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

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...

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Bibliographic Details
Main Authors: Bastani, Favyen, Madden, Sam
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Computer vision
Roads
Satellites
Task analysis
Topology
Trajectory
Vision applications and systems
Online Access:Request full text
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Summary: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.
ISSN:2380-7504
DOI:10.1109/ICCV48922.2021.01169