A Lightweight Transfer Learning-Based Model for Building Classification in Aerial Imagery

Jacob Herman*, Rami Zewail, Tetsuji Ogawa, Samir Elsagheer

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Over the past decade, there has been a growing interest in the potential of artificial intelligence and computerr vision in tackling challenges related to disaster resilience in urban communities. Unmanned aerial imagery has been the focal of a number of initiatives targeting urban planning and aftermath disaster assessment. Within this context, this presents a novel lightweight transfer learning-based model for assessment of building conditions from aerial images. The proposed method is suitable for EDGE-based operations in resource-limited settings. Experiments were conducted to identify post-flooding building conditions in Zanzibar city in Tanzania. Considerable gains in terms of memory and computation time have been achieved while maintaining accuracies that are in line with state-of-Art approaches.

本文言語English
ホスト出版物のタイトル2023 15th International Conference on Computer Research and Development, ICCRD 2023
出版社Institute of Electrical and Electronics Engineers Inc.
ページ181-186
ページ数6
ISBN(電子版)9781665487504
DOI
出版ステータスPublished - 2023
イベント15th International Conference on Computer Research and Development, ICCRD 2023 - Virtual, Online, China
継続期間: 2023 1月 102023 1月 12

出版物シリーズ

名前2023 15th International Conference on Computer Research and Development, ICCRD 2023

Conference

Conference15th International Conference on Computer Research and Development, ICCRD 2023
国/地域China
CityVirtual, Online
Period23/1/1023/1/12

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 計算数学
  • 制御と最適化
  • モデリングとシミュレーション

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