A deep contrastive learning approach to extremely-sparse disaster damage assessment in social sensing

Yang Zhang, Ruohan Zong, Lanyu Shang, Ziyi Kou, Dong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social sensing has emerged as a pervasive and scalable sensing paradigm to obtain timely information of the physical world from "human sensors". In this paper, we study a new extremely-sparse disaster damage assessment (DBA) problem in social sensing. The objective is to automatically assess the damage severity of affected areas in a disaster event by leveraging the imagery data reported on online social media with extremely sparse training data (e.g., only 1% of the data samples have labels). Our problem is motivated by the limitation of current DDA solutions that often require a significant amount of high-quality training data to learn an effective DDA model. We identify two critical challenges in solving our problem: i) it remains to be a fundamental challenge on how to effectively train a reliable DDA model given the lack of sufficient damage severity labels; ii) it is a difficult task to capture the excessive and fine-grained damage-related features in each image for accurate damage assessment. In this paper, we propose ContrastDDA, a deep contrastive learning approach to address the extremely-sparse DDA problem by designing an integrated contrastive and augmentative neural network architecture for accurate disaster damage assessment using the extremely sparse training samples. The evaluation results on two real-world DDA applications demonstrate that ContrastDDA clearly outperforms state-of-the-art deep learning and semi-supervised learning baselines with the highest DDA accuracy under different application scenarios.

Original languageEnglish (US)
Title of host publicationProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
EditorsMichele Coscia, Alfredo Cuzzocrea, Kai Shu
PublisherAssociation for Computing Machinery
Pages151-158
Number of pages8
ISBN (Electronic)9781450391283
DOIs
StatePublished - Nov 8 2021
Event13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 - Virtual, Online, Netherlands
Duration: Nov 8 2021 → …

Publication series

NameProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021

Conference

Conference13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
Country/TerritoryNetherlands
CityVirtual, Online
Period11/8/21 → …

ASJC Scopus subject areas

  • Computer Networks and Communications
  • General Social Sciences
  • Computer Science Applications
  • Software

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