Updating Street Maps using Changes Detected in Satellite Imagery

TL;DR

This paper presents a novel satellite imagery change detection method that significantly improves the accuracy of updating existing digital street maps, reducing errors four-fold compared to previous techniques.

Contribution

The paper introduces a change-based approach leveraging temporal satellite imagery to enhance map update accuracy, addressing limitations of existing map extraction methods.

Findings

Reduces map update error rates four-fold

Effectively detects physical road network changes over time

Improves accuracy of existing map update processes

Abstract

Accurately maintaining digital street maps is labor-intensive. To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps. An end-to-end map update system would first process geospatial data sources to extract insights, and second leverage those insights to update and improve the map. However, prior work largely focuses on the first step of this pipeline: these map extraction methods infer road networks from scratch given geospatial data sources (in effect creating entirely new maps), but do not address the second step of leveraging this extracted information to update the existing digital map data. In this paper, we first explain why current map extraction techniques yield low accuracy when extended to update existing maps. We then propose a novel method that leverages the progression of satellite imagery over time to substantially improve accuracy. Our approach first compares satellite images captured at different times to identify portions of the physical road network that have visibly changed, and then updates the existing map accordingly. We show that our change-based approach reduces map update error rates four-fold.