Recognizing road from satellite images by structured neural network

Guangliang Cheng, Chongruo Wu, Qingqing Huang, Yu Meng, Jianping Shi, Jiansheng Chen, Dongmei Yan

Abstract: Highlights • We have collected a large labeled road extraction dataset with varying region distribution, which includes labels for both roads and their orientations. • We further designed a structured deep neural network for road extraction. The specially designed cascade network and direction module are quite effective to capture the linear structure of the road. The experimental results reveal the effectiveness of our structured road extraction network. • We designed a novel road performance evaluation metric that considers both the pixel-level aspect and number-level aspect. To our best knowledge, it is the first time to introduce the number-level metric for the road detection task. Abstract Recognizing and extracting roads accurately are significant for auto-driving cars and map providers. Thanks to the power of deep learning, it is possible to achieve high accuracy with a large amount of labeled data. However, as far as we know, there is not enough public data for road recognition from satellite images, especially for the urban scene. To provide sufficient data for training a neural network, we collect a large dataset for road recognition task, which covers varieties of road scenes and contains large-size images from the satellite view. Inspired by the unique road structure, we propose a structured deep neural network to obtain smooth and continuous road skeleton. The proposed network incorporates the road segmentation result and direction result together. Based on the shape prior of the road, the predicted direction information can facilitate road extraction in an end-to-end learning network. Then, a cascade skeleton network is proposed to achieve smooth, continuous and equal-width road skeleton. We also design an evaluation metric which measures both per pixel accuracy and per road accuracy. Our structured road extraction network outperforms the state-of-the-art approaches and the baseline without road prior.