# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 1 Introduction  
Digital mapping of road networks is necessary for various industrial applications such as land use and land cover mapping [1], geographic information system updates [2, 3] and natural disaster warning [4]. Moreover, it is a critical requirement for digital cities and intelligent transportation [5]. Traditional cartographic techniques are time-consuming and labour-intensive [6, 7]. In comparison, remote sensing techniques changed the mapping community fundamentally without relying entirely on surveyed ground measurements [6]. Remote sensing data used for road extraction include ground moving target indicator (GMTI) tracking, smart phones global positioning system (GPS) data, street view images, synthetic aperture radar (SAR) images, light detection and ranging (LiDAR) data, high-resolution images, and hyperspectral images. GMIT radar has been used for extracting road map information due to the advantages of all-weather, real-time capabilities, and wide-area [8, 9]. Smart phone GPS data were used for extracting road centrelines and monitoring road and traffic conditions [10, 11]. Street view images obtained by Google of USA and Baidu of China companies have been used to detect, classify, and map trafficsigns and road crack information extraction [12; 13]. In this paper, based on the different data sources, the existing road extraction technology can be roughly divided into four methods: high-resolution imaging-based, hyperspectral imaging-based, SAR imaging-based and LiDAR-based methods. SAR and LiDAR are active information acquisition methods. In contrast, high-resolution and hyperspectral imaging are passive optical imaging approaches. Each road extraction method, based on a different data source, has unique characteristics. For instance, the high-resolution imaging technology can be used to obtain images with centimetre-level accuracy and detailed target information [14]; hyperspectral remote sensing images are used for conventional road extraction; they also demonstrate excellent potential for road condition detection owing to a large number of bands (generally more than 100 bands) and continuous spectrum coverage [15]; SAR and LiDAR datasets are not easily affected by environmental factors such as changes in environmental illumination conditions or weather [16]. Road extraction is a popular research topic and has attracted the interest of numerous researchers. More than 2,770,000 results can be found in Google Scholar using the keyword 'road extraction', including some state-of-the-art reviews published in recent years. [PERSON] et al. [17] summarised the main road extraction methods from 1984 to 2014 based on high-resolution images. In this study, road information extraction methods were divided into knowledge-based [18; 19], classification-based [20; 21; 22; 23; 24; 25; 26], active contour-based [27; 28; 29], mathematical morphology-based [30; 31], and dynamic programming-based grouping methods [32; 33]. However, these methods were chiefly heuristic, and deep learning-based approaches were not presented [34]. This scenario has undergone a drastic change in recent years with the rapid development of patch-based convolutional neural networks (CNNs) [35; 36; 37; 38; 39; 40], full convolutional network (FCN)-based [41; 42; 43; 44; 45; 46], deconvolutional net-based [47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64], generative adversarial network (GAN)-based [65; 66], and graph-based deep learning methods [67; 68; 69; 70] for road extraction. Road extraction methods based on deep learning are collectively referred to as data-driven approaches in [34; 71]. [PERSON] et al. [34] and [PERSON] et al. [71] presented and compared the deep learning-based state-of-the-art road extraction methods using publicly available high-resolution image datasets. [PERSON] et al. [72] reviewed the SAR image-based road extraction method. This review initially introduces the road characteristics and basic strategies and then presents a summary of the main road extraction techniques based on SAR images.