  Automatically extracting roads from satellite imagery is a fundamental yet
challenging computer vision task in the field of remote sensing. Pixel-wise
semantic segmentation-based approaches and graph-based approaches are two
prevailing schemes. However, prior works show the imperfections that semantic
segmentation-based approaches yield road graphs with low connectivity, while
graph-based methods with iterative exploring paradigms and smaller receptive
fields focus more on local information and are also time-consuming. In this
paper, we propose a new scheme for multi-task satellite imagery road
extraction, Patch-wise Road Keypoints Detection (PaRK-Detect). Building on top
of D-LinkNet architecture and adopting the structure of keypoint detection, our
framework predicts the position of patch-wise road keypoints and the adjacent
relationships between them to construct road graphs in a single pass.
Meanwhile, the multi-task framework also performs pixel-wise semantic
segmentation and generates road segmentation masks. We evaluate our approach
against the existing state-of-the-art methods on DeepGlobe, Massachusetts
Roads, and RoadTracer datasets and achieve competitive or better results. We
also demonstrate a considerable outperformance in terms of inference speed.
