SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving

Abstract: Road extraction is an essential step in building autonomous navigation systems. Detecting road segments is challenging as they are of varying widths, bifurcated throughout the image, and are often occluded by terrain, cloud, or other weather conditions. Using just convolution neural networks (ConvNets) for this problem is not effective as it is inefficient at capturing distant dependencies between road segments in the image which is essential to extract road connectivity. To this end, we propose a Spatial and Interaction Space Graph Reasoning (SPIN) module which when plugged into a ConvNet performs reasoning over graphs constructed on spatial and interaction spaces projected from the feature maps. Reasoning over spatial space extracts dependencies between different spatial regions and other contextual information. Reasoning over a projected interaction space helps in appropriate delineation of roads from other topographies present in the image. Thus, SPIN extracts long-range dependencies between road segments and effectively delineates roads from other semantics. We also introduce a SPIN pyramid which performs SPIN graph reasoning across multiple scales to extract multi-scale features. We propose a network based on stacked hourglass modules and SPIN pyramid for road segmentation which achieves better performance compared to existing methods. Moreover, our method is computationally efficient and significantly boosts the convergence speed during training, making it feasible for applying on large-scale high-resolution aerial images. Code available at: https://github.com/wgcban/SPIN_RoadMapper.git.

• The paper proposes the novel SPIN module that enhances road connectivity in aerial imagery through dual graph reasoning in spatial and interaction spaces.
• It integrates a stacked hourglass architecture with multi-scale feature extraction, achieving superior F1, IoU, and APLS scores on major datasets.
• The method reduces convergence time and computational load, making it optimal for real-time mapping and autonomous navigation applications.

SPIN Road Mapper: Extracting Roads from Aerial Images for Autonomous Driving

The paper "SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving" addresses the critical task of road extraction from aerial imagery, a prerequisite for various applications in autonomous driving, navigation, and traffic management. The challenge in this domain arises from the variable morphology of road networks, including varying widths and occlusions caused by environmental factors such as buildings, trees, and adverse weather conditions.

The authors propose the Spatial and Interaction Space Graph Reasoning (SPIN) module, which enhances traditional convolutional neural networks (ConvNets) by facilitating the capture of distant dependencies within the image data. This enhancement is crucial for understanding the connectivity of road segments, which ConvNets historically struggle with due to their limited receptive field that inherently reduces their ability to incorporate broad contextual information.

SPIN Module and Architecture

The SPIN module operates in two graph domains: spatial space and interaction space. The spatial space graph reasoning captures dependencies between different spatial regions of the image, thus improving road detection accuracy by understanding spatial region interactions. Interaction space graph reasoning, on the other hand, projects features into a latent space to delineate roads from other image semantics, effectively distinguishing roads from similarly textured non-road areas.

The proposed network integrates the SPIN module into a framework consisting of stacked hourglass modules and a feature extractor. This combination allows for effective multi-scale feature extraction necessary for precise road segmentation across high-resolution images. The architecture is computationally efficient, with the SPIN module adding only a marginal number of parameters, leading to faster convergence during training without compromising performance.

Experimental Results

The paper presents thorough experimental evaluations on two major datasets: the DeepGlobe and Massachusetts Road datasets. The proposed method achieves superior performance compared to existing state-of-the-art methods, showing improvements in F1 score and IoU metrics, along with the APLS score. This demonstrates the effectiveness of the SPIN module in enhancing connectivity in road segmentation maps—a critical factor for generating usable map data for navigation and autonomous planning tasks.

Implications and Future Directions

The proposed SPIN Road Mapper presents several practical advantages. Its light computational load and reduced convergence time make it suitable for deployment on large-scale mapping projects with high-resolution aerial images—a common scenario in urban planning and autonomous vehicle navigation. The method's capacity to improve road connectivity maps is particularly beneficial for applications in dynamic environments where real-time data processing is crucial.

Future work may explore extending the SPIN framework to other types of geospatial features or integrating it with transformer models, which have shown potential in capturing even broader contextual dependencies. Additionally, exploring alternative graph reasoning strategies that might integrate with sensor data fusion could further enhance the geospatial understanding of autonomous systems, leading to more robust navigation solutions.

This paper contributes significantly to the field of automated map updating and autonomous navigation, with its methodological advancements likely to inform subsequent research in road extraction and possibly other feature extraction tasks from aerial imagery.