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- W4361791222 abstract "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." @default.
- W4361791222 created "2023-04-05" @default.
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- W4361791222 date "2023-01-10" @default.
- W4361791222 modified "2023-10-14" @default.
- W4361791222 title "A Lightweight Transfer Learning-Based Model for Building Classification in Aerial Imagery" @default.
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- W4361791222 doi "https://doi.org/10.1109/iccrd56364.2023.10080643" @default.
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