RoadTracer: Automatic Extraction of Road Networks from Aerial Images
This addresses the expensive and labor-intensive task of mapping road networks for urban planning and navigation, representing a novel method rather than an incremental improvement.
The paper tackles the problem of automatically extracting road networks from aerial images by proposing RoadTracer, which uses an iterative search guided by a CNN to directly construct graphs, resulting in a 45% increase in correctly captured junctions at a 5% error rate compared to segmentation methods.
Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. We compare our approach with a segmentation method on fifteen cities, and find that at a 5% error rate, RoadTracer correctly captures 45% more junctions across these cities.