# Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language Grounding Tasks in Street View

arXiv: https://arxiv.org/abs/2001.03671 · HTML: https://mlsanity.com/paper/2001.03671/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

## Built on by (most cited first, 18 of 18)

- [Episodic Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2105.06453/) (2021)
- [Pathdreamer: A World Model for Indoor Navigation](https://mlsanity.com/paper/2105.08756/) (2021)
- [Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2109.15207/) (2021)
- [A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning](https://mlsanity.com/paper/2210.03112/) (2022)
- [Improving Vision-and-Language Navigation by Generating Future-View Image Semantics](https://mlsanity.com/paper/2304.04907/) (2023)
- [On the Evaluation of Vision-and-Language Navigation Instructions](https://mlsanity.com/paper/2101.10504/) (2021)
- [Diagnosing Vision-and-Language Navigation: What Really Matters](https://mlsanity.com/paper/2103.16561/) (2021)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2106.13948/) (2021)
- [Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions](https://mlsanity.com/paper/2408.04168/) (2024)
- [Controllable Navigation Instruction Generation with Chain of Thought Prompting](https://mlsanity.com/paper/2407.07433/) (2024)
- [NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2411.08579/) (2024)
- [VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation](https://mlsanity.com/paper/2402.03561/) (2024)
- [SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark](https://mlsanity.com/paper/2110.10661/) (2021)
- [PanGEA: The Panoramic Graph Environment Annotation Toolkit](https://mlsanity.com/paper/2103.12703/) (2021)
- [A Joint Study of Phrase Grounding and Task Performance in Vision and Language Models](https://mlsanity.com/paper/2309.02691/) (2023)
- [AIGeN: An Adversarial Approach for Instruction Generation in VLN](https://mlsanity.com/paper/2404.10054/) (2024)
- [GROKE: Vision-Free Navigation Instruction Evaluation via Graph Reasoning on OpenStreetMap](https://mlsanity.com/paper/2601.07375/) (2026)
- [Core Challenges in Embodied Vision-Language Planning](https://mlsanity.com/paper/2304.02738/) (2023)

## Abstract (arXiv)

The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given location. To enable the wider research community to work effectively with the Touchdown tasks, we are publicly releasing the 29k raw Street View panoramas needed for Touchdown. We follow the process used for the StreetLearn data release (Mirowski et al., 2019) to check panoramas for personally identifiable information and blur them as necessary. These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn. We also provide a reference implementation for both of the Touchdown tasks: vision and language navigation (VLN) and spatial description resolution (SDR). We compare our model results to those given in Chen et al. (2019) and show that the panoramas we have added to StreetLearn fully support both Touchdown tasks and can be used effectively for further research and comparison.