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arXiv Computer Vision By Olivier Dietrich, Krishna Sapkota, Konrad Schindler, Genady Beryozkin

GeBDA: Building Damage Assessment as Text-Based Sequence Prediction

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The paper proposes GeBDA, a method that treats Building Damage Assessment as a text-based sequence prediction task using a Vision‑Language Model. Instead of specialized architectures, it uses an autoregressive VLM to output a variable‑length list of bounding boxes with coordinates and damage labels. The authors demonstrate promising results on bi‑temporal satellite images using the open Gemma model and a tailored text prompt.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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