# Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities

arXiv: https://arxiv.org/abs/2206.13677 · HTML: https://mlsanity.com/paper/2206.13677/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Hierarchical Multi-Scale Attention for Semantic Segmentation](https://mlsanity.com/paper/2005.10821/) (2020)
- [ParseNet: Looking Wider to See Better](https://mlsanity.com/paper/1506.04579/) (2015)
- [Attention to Scale: Scale-aware Semantic Image Segmentation](https://mlsanity.com/paper/1511.03339/) (2015)
- [Improving Semantic Segmentation via Video Propagation and Label Relaxation](https://mlsanity.com/paper/1812.01593/) (2018)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [City-Scale Road Extraction from Satellite Imagery v2: Road Speeds and Travel Times](https://mlsanity.com/paper/1908.09715/) (2019)
- [Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition](https://mlsanity.com/paper/1706.06169/) (2017)
- [CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials](https://mlsanity.com/paper/2201.02260/) (2022)
- [Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data](https://mlsanity.com/paper/2008.13321/) (2020)
- [Sidewalk Measurements from Satellite Images: Preliminary Findings](https://mlsanity.com/paper/2112.06120/) (2021)

## Built on by (most cited first, 3 of 3)

- [StreetReaderAI: Making Street View Accessible Using Context-Aware Multimodal AI](https://mlsanity.com/paper/2508.08524/) (2025)
- [Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision](https://mlsanity.com/paper/2210.02533/) (2022)
- [Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces](https://mlsanity.com/paper/2402.17169/) (2024)

## Abstract (arXiv)

There is a lack of data on the location, condition, and accessibility of sidewalks across the world, which not only impacts where and how people travel but also fundamentally limits interactive mapping tools and urban analytics. In this paper, we describe initial work in semi-automatically building a sidewalk network topology from satellite imagery using hierarchical multi-scale attention models, inferring surface materials from street-level images using active learning-based semantic segmentation, and assessing sidewalk condition and accessibility features using Crowd+AI. We close with a call to create a database of labeled satellite and streetscape scenes for sidewalks and sidewalk accessibility issues along with standardized benchmarks.