  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.
