RoadTracer: Automatic Extraction of Road Networks from Aerial Images (1802.03680v2)

Abstract: 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.

• The paper presents a novel CNN-driven iterative graph construction method that directly extracts road networks, bypassing error-prone segmentation.
• The approach dynamically trains the CNN using on-the-fly examples, resembling reinforcement learning to refine the evolving graph structure.
• Experimental results show a 45% improvement in junction detection over traditional methods, demonstrating robust performance in occluded areas.

Overview of RoadTracer: Automatic Extraction of Road Networks from Aerial Images

The paper "RoadTracer: Automatic Extraction of Road Networks from Aerial Images" presents a novel methodology for the automatic construction of accurate road network maps utilizing aerial imagery. Traditional efforts in this domain have predominantly relied on segmentation techniques to classify pixels as road or non-road, which are followed by heuristic post-processing to determine connectivity and produce a graph representation. However, these methods encounter significant challenges due to noise and occlusion, often resulting in high error rates. RoadTracer introduces a new approach that directly constructs a road network graph from aerial images by leveraging a convolutional neural network (CNN) guided iterative search, significantly enhancing performance in terms of junction accuracy.

Methodology

RoadTracer departs from the prevalent segmentation approach by employing an iterative graph construction method. This procedure is governed by a CNN-based decision function, which guides the search process to derive the road network graph directly. Unlike prior models, RoadTracer avoids the intermediate noisy image representation of the road network.

• Iterative Graph Construction: The RoadTracer algorithm starts from a known point on the road network and iteratively builds the graph by exploring road segments. The CNN decision function determines the best action at each step—whether to extend a road segment or to stop processing a particular path, based on the local image context and the partial graph constructed so far.
• Dynamic Training with CNN: The CNN guides the iterative search and is dynamically trained using a set of ground truth maps. By employing a dynamic labeling approach that resembles reinforcement learning, the CNN is trained to adapt to the evolving structure of the partially constructed graph, generating training examples on-the-fly as the model learns.

Experimental Findings

RoadTracer's efficacy is validated across a dataset comprising high-resolution satellite imagery of the urban cores from 40 cities, divided into 25 cities for training and 15 for testing. The method demonstrates superior performance over two baselines: an implementation of the DeepRoadMapper approach and a custom segmentation-based approach.

• Junction Accuracy: At a 5% error rate, RoadTracer captures 45% more junctions compared to the segmentation approach. This indicates a substantial improvement in capturing the local topology around road junctions, which is pivotal for various applications like navigation and urban planning.
• Robustness Against Occlusion: RoadTracer shows marked improvements in regions with frequent occlusions from buildings or environmental elements, outperforming segmentation methods that rely heavily on pixel classification, which is inherently noisy.

Theoretical and Practical Implications

From a theoretical standpoint, RoadTracer represents a shift towards integrating graph construction directly within the CNN's predictive capability, eschewing the reliance on heuristic post-processing. This signifies progress toward automating complex spatial tasks through learned representations that capture connectivity directly from visual cues. Practically, applying RoadTracer could considerably reduce the labor-intensive process of road network mapping and updating, offering promise for more accurate and timely map data, crucial for autonomous vehicles and advanced geographic information systems.

Future Directions

Future work could explore enhancing the model's adaptability to different geographies and urban layouts, incorporating multi-scale imagery for richer context, and integrating additional data sources, such as GPS traces, to handle regions where roads are less visible due to environmental conditions. Additionally, expanding the model's capability to infer properties like road directionality and speed limits from imagery could present significant advancements.

In conclusion, RoadTracer presents a crucial advancement in the automation of road network extraction from aerial imagery, demonstrating robust improvements in accuracy and efficiency over traditional methods. Its approach potentially paves the way for more sophisticated applications in automated mapping and intelligent transportation systems.