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<p>Rapid changes in the human-changed and natural environment have intensified the need for efficient, scalable, and accurate topographic map updates. Traditional methods, which rely on labor-intensive and costly field surveys, are increasingly inadequate due to time and resource constraints. In parallel, advances in remote sensing have greatly improved the availability of high-resolution, multimodal data–though often at the cost of increased complexity and heterogeneity. This study demonstrates that such data, combined with state-of-the-art deep learning techniques, can effectively support the automation of topographic map updates through bi-temporal change detection. Focusing on urban areas where changes are most frequent, we propose a framework that integrates a panoptic segmentation model with a novel change detection algorithm designed to process VNIR orthophotos and classified LiDAR data. The algorithm combines three complementary metrics–semantic class comparison, geometric overlap, and histogram similarity–to categorize detected changes as added, removed, or modified. Experimental validation across 40 synthetic and real-world scenarios confirms the robustness of the approach, with an average processing time of 20 ms per 500 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\text {m}^{2}\)</EquationSource> </InlineEquation> tile using GPU acceleration. Based on the Mask2Former architecture, the segmentation model generates high-quality semantic and instance masks for diverse object classes, achieving 0.86–0.87 mIoU for buildings and roads and 0.94 mIoU for forests and cultivated land. Incorporating LiDAR data improves segmentation performance by 5.9 percentage points. The framework enables computer-aided workflows with user-adjustable thresholds and produces georeferenced raster outputs compatible with GIS analysis. Future work will address segmentation challenges and explore soft classification strategies to enhance interpretability.</p>

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Bi-temporal change detection for topographic map updates using panoptic segmentation of VNIR orthophotos and LiDAR data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月28日

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  • Maciej Adamiak,
  • Krzysztof Będkowski,
  • Marta Nalej,
  • Patryk Ożadowicz,
  • Jacek Pietruk,
  • Szymon Wójcik

摘要

Rapid changes in the human-changed and natural environment have intensified the need for efficient, scalable, and accurate topographic map updates. Traditional methods, which rely on labor-intensive and costly field surveys, are increasingly inadequate due to time and resource constraints. In parallel, advances in remote sensing have greatly improved the availability of high-resolution, multimodal data–though often at the cost of increased complexity and heterogeneity. This study demonstrates that such data, combined with state-of-the-art deep learning techniques, can effectively support the automation of topographic map updates through bi-temporal change detection. Focusing on urban areas where changes are most frequent, we propose a framework that integrates a panoptic segmentation model with a novel change detection algorithm designed to process VNIR orthophotos and classified LiDAR data. The algorithm combines three complementary metrics–semantic class comparison, geometric overlap, and histogram similarity–to categorize detected changes as added, removed, or modified. Experimental validation across 40 synthetic and real-world scenarios confirms the robustness of the approach, with an average processing time of 20 ms per 500 \(\text {m}^{2}\) tile using GPU acceleration. Based on the Mask2Former architecture, the segmentation model generates high-quality semantic and instance masks for diverse object classes, achieving 0.86–0.87 mIoU for buildings and roads and 0.94 mIoU for forests and cultivated land. Incorporating LiDAR data improves segmentation performance by 5.9 percentage points. The framework enables computer-aided workflows with user-adjustable thresholds and produces georeferenced raster outputs compatible with GIS analysis. Future work will address segmentation challenges and explore soft classification strategies to enhance interpretability.