# GO: The Great Outdoors Multimodal Dataset

Source: [GO: The Great Outdoors Multimodal Dataset](<https://www.sota2.com/research/paper/68fa76c41e209131cea6ee63>)

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

Data updated: 2026-06-27T05:00:11.922Z

Authors: Peng Jiang, Kasi Viswanath, Akhil Nagariya, George Chustz, Maggie Wigness, Philip Osteen, Timothy Overbye, Christian Ellis, Long Quang, Jia Huang, Srikanth Saripalli

Published: 2025-01-31T16:37:09.000Z

## Abstract

The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. Existing off-road datasets often lack sensor diversity and exclude vital modalities like thermal and radar that are critical for operation in degraded conditions (e.g., low visibility or adverse weather). To address these gaps, we introduce a large-scale multimodal off-road dataset with six complementary sensor modalities, along with semantic annotations and GPS traces, to support tasks such as semantic segmentation, object detection, and SLAM. The diverse environmental conditions represented in the dataset present significant real-world challenges, which provide opportunities to develop more robust solutions to support the continued advancement of field robotics, autonomous exploration, and perception systems in natural environments. The dataset can be downloaded at: https://www.unmannedlab.org/the-great-outdoors-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/2501.19274>)

[PDF](<https://arxiv.org/pdf/2501.19274.pdf>)