Abstract
Aerial photographs and satellite images are one of the resources used for earth observation. In practice, automated detection of roads on aerial images is of significant values for the application such as car navigation, law enforcement, and fire services. In this paper, we present a novel road extraction method from aerial images based on an improved generative adversarial network, which is an end-to-end framework only requiring a few samples for training. Experimental results on the Massachusetts Roads Dataset show that the proposed method provides better performance than several state of the art techniques in terms of detection accuracy, recall, precision and F1-score.
Highlights
Roads act as a fundamental unit for many geographic information system applications, such as vehicle navigation, traffic management, and emergency response
There are other kinds of remote sensing data can be used for road extraction, such as hyperspectral images (HSI) [1,2], synthetic aperture radar (SAR) data [3,4,5], airborne laser scanning (ALS) data [6,7,8] and mobile laser scanner (MLS) data [9,10,11]
The dataset consists of 1171 aerial images, where each image is with the size of 1500 × 1500 pixels. 1108 of these images have been randomly assigned to the training set
Summary
Roads act as a fundamental unit for many geographic information system applications, such as vehicle navigation, traffic management, and emergency response. It is an important element of military surveying and mapping. Traditional road network data mainly comes from manual extraction, which consumes intensive human resources. With the improvement of spatial resolution, it becomes an increasingly important data source for extracting road network information from aerial images. With the continuous updating of road information, traditional manual operation has been unable to meet the demand. Combining remote sensing technology with computer vision to extract road information from aerial images helps automate and accelerate road monitoring
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