# AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and Autonomous Operations

arXiv: https://arxiv.org/abs/2409.06856 · HTML: https://mlsanity.com/paper/2409.06856/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2102.11585/) (2021)
- [Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI](https://mlsanity.com/paper/2005.07173/) (2020)
- [Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study](https://mlsanity.com/paper/2302.04634/) (2023)
- [BARS: A Benchmark for Airport Runway Segmentation](https://mlsanity.com/paper/2210.12922/) (2022)
- [Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios](https://mlsanity.com/paper/2401.06019/) (2024)

## Built on by (most cited first, 2 of 2)

- [Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches](https://mlsanity.com/paper/2501.18494/) (2025)
- [Adapt, But Don't Forget: Fine-Tuning and Contrastive Routing for Lane Detection under Distribution Shift](https://mlsanity.com/paper/2507.18653/) (2025)

## Abstract (arXiv)

The availability of high-quality datasets play a crucial role in advancing research and development especially, for safety critical and autonomous systems. In this paper, we present AssistTaxi, a comprehensive novel dataset which is a collection of images for runway and taxiway analysis. The dataset comprises of more than 300,000 frames of diverse and carefully collected data, gathered from Melbourne (MLB) and Grant-Valkaria (X59) general aviation airports. The importance of AssistTaxi lies in its potential to advance autonomous operations, enabling researchers and developers to train and evaluate algorithms for efficient and safe taxiing. Researchers can utilize AssistTaxi to benchmark their algorithms, assess performance, and explore novel approaches for runway and taxiway analysis. Addition-ally, the dataset serves as a valuable resource for validating and enhancing existing algorithms, facilitating innovation in autonomous operations for aviation. We also propose an initial approach to label the dataset using a contour based detection and line extraction technique.