Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This study introduces DebrisHeightNet, a segmentation-conditioned monocular debris-height network that estimates debris volume from a single aerial RGB image captured after hurricanes. By training a lightweight model on top of frozen vision foundation models and employing a novel confidence-weighted LiDAR-monocular fusion approach for target synthesis, the method achieves a high correlation with independent UAV surveys and significantly outperforms traditional parametric forecasts. The model's deployment is streamlined, requiring no additional ground access or LiDAR, making it a practical tool for rapid post-hurricane assessments.
DebrisHeightNet achieves 30% accuracy in debris volume estimation from single aerial images, outperforming traditional methods that overestimate by up to 4.8 times.
Hurricane debris removal is planned, contracted, and federally reimbursed on the basis of volume estimates, yet operational practice still relies on parametric forecasts with 41-90% documented over-estimation or on truck-load tallies that arrive only after hauling begins. We present DebrisHeightNet, a segmentation-conditioned monocular debris-height network that estimates spatially explicit debris volume from a single pass of post-event aerial RGB imagery, the kind of survey routinely flown within days of a hurricane landfall. We train only a lightweight 1.08 M-parameter head on top of two frozen vision foundation models. This head regresses height from a Depth Anything V2 backbone, conditioned on the debris segmentation of CLIPSeg-debris from our prior work. Because no post-hurricane debris-height ground truth exists, we synthesize the training target by confidence-weighted LiDAR-monocular fusion (CW-LMF), designed to suppress non-debris LiDAR returns. This fused target is a constructed supervision signal rather than ground truth, so we corroborate it against external references rather than claiming it as truth. A region-level power-law calibration, driven by each region's low-density debris fraction, converts model volume into an estimate of the reported hauled debris with quantified uncertainty. Across ten regions spanning five hurricanes and three states, the uncalibrated model agrees with an independent uncrewed-aerial-vehicle (UAV) survey of the training region at Spearman $\rho = 0.87$ and lands within 30% of the reported record where the Hazus and FEMA-hybrid parametric forecasts over-predict it by 2.7-4.8$\times$. Deployment requires no LiDAR, no ground access, and no second flight, so the method can produce spatially explicit volume estimates wherever single-pass post-event imagery is flown.