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Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagery

Hao Li, Liwei Zou, Wenping Yin, Gulsen Taskin, Naoto Yokoya, Danfeng Hong, Wufan Zhao

Abstract

Living in a changing climate, human society now faces more frequent and severe natural disasters than ever before. As a consequence, rapid disaster response during the "Golden 72 Hours" of search and rescue becomes a vital humanitarian necessity and community concern. However, traditional disaster damage surveys routinely fail to generalize across distinct urban morphologies and new disaster events. Effective damage mapping typically requires exhaustive and time-consuming manual data annotation. To address this issue, we introduce Smart Transfer, a novel Geospatial Artificial Intelligence (GeoAI) framework, leveraging state-of-the-art vision Foundation Models (FMs) for rapid building damage mapping with post-earthquake Very High Resolution (VHR) imagery. Specifically, we design two novel model transfer strategies: first, Pixel-wise Clustering (PC), ensuring robust prototype-level global feature alignment; second, a Distance-Penalized Triplet (DPT), integrating patch-level spatial autocorrelation patterns by assigning stronger penalties to semantically inconsistent yet spatially adjacent patches. Extensive experiments and ablations from the recent 2023 Turkiye-Syria earthquake show promising performance in multiple cross-region transfer settings, namely Leave One Domain Out (LODO) and Specific Source Domain Combination (SSDC). Moreover, Smart Transfer provides a scalable, automated GeoAI solution to accelerate building damage mapping and support rapid disaster response, offering new opportunities to enhance disaster resilience in climate-vulnerable regions and communities. The data and code are publicly available at https://github.com/ai4city-hkust/SmartTransfer.

Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagery

Abstract

Living in a changing climate, human society now faces more frequent and severe natural disasters than ever before. As a consequence, rapid disaster response during the "Golden 72 Hours" of search and rescue becomes a vital humanitarian necessity and community concern. However, traditional disaster damage surveys routinely fail to generalize across distinct urban morphologies and new disaster events. Effective damage mapping typically requires exhaustive and time-consuming manual data annotation. To address this issue, we introduce Smart Transfer, a novel Geospatial Artificial Intelligence (GeoAI) framework, leveraging state-of-the-art vision Foundation Models (FMs) for rapid building damage mapping with post-earthquake Very High Resolution (VHR) imagery. Specifically, we design two novel model transfer strategies: first, Pixel-wise Clustering (PC), ensuring robust prototype-level global feature alignment; second, a Distance-Penalized Triplet (DPT), integrating patch-level spatial autocorrelation patterns by assigning stronger penalties to semantically inconsistent yet spatially adjacent patches. Extensive experiments and ablations from the recent 2023 Turkiye-Syria earthquake show promising performance in multiple cross-region transfer settings, namely Leave One Domain Out (LODO) and Specific Source Domain Combination (SSDC). Moreover, Smart Transfer provides a scalable, automated GeoAI solution to accelerate building damage mapping and support rapid disaster response, offering new opportunities to enhance disaster resilience in climate-vulnerable regions and communities. The data and code are publicly available at https://github.com/ai4city-hkust/SmartTransfer.

Paper Structure

This paper contains 18 sections, 11 equations, 10 figures, 7 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Study area and data
  3. Study area
  4. Data source
  5. Post-disaster VHR remote sensing data
  6. Pre-disaster building footprint data
  7. Damaged-building annotation benchmark
  8. Methodology
  9. Backbone Foundation Model
  10. Foundation Model Smart Transfer
  11. Evaluation Metrics and Implementations
  12. Experiment
  13. Fully Supervised Baseline
  14. Performance Improvements of Smart Transfer
  15. Towards Building-level Damage Mapping and Deployment
  16. ...and 3 more sections

Figures (10)

  • Figure 1: A conceptual diagram of post-earthquake damage mapping, illustrating the Smart Transfer framework's objective to minimize the rapid response time window for critical disaster response operations.
  • Figure 2: Study area overview displaying the USGS Modified Mercalli Intensity (MMI) shakemap. Post-disaster VHR imagery is provided for the 9 selected urban regions.
  • Figure 3: Selected examples of multiple building damage levels from 9 study regions, from top to bottom row refer to slight damage, heavy damage, buildings requiring demolition, and collapse building, respectively.
  • Figure 4: Post-disaster remote sensing imagery of part of Kahramanmaras (a) post-disaster VHR imagery and (b) with annotated damaged buildings (red) and undamaged buildings (green). Color intensity indicates the damage score of the ground truth annotation.
  • Figure 5: Three Stages of Smart Transfer. Stage 1 is the cold start of foundation models, Stage 2 is the warm-up of foundation models, and Stage 3 is the smart transfer strategy designed for diverse damage mapping scenarios.
  • ...and 5 more figures