# Caltech Aerial RGB-Thermal Dataset in the Wild

Source: [Caltech Aerial RGB-Thermal Dataset in the Wild](<https://www.sota2.com/research/paper/68fa744e1e209131cea60e73>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:37:02.192Z

Authors: Connor Lee, Matthew Anderson, Nikhil Raganathan, Xingxing Zuo, Kevin Do, Georgia Gkioxari, Soon-Jo Chung

Published: 2024-03-13T23:31:04.000Z

## Abstract

We present the first publicly-available RGB-thermal dataset designed for aerial robotics operating in natural environments. Our dataset captures a variety of terrain across the United States, including rivers, lakes, coastlines, deserts, and forests, and consists of synchronized RGB, thermal, global positioning, and inertial data. We provide semantic segmentation annotations for 10 classes commonly encountered in natural settings in order to drive the development of perception algorithms robust to adverse weather and nighttime conditions. Using this dataset, we propose new and challenging benchmarks for thermal and RGB-thermal (RGB-T) semantic segmentation, RGB-T image translation, and motion tracking. We present extensive results using state-of-the-art methods and highlight the challenges posed by temporal and geographical domain shifts in our data. The dataset and accompanying code is available at https://github.com/aerorobotics/caltech-aerial-rgbt-dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.08997>)

[PDF](<https://arxiv.org/pdf/2403.08997.pdf>)