# Updating Street Maps using Changes Detected in Satellite Imagery

arXiv: https://arxiv.org/abs/2110.06456 · HTML: https://mlsanity.com/paper/2110.06456/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [Topological Map Extraction from Overhead Images](https://mlsanity.com/paper/1812.01497/) (2018)
- [Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding](https://mlsanity.com/paper/2007.09547/) (2020)
- [Neural Turtle Graphics for Modeling City Road Layouts](https://mlsanity.com/paper/1910.02055/) (2019)
- [Joint Segmentation and Path Classification of Curvilinear Structures](https://mlsanity.com/paper/1905.03892/) (2019)
- [Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks](https://mlsanity.com/paper/1812.05815/) (2018)
- [COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference](https://mlsanity.com/paper/1909.11048/) (2019)
- [A Comparison and Evaluation of Map Construction Algorithms](https://mlsanity.com/paper/1402.5138/) (2014)
- [Machine-Assisted Map Editing](https://mlsanity.com/paper/1906.07138/) (2019)

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

- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [LT-Gaussian: Long-Term Map Update Using 3D Gaussian Splatting for Autonomous Driving](https://mlsanity.com/paper/2508.01704/) (2025)
- [OSMGen: Highly Controllable Satellite Image Synthesis using OpenStreetMap Data](https://mlsanity.com/paper/2511.00345/) (2025)

## Abstract (arXiv)

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.