FenGePad-A Tangible Multi-Prompt Interactive Framework for Deep Dune Segmentation

Interactive segmentation has become critical for efficiently delineating dune boundaries from remote sensing landform images, enabling geographers to iteratively refine model predictions through minimal user guidance. However, geographers report two major challenges when working with existing tools: (1) handling segmentation around ambiguous dune boundaries forces geographers into dense, repetitive clicking, making the interaction tedious and reducing annotation efficiency; (2) conventional desktop-based annotation platforms mainly support sequential, isolated interactions, hindering the smooth, co-located collaboration necessary for dealing with difficult cases. We thus propose FenGePad, a tangible collaborative interactive segmentation framework. It supports flexible prompt types–clicks, polylines, and scribbles–designed to accommodate geographers’ diverse annotation preferences and improve annotation efficiency. To enhance model robustness and generalization, we introduce prompt generation strategies that simulate realistic annotation behaviors of geographers during training. Finally, we instantiate a tablet-based application supporting FenGePad’s tangible annotation and collaboration. Comprehensive experiments demonstrate that FenGePad achieves competitive segmentation performance while effectively improving annotation quality and collaborative efficiency. Our results demonstrate the promise of tangible interactive frameworks for applying deep learning in geographic research.

Authors

• Zheng Jiang
• Haonan Kang
• Zifeng Wu
• Wei Wang 0353
• Gaowei Zhang
• Eerdun Hasi
• Xiaohan Sun
• Yi Wang 0013

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