Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
This enables precise and low-cost thermal semantic perception for aerial field robots, addressing a domain-specific bottleneck in robotics.
The paper tackles the lack of annotated thermal datasets for aerial field robots by introducing an automated method to generate semantic segmentation annotations using satellite data and thermal-conditioned refinement, achieving 98.5% of the performance of costly high-resolution options and 70-160% improvement over zero-shot methods.
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.