# PathwayBench: Assessing Routability of Pedestrian Pathway Networks Inferred from Multi-City Imagery

arXiv: https://arxiv.org/abs/2407.16875 · HTML: https://mlsanity.com/paper/2407.16875/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Predictive Inequity in Object Detection](https://mlsanity.com/paper/1902.11097/) (2019)
- [HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps](https://mlsanity.com/paper/2106.14880/) (2021)
- [TorontoCity: Seeing the World with a Million Eyes](https://mlsanity.com/paper/1612.00423/) (2016)
- [Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery](https://mlsanity.com/paper/1905.01447/) (2019)
- [APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks](https://mlsanity.com/paper/2303.02323/) (2023)
- [A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data](https://mlsanity.com/paper/2105.08814/) (2021)

## Built on by (most cited first, 4 of 4)

- [Reliable, Routable, and Reproducible: Collection of Pedestrian Pathways at Statewide Scale](https://mlsanity.com/paper/2410.19762/) (2024)
- [Fragments to Facts: Partial-Information Fragment Inference from LLMs](https://mlsanity.com/paper/2505.13819/) (2025)
- [iOSPointMapper: RealTime Pedestrian and Accessibility Mapping with Mobile AI](https://mlsanity.com/paper/2512.22392/) (2025)
- [TraversRL: Traversable Pedestrian Pathway Generation With Reinforcement Learning](https://mlsanity.com/paper/2607.17479/) (2026)

## Abstract (arXiv)

Applications to support pedestrian mobility in urban areas require a complete, and routable graph representation of the built environment. Globally available information, including aerial imagery provides a scalable source for constructing these path networks, but the associated learning problem is challenging: Relative to road network pathways, pedestrian network pathways are narrower, more frequently disconnected, often visually and materially variable in smaller areas, and their boundaries are broken up by driveway incursions, alleyways, marked or unmarked crossings through roadways. Existing algorithms to extract pedestrian pathway network graphs are inconsistently evaluated and tend to ignore routability, making it difficult to assess utility for mobility applications: Even if all path segments are available, discontinuities could dramatically and arbitrarily shift the overall path taken by a pedestrian. In this paper, we describe a first standard benchmark for the pedestrian pathway graph extraction problem, comprising the largest available dataset equipped with manually vetted ground truth annotations (covering 3,000 km2 land area in regions from 8 cities), and a family of evaluation metrics centering routability and downstream utility. By partitioning the data into polygons at the scale of individual intersections, we compute local routability as an efficient proxy for global routability. We consider multiple measures of polygon-level routability and compare predicted measures with ground truth to construct evaluation metrics. Using these metrics, we show that this benchmark can surface strengths and weaknesses of existing methods that are hidden by simple edge-counting metrics over single-region datasets used in prior work, representing a challenging, high-impact problem in computer vision and machine learning.