## 2 Literature Review  
In the early stage, optical remote sensing images provided the primary data sources for fast acquiring road network data. The existing methods can mainly be categorized into four kinds, e.g., road detection methods based on template matching [23, 24], road extraction models based on prior knowledge [25], object-oriented road segmentation methods [26] and road semantic labelling methods based on deep learning [13]. Generally, the image-based road extraction methods have the advantages of high efficiency and mature technologies, which are suitable for large-scale scenarios. However, the image-based methods may suffer from some difficulties in approaching poor illumination, severe weather condition and ground object occlusion, as well as obtaining dynamic traffic information. After that, Light Detection And Ranging (LiDAR) technologies produced a new data source for extracting high detailed road features, such as 3D road boundary, road markings  
Figure 1: Spatio-temporal heterogeneities and uneven density distribution of crowdsourced trajectory data: (**a**) inconsistent trajectory shape and (**b**) varied trajectory densities. and affiliated road facilities (e.g., street lamp, traffic signs and street trees). The road boundary extraction methods were mostly based on the assumption that the road surfaces are approximately flat and sharp elevation fluctuations probably occur at road curbs [11; 27]. The road marking extraction methods generally utilized the intensity difference of point clouds and specific shapes of road markings to obtain typical road marking information [28; 29]. Additionally, numerous studies attempted to apply template matching, super voxel and deep learning in extracting pole-like road facilities [30; 31]. In brief, the LiDAR-based methods can extract highly precise and very detailed road features under different environmental conditions, but meanwhile, high data cost together with the vulnerable occlusion problem may limit their application in large-scale areas. Presently, the emerging of crowdsourced mapping (i.e., Volunteer Geographic Information, VGI) prompts a paradigm shift from professional surveying to ubiquitous mapping [32]. As a low-cost, wide-coverage and real-time data resource, crowdsourced trajectories collected by vehicles or pedestrians can be processed to extract road networks [20; 33; 34; 35; 36]. The trajectory-based road extraction approaches can mainly be summarized as rasterization methods and vectorization methods. The rasterization methods first converted vector trajectories to raster images and then made use of various image processing algorithms, such as morphological operators and skeletonization algorithms, to generate vector road networks [37; 38]. The rasterization methods are proved efficient and suitable to handle wide-range construction of road maps, but it may be challenging to obtain road semantic information and topological relations for their neglecting the trajectory movement characteristics [39]. The vectorization methods mainly consist of two kinds of strategies. One was devoted to automated road map construction by exploring the clustering patterns of moving trajectories [40], and the other one adopted a trajectory-aligning strategy to incrementally construct road networks [41; 42]. Generally, the vectorization methods can generate detailed road structures and infer some road semantic information (e.g., road width and turning rules); however, these methods are still not robust enough to deal with large trajectory density disparities. Particularly, [PERSON] et al. (2020) proposed a hybrid road extraction method by considering the road changing patterns implied in multi-temporal trajectory data [12]. Furthermore, some studies attempted to construct lane-level road maps from high frequency/precision trajectory data.