来自太空的语义：面向空中野外机器人的卫星引导热红外语义分割标注
Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
AI总结：
提出利用卫星数据和机载定位姿态估计自动生成热红外图像语义分割标注的方法，结合视觉基础模型实现低成本高精度标注，性能接近高分辨率选项且显著优于零样本方法。
摘要：
我们提出了一种新方法，通过利用卫星衍生数据产品以及机载全球定位和姿态估计，自动为飞行器捕获的热红外图像生成语义分割标注。由于缺乏标注的热红外野外数据集以及人工标注的时间与成本，野外机器人的热红外语义感知算法开发面临挑战，这一新能力克服了该挑战，实现了以大规模可并行化的规模对野外采集的热红外数据进行精确且快速的标注。通过将热红外条件优化步骤与视觉基础模型相结合，我们的方法能够利用低分辨率卫星土地覆盖数据，以极低甚至零成本生成高精度的语义分割标签。它达到了使用昂贵高分辨率选项98.5%的性能，并且相较于目前用于为RGB图像生成标注的、基于大型视觉语言模型的流行零样本语义分割方法，展现出70-160%的提升。代码将发布于：https://github.com/connorlee77/aerial-auto-segment。
英文摘要：
We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products alongside onboard global positioning and attitude estimates. This new capability overcomes the challenge of developing thermal semantic perception algorithms for field robots due to the lack of annotated thermal field datasets and the time and costs of manual annotation, enabling precise and rapid annotation of thermal data from field collection efforts at a massively-parallelizable scale. By incorporating a thermal-conditioned refinement step with visual foundation models, our approach can produce highly-precise semantic segmentation labels using low-resolution satellite land cover data for little-to-no cost. It achieves 98.5% of the performance from using costly high-resolution options and demonstrates between 70-160% improvement over popular zero-shot semantic segmentation methods based on large vision-language models currently used for generating annotations for RGB imagery. Code will be available at: https://github.com/connorlee77/aerial-auto-segment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
