English(EN) LoDEOT: Low-Dimensional and Efficient Offset Tokens for Building Footprint Extraction from Off-Nadir Imagery

新的LoDEOT方法改进了从影像中提取建筑物轮廓的精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了LoDEOT，一种从倾斜遥感影像中提取建筑物轮廓的新颖方法。该方法利用紧凑的五维偏移量化标记，而不是高维实例标记，可以通过端到端训练学习与任务相关的表示。在多个数据集上的实验表明，LoDEOT的有效性，与现有的端到端方法相比，在屋顶检测和建筑物轮廓校正指标上取得了优越的性能。 AI

影响引入了一种从影像中提取建筑物轮廓的更有效方法，有望改进地理空间分析。

排序理由该集群包含一篇详细介绍图像分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv cs.CV TIER_1 English(EN) · Kai Li, Zigan Zhou, Zhenyang Li, Hui Shan, Zhe Chen, Yupeng Deng, Zhihao Xi, Yu Meng, Yifan Peng, Xiangyu Zhao · 2026-10-07 04:00

LoDEOT：用于从天底外影像中提取建筑物足迹的低维高效偏移量标记

arXiv:2610.05899v2 Announce Type: replace Abstract: Instance-level roof-to-footprint offset (RFO) prediction is central to extracting building footprints from off-nadir imagery. Query-based pipelines commonly use high-dimensional instance tokens to predict signed two-dimensional …