This analysis demonstrates improved damage assessments using satellite imagery in underdeveloped regions, suggesting ViTs may enhance classification accuracy.
Key Points
This research aims to enhance building damage assessment following earthquakes utilizing advanced imaging techniques.
Utilized high-resolution satellite imagery
Employed Vision Transformers for damage classification
Compared effectiveness against CNN models like ResNet50 and Inception-V3
Analyzed real-world earthquake data from Ludian and Yushu
Proposed a new architecture incorporating a CNN-based Inception module
ViT model showed improved accuracy in assessing rural building damage
Demonstrated better generalization across various earthquake scenarios