Post-Disaster Building Damage Assessment: Multi-Class Object Detection vs. Object Localization and Classification

Highlights: What are the main findings? Separating building localization from damage classification outperforms multi-class detection on unmanned aerial vehicle (UAV) orthomosaics of post-cyclone Mozambique and provides stronger single-class localization. Transfer learning remains consistently beneficial despite the domain gap between COCO/ImageNet and UAV disaster imagery, indicating that generic mid-level features transfer well to this task. What are the implications of the main findings? A modular two-stage pipeline improves robustness and adaptability for real-world deployments, enabling independent optimization, rapid component swaps, and straightforward extension to new damage taxonomies, geographies, or sensing conditions without full retraining. By strengthening localization and dedicating classification, the approach better handles challenging, imbalanced classes (e.g., destroyed buildings) and shows improved robustness on non-Western, underrepresented built environments - facilitating more reliable, scalable post-disaster assessment across diverse global contexts. Natural disasters demand swift and accurate impact assessment, yet traditional field-based methods remain prohibitively slow. While semi-automatic techniques leveraging remote sensing and drone imagery have accelerated evaluations, existing datasets predominantly emphasize Western infrastructure, offering limited representation of African contexts. The EDDA dataset (a Mozambique post-disaster building damage dataset developed under the Efficient Humanitarian Aid Through Intelligent Image Analysis project), addresses this critical gap by capturing rural and urban damage patterns in Mozambique following Cyclone Idai. Despite encouraging early results, significant challenges persist due to task complexity, severe class imbalance, and substantial architectural diversity across regions. Building upon EDDA, this study introduces a two-stage building damage assessment pipeline that decouples localization from classification. We employ lightweight You Only Look Once (YOLO)-based detectors - RTMDet, YOLOv7, and YOLOv8 - for building localization, followed by dedicated damage severity classification using state-of-the-art architectures including Compact Convolutional Transformers, EfficientNet, and ResNet. This approach tests whether separating feature extraction tasks - assigning detectors solely to localization and specialized classifiers to damage assessment - yields superior performance compared to multi-class detection models that jointly learn both objectives. Comprehensive evaluation across 640+ model combinations demonstrates that our two-stage pipeline achieves competitive performance (mAP 0.478) with enhanced modularity compared to multi-class detection baselines (mAP 0.455), offering improved robustness across diverse building types and imbalanced damage classes.