# HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving

arXiv: https://arxiv.org/abs/2411.17530 · HTML: https://mlsanity.com/paper/2411.17530/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [Hyperspectral City V1.0 Dataset and Benchmark](https://mlsanity.com/paper/1907.10270/) (2019)

## Built on by (most cited first, 12 of 12)

- [Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective](https://mlsanity.com/paper/2412.03630/) (2024)
- [Hyperspectral Adapter for Semantic Segmentation with Vision Foundation Models](https://mlsanity.com/paper/2509.20107/) (2025)
- [Hyperspectral Sensors and Autonomous Driving: Technologies, Limitations, and Opportunities](https://mlsanity.com/paper/2508.19905/) (2025)
- [CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving](https://mlsanity.com/paper/2508.10962/) (2025)
- [Designing DNNs for a trade-off between robustness and processing performance in embedded devices](https://mlsanity.com/paper/2412.03682/) (2024)
- [Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios](https://mlsanity.com/paper/2506.18682/) (2025)
- [DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation](https://mlsanity.com/paper/2504.05122/) (2025)
- [Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation](https://mlsanity.com/paper/2603.26528/) (2026)
- [HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone](https://mlsanity.com/paper/2605.17286/) (2026)
- [Optimization of DNN-based HSI Segmentation FPGA-based SoC for ADS: A Practical Approach](https://mlsanity.com/paper/2507.16556/) (2025)
- [Challenges in Hyperspectral Imaging for Autonomous Driving: The HSI-Drive Case](https://mlsanity.com/paper/2603.25510/) (2026)
- [Perception Beyond the Visible: Incompleteness-Aware Scene Understanding for Robot Autonomy](https://mlsanity.com/paper/2610.18670/) (2026)

## Abstract (arXiv)

We present the updated version of the HSI-Drive dataset aimed at developing automated driving systems (ADS) using hyperspectral imaging (HSI). The v2.0 version includes new annotated images from videos recorded during winter and fall in real driving scenarios. Added to the spring and summer images included in the previous v1.1 version, the new dataset contains 752 images covering the four seasons. In this paper, we show the improvements achieved over previously published results obtained on the v1.1 dataset, showcasing the enhanced performance of models trained on the new v2.0 dataset. We also show the progress made in comprehensive scene understanding by experimenting with more capable image segmentation models. These models include new segmentation categories aimed at the identification of essential road safety objects such as the presence of vehicles and road signs, as well as highly vulnerable groups like pedestrians and cyclists. In addition, we provide evidence of the performance and robustness of the models when applied to segmenting HSI video sequences captured in various environments and conditions. Finally, for a correct assessment of the results described in this work, the constraints imposed by the processing platforms that can sensibly be deployed in vehicles for ADS must be taken into account. Thus, and although implementation details are out of the scope of this paper, we focus our research on the development of computationally efficient, lightweight ML models that can eventually operate at high throughput rates. The dataset and some examples of segmented videos are available in https://ipaccess.ehu.eus/HSI-Drive/.