GeBDA: Building Damage Assessment as Text-Based Sequence Prediction

Olivier Dietrich; Krishna Sapkota; Konrad Schindler; Genady Beryozkin

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arXiv · 2608.28567

GeBDA: Building Damage Assessment as Text-Based Sequence Prediction

Olivier Dietrich·Krishna Sapkota·Konrad Schindler·Genady Beryozkin

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Abstract

Conventionally, Building Damage Assessment (BDA) is tackled either with dedicated network architectures or by fine-tuning geospatial image foundation models. In this work, we ask whether a general-purpose Vision-Language Model (VLM) can localize buildings and grade their damage through autoregressive sequence generation alone. We cast BDA as predicting a variable-length set of bounding boxes, each specified by its coordinates and a damage label. Our preliminary implementation, based on the open Gemma model, achieves promising damage mapping results from only bi-temporal satellite images and a suitable text prompt.

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BibTeX RIS

Olivier Dietrich, Krishna Sapkota, Konrad Schindler, Genady Beryozkin. 2026-08-28. GeBDA: Building Damage Assessment as Text-Based Sequence Prediction. https://arxiv.org/abs/2608.28567

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