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In the field of natural hazard assessment, evaluating the vulnerability of exposed elements is essential. Residential buildings are particularly vulnerable to major risks, especially in the context of shrinking and swelling clay soils. However, assessing vulnerability requires extracting their usage type and structural characteristics. Building footprints from IGN \(^{1}\) (French National Institute of Geographic and Forest Information https://www.ign.fr/ ) BD TOPO provide incomplete data on their usage. This is obvious in our study area situated in the west of Orléans, where 40% of buildings listed by IGN have an unknown usage type due to missing information. By deep learning models and remote sensing data, we were able to resolve this uncertainty with an accuracy of 0.74 and a recall of 0.84, outperforming state of the art models. This marks the initial phase in developing a new processing pipeline capable of automatically extracting building features from multimodal data sources, including ground-level imagery and LiDAR data.

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Improved Fine Grained Classification of Buildings Using Aerial Images and Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Youssef Fouzai,
  • Cécile Gracianne,
  • Yves Lucas,
  • Caterina Negulescu,
  • Gilles Grandjean

摘要

In the field of natural hazard assessment, evaluating the vulnerability of exposed elements is essential. Residential buildings are particularly vulnerable to major risks, especially in the context of shrinking and swelling clay soils. However, assessing vulnerability requires extracting their usage type and structural characteristics. Building footprints from IGN \(^{1}\) (French National Institute of Geographic and Forest Information https://www.ign.fr/ ) BD TOPO provide incomplete data on their usage. This is obvious in our study area situated in the west of Orléans, where 40% of buildings listed by IGN have an unknown usage type due to missing information. By deep learning models and remote sensing data, we were able to resolve this uncertainty with an accuracy of 0.74 and a recall of 0.84, outperforming state of the art models. This marks the initial phase in developing a new processing pipeline capable of automatically extracting building features from multimodal data sources, including ground-level imagery and LiDAR data.