antennas
Damage Assessment
Critical infrastructures
Critical Infrastructure
Damage

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

Cite this

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title = "Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event",
abstract = "This paper proposes a methodology that integrates deep learning and machine learning for automatically assessing damage with limited human input in hundreds of thousands of aerial images. The goal is to develop a system that can help automatically identifying damaged areas in massive amount of data. The main difficulty consists in damaged infrastructure looking very different from when undamaged, likely resulting in an incorrect classification because of their different appearance, and the fact that deep learning and machine learning training sets normally only include undamaged infrastructures. In the proposed method, a deep learning algorithm is firstly used to automatically extract the presence of critical infrastructure from imagery, such as bridges, roads, or houses. However, because damaged infrastructure looks very different from when undamaged, the set of features identified can contain errors. A small portion of the images are then manually labeled if they include damaged areas, or not. Multiple machine learning algorithms are used to learn attribute–value relationships on the labeled data to capture the characteristic features associated with damaged areas. Finally, the trained classifiers are combined to construct an ensemble max-voting classifier. The selected max-voting model is then applied to the remaining unlabeled data to automatically identify images including damaged infrastructure. Evaluation results (85.6{\%} accuracy and 89.09{\%} F1 score) demonstrated the effectiveness of combining deep learning and an ensemble max-voting classifier of multiple machine learning models to analyze aerial images for damage assessment.",
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