# Beyond Road Extraction: A Dataset for Map Update using Aerial Images

arXiv: https://arxiv.org/abs/2110.04690 · HTML: https://mlsanity.com/paper/2110.04690/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 10 Oct 2021.

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

- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [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)
- [Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery](https://mlsanity.com/paper/1905.01447/) (2019)
- [Joint Segmentation and Path Classification of Curvilinear Structures](https://mlsanity.com/paper/1905.03892/) (2019)
- [COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference](https://mlsanity.com/paper/1909.11048/) (2019)
- [Machine-Assisted Map Editing](https://mlsanity.com/paper/1906.07138/) (2019)

## Built on by (most cited first, 5 of 5)

- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [MapFormer: Boosting Change Detection by Using Pre-change Information](https://mlsanity.com/paper/2303.17859/) (2023)
- [Road Extraction from Overhead Images with Graph Neural Networks](https://mlsanity.com/paper/2112.05215/) (2021)
- [GLD-Road:A global-local decoding road network extraction model for remote sensing images](https://mlsanity.com/paper/2506.09553/) (2025)
- [Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery](https://mlsanity.com/paper/2401.06762/) (2024)

## Abstract (arXiv)

The increasing availability of satellite and aerial imagery has sparked substantial interest in automatically updating street maps by processing aerial images. Until now, the community has largely focused on road extraction, where road networks are inferred from scratch from an aerial image. However, given that relatively high-quality maps exist in most parts of the world, in practice, inference approaches must be applied to update existing maps rather than infer new ones. With recent road extraction methods showing high accuracy, we argue that it is time to transition to the more practical map update task, where an existing map is updated by adding, removing, and shifting roads, without introducing errors in parts of the existing map that remain up-to-date. In this paper, we develop a new dataset called MUNO21 for the map update task, and show that it poses several new and interesting research challenges. We evaluate several state-of-the-art road extraction methods on MUNO21, and find that substantial further improvements in accuracy will be needed to realize automatic map update.