Insta-BDA: Instance-Aware Building Damage Assessment and Counting via Foundation Model Fusion

Abstract

Accurate building damage assessment from satellite imagery is essential for post-disaster response and recovery. Most deep learning approaches treat this task as semantic segmentation, producing pixel-level damage maps without separating individual buildings. This formulation inherently limits reliable per-building damage quantification, which is often far more informative for operational decision-making. We present Insta-BDA, an instance-aware framework that integrates change detection with foundation-model-based instance segmentation for building-level damage assessment using only pixel-level supervision. The approach combines ChangeMamba for spatiotemporal change detection with SAM3 for zero-shot building instance extraction, reconciled through a confidence-guided fusion mechanism. The framework does not require instance-level annotations (polygons, bounding boxes, or instance masks) for training; instance-level structure is derived automatically from SAM3’s zero-shot detections, and the learnable fusion variant requires only pixel-level damage labels. We investigate two fusion strategies—a rule-based approach (Insta-BDA-RB) and a learnable variant (Insta-BDA-MLP)—using a compact multilayer perceptron on per-instance features. To improve robustness under typical satellite resolutions and annotation variability, we adopt a binary damage formulation. Experiments on xBD show that Insta-BDA reduces the aggregate building count deviation to −34.96%, compared with −47.06% for ChangeMamba, while maintaining competitive damage classification performance. The learnable fusion further improves damage F1 (0.70 vs. 0.67 for rule-based fusion). Cross-dataset evaluation on IAN-BD and IDA-BD indicates improved generalization. These results suggest that integrating foundation model segmentation with change detection offers a practical pathway toward operational, instance-level building damage assessment.