A Step towards Automated and Generalizable Tactile Map Generation using Generative Adversarial Networks

TL;DR

This paper presents a novel GAN-based approach to automate tactile map generation from street-view images, demonstrating high accuracy and generalization across scales and regions, addressing limitations of existing tools.

Contribution

It introduces the first dataset of tactile maps from Google Street View and develops a GAN model capable of identifying, removing, and inpainting map features for tactile map creation.

Findings

GAN models achieve median F1 and IoU scores > 0.97

Models trained on one zoom level perform well on others

The approach generalizes across different map scales and regions

Abstract

Blindness and visual impairments affect many people worldwide. For help with navigation, people with visual impairments often rely on tactile maps that utilize raised surfaces and edges to convey information through touch. Although these maps are helpful, they are often not widely available and current tools to automate their production have similar limitations including only working at certain scales, for particular world regions, or adhering to specific tactile map standards. To address these shortcomings, we train a proof-of-concept model as a first step towards applying computer vision techniques to help automate the generation of tactile maps. We create a first-of-its-kind tactile maps dataset of street-views from Google Maps spanning 6500 locations and including different tactile line- and area-like features. Generative adversarial network (GAN) models trained on a single zoom successfully identify key map elements, remove extraneous ones, and perform inpainting with median F1 and intersection-over-union (IoU) scores of better than 0.97 across all features. Models trained on two zooms experience only minor drops in performance, and generalize well both to unseen map scales and world regions. Finally, we discuss future directions towards a full implementation of a tactile map solution that builds on our results.