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.

翻译：我们提出一种新方法，通过利用卫星衍生数据产品并结合机载全球定位与姿态估计，为空中飞行器采集的热红外图像自动生成语义分割标注。该新能力克服了因缺乏标注热红外场地数据集以及人工标注耗时耗资而导致的场地机器人热语义感知算法开发难题，能够以大规模并行化方式对实地采集的热红外数据进行精确快速标注。通过引入热条件化的精炼步骤与视觉基础模型，我们的方法可利用低分辨率卫星土地覆盖数据以近乎零成本生成高精度语义分割标签。该方法达到了使用昂贵高分辨率选项时性能的98.5%，并在当前用于RGB图像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基础上实现了70%-160%的性能提升。代码将发布于：https://github.com/connorlee77/aerial-auto-segment