# Are We Ready for Unmanned Surface Vehicles in Inland Waterways? The USVInland Multisensor Dataset and Benchmark

arXiv: https://arxiv.org/abs/2103.05383 · HTML: https://mlsanity.com/paper/2103.05383/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2021.

## Built on by (most cited first, 21 of 21)

- [WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces](https://mlsanity.com/paper/2307.06505/) (2023)
- [M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots](https://mlsanity.com/paper/2112.13659/) (2021)
- [Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future](https://mlsanity.com/paper/2312.03408/) (2023)
- [LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark](https://mlsanity.com/paper/2308.09618/) (2023)
- [WaterVG: Waterway Visual Grounding based on Text-Guided Vision and mmWave Radar](https://mlsanity.com/paper/2403.12686/) (2024)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior](https://mlsanity.com/paper/2404.07176/) (2024)
- [NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar](https://mlsanity.com/paper/2408.17207/) (2024)
- [Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection](https://mlsanity.com/paper/2509.20745/) (2025)
- [USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways](https://mlsanity.com/paper/2506.18737/) (2025)
- [SeePerSea: Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles](https://mlsanity.com/paper/2404.18411/) (2024)
- [MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application](https://mlsanity.com/paper/2412.03887/) (2024)
- [PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System](https://mlsanity.com/paper/2206.07468/) (2022)
- [Camera-LiDAR Fusion with Latent Contact for Place Recognition in Challenging Cross-Scenes](https://mlsanity.com/paper/2310.10371/) (2023)
- [Evaluation of Polarimetric Fusion for Semantic Segmentation in Aquatic Environments](https://mlsanity.com/paper/2509.24731/) (2025)
- [MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic segmentation](https://mlsanity.com/paper/2512.17450/) (2025)
- [Learning with Weak Annotations for Robust Maritime Obstacle Detection](https://mlsanity.com/paper/2206.13263/) (2022)
- [A Lightweight Target-Driven Network of Stereo Matching for Inland Waterways](https://mlsanity.com/paper/2410.07915/) (2024)
- [Da Yu: Towards USV-Based Image Captioning for Waterway Surveillance and Scene Understanding](https://mlsanity.com/paper/2506.19288/) (2025)
- [The Coastline as a Structural Constraint: Harnessing Scene Geometry for Autonomous Surface Vessel Localization](https://mlsanity.com/paper/2608.21276/) (2026)

## Abstract (arXiv)

Unmanned surface vehicles (USVs) have great value with their ability to execute hazardous and time-consuming missions over water surfaces. Recently, USVs for inland waterways have attracted increasing attention for their potential application in autonomous monitoring, transportation, and cleaning. However, unlike sailing in open water, the challenges posed by scenes of inland waterways, such as the complex distribution of obstacles, the global positioning system (GPS) signal denial environment, the reflection of bank-side structures, and the fog over the water surface, all impede USV application in inland waterways. To address these problems and stimulate relevant research, we introduce USVInland, a multisensor dataset for USVs in inland waterways. The collection of USVInland spans a trajectory of more than 26 km in diverse real-world scenes of inland waterways using various modalities, including lidar, stereo cameras, millimeter-wave radar, GPS, and inertial measurement units (IMUs). Based on the requirements and challenges in the perception and navigation of USVs for inland waterways, we build benchmarks for simultaneous localization and mapping (SLAM), stereo matching, and water segmentation. We evaluate common algorithms for the above tasks to determine the influence of unique inland waterway scenes on algorithm performance. Our dataset and the development tools are available online at https://www.orca-tech.cn/datasets.html.