# The GOOSE Dataset for Perception in Unstructured Environments

arXiv: https://arxiv.org/abs/2310.16788 · HTML: https://mlsanity.com/paper/2310.16788/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 17 of 17)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution](https://mlsanity.com/paper/2007.16100/) (2020)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [MSeg: A Composite Dataset for Multi-domain Semantic Segmentation](https://mlsanity.com/paper/2112.13762/) (2021)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2206.02099/) (2022)
- [TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models](https://mlsanity.com/paper/2205.01791/) (2022)
- [PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model](https://mlsanity.com/paper/2204.02681/) (2022)
- [ORFD: A Dataset and Benchmark for Off-Road Freespace Detection](https://mlsanity.com/paper/2206.09907/) (2022)
- [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://mlsanity.com/paper/2101.04431/) (2021)
- [A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios](https://mlsanity.com/paper/2103.13109/) (2021)
- [Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups](https://mlsanity.com/paper/1705.04085/) (2017)
- [LiDAR-as-Camera for End-to-End Driving](https://mlsanity.com/paper/2206.15170/) (2022)

## Built on by (most cited first, 30 of 37)

- [TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation](https://mlsanity.com/paper/2505.10696/) (2025)
- [Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentation](https://mlsanity.com/paper/2409.18788/) (2024)
- [Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks](https://mlsanity.com/paper/2510.16500/) (2025)
- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision](https://mlsanity.com/paper/2411.18025/) (2024)
- [ROVER: A Multi-Season Dataset for Visual SLAM](https://mlsanity.com/paper/2412.02506/) (2024)
- [Survey on Datasets for Perception in Unstructured Outdoor Environments](https://mlsanity.com/paper/2404.18750/) (2024)
- [From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments](https://mlsanity.com/paper/2605.29565/) (2026)
- [DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads](https://mlsanity.com/paper/2404.09622/) (2024)
- [Bench2FreeAD: A Benchmark for Vision-based End-to-end Navigation in Unstructured Robotic Environments](https://mlsanity.com/paper/2503.12180/) (2025)
- [Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding](https://mlsanity.com/paper/2505.06991/) (2025)
- [Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Boosting Off-Road Segmentation via Photometric Distortion and Exponential Moving Average](https://mlsanity.com/paper/2505.11769/) (2025)
- [TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions](https://mlsanity.com/paper/2506.21630/) (2025)
- [How to Relieve Distribution Shifts in Semantic Segmentation for Off-Road Environments](https://mlsanity.com/paper/2605.29599/) (2026)
- [Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive Perspective](https://mlsanity.com/paper/2409.04086/) (2024)
- [IRisPath: Enhancing Costmap for Off-Road Navigation with Robust IR-RGB Fusion for Improved Day and Night Traversability](https://mlsanity.com/paper/2412.03173/) (2024)
- [3DTTNet: Multimodal Fusion-Based 3D Traversable Terrain Modeling for Off-Road Environments](https://mlsanity.com/paper/2412.08195/) (2024)
- [Knowledge Distillation for Semantic Segmentation: A Label Space Unification Approach](https://mlsanity.com/paper/2502.19177/) (2025)
- [Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems](https://mlsanity.com/paper/2506.06995/) (2025)
- [Robotics Under Construction: Challenges on Job Sites](https://mlsanity.com/paper/2506.19597/) (2025)
- [Diffusion-Based Image Augmentation for Semantic Segmentation in Outdoor Robotics](https://mlsanity.com/paper/2507.00153/) (2025)
- [SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests](https://mlsanity.com/paper/2510.09458/) (2025)
- [FORWARD: Dataset of a forwarder operating in rough terrain](https://mlsanity.com/paper/2511.17318/) (2025)
- [A Cross-Scale Decoder with Token Refinement for Off-Road Semantic Segmentation](https://mlsanity.com/paper/2603.27931/) (2026)
- [Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark](https://mlsanity.com/paper/2604.27499/) (2026)
- [GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain](https://mlsanity.com/paper/2606.15937/) (2026)
- [Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Leveraging DINOv3 for Robust Outdoor Scene Understanding in Field Robotics](https://mlsanity.com/paper/2606.18582/) (2026)
- [SAM3 Self-Distillation for Fine-Grained GOOSE 2D Semantic Segmentation](https://mlsanity.com/paper/2606.20130/) (2026)
- [Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Exploring Query-Based Segmentation and Increased Spatial Context for Outdoor Scene Understanding](https://mlsanity.com/paper/2606.21456/) (2026)
- [Technical Report for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Pretraining-Diverse Ensemble of Foundation Vision Encoders for Robust Outdoor Scene Understanding](https://mlsanity.com/paper/2606.23113/) (2026)

## Abstract (arXiv)

The potential for deploying autonomous systems can be significantly increased by improving the perception and interpretation of the environment. However, the development of deep learning-based techniques for autonomous systems in unstructured outdoor environments poses challenges due to limited data availability for training and testing. To address this gap, we present the German Outdoor and Offroad Dataset (GOOSE), a comprehensive dataset specifically designed for unstructured outdoor environments. The GOOSE dataset incorporates 10 000 labeled pairs of images and point clouds, which are utilized to train a range of state-of-the-art segmentation models on both image and point cloud data. We open source the dataset, along with an ontology for unstructured terrain, as well as dataset standards and guidelines. This initiative aims to establish a common framework, enabling the seamless inclusion of existing datasets and a fast way to enhance the perception capabilities of various robots operating in unstructured environments. The dataset, pre-trained models for offroad perception, and additional documentation can be found at https://goose-dataset.de/.