## 1 Introduction  
The Ministry of Natural Resources of the People's Republic of China issued the 'Notice on Comprehensively Promoting the Construction of Realistic 3D China' in February 2022 ([[http://gi.mmr.gov.cn/202202/t20220225_2729401.html](http://gi.mmr.gov.cn/202202/t20220225_2729401.html)]([http://gi.mmr.gov.cn/202202/t20220225_2729401.html](http://gi.mmr.gov.cn/202202/t20220225_2729401.html)) (accessed on 24 February 2022)) (hereafter referred to as the 'Notice'). This notice takes terrain-level 3D construction as the primary task of the real 3D China construction. The specific tasks include completing 10 m and 5 m grids at the national level and completing the construction of the digital elevation model (DEM), DSM, and other data resources of the 2 m grid at the local level. As the key foundation of the real 3D China, the precise expression of terrain plays a key role in supporting the city-level and component-level real 3D construction specified in the Notice. With the continuous development of the social economy and the continuous transformation of human activities, the earth's surface has gradually evolved into a mixed natural and artificial terrain with multiple landforms. In this context, making full use of multisource and multiscale surface observation data to achieve accurate simulation and expression of the natural and artificial mixed terrain has become the basic task of real 3D China construction, as well as a popular topic in geography, surveying, mapping, and other related fields. In the process of expression and modeling of mixed natural and artificial terrain, road is a key element. Especially in urban areas, roads are the skeletal elements of urban terrain expression [1]. Therefore, the accurate construction of the road DEM becomes the key goal in the construction of mixed natural and artificial terrain DEM. As a typical artificial terrain, the road has the characteristics of a clear boundary and a smooth surface; therefore, the construction of the road DEM involves two basic processes, namely, road boundary information extraction and road surface DEM interpolation. The extraction of road boundary information can be attributed to feature extraction technology based on multisource multiscale images, Lidar, and other data. This topic is widely discussed in the fields of remote sensing and photogrammetry, as well as in other fields, and has achieved fruitful research results, such as extracting the building base range of urban areas based on different DEM data [2], combining image data with the DEM [3] and LIDAR data [4], using image and LIDAR point cloud and other data to extract road boundary information in urban areas [5; 6; 7], extracting water network boundary information in urban areas based on image data [8], extracting bare land area boundaries in urban areas [9], etc. Using high-definition image data to detect changes in urban areas, as well as to extract surface land use type classification, can also produce rich terrain entity boundary information [10; 11]. In recent years, the depth learning method has also been introduced into the field of image processing, and relevant scholars have achieved good results by applying this method in the fine classification of the surface cover [12; 13; 14; 15; 16]. In terms of road DEM construction, [PERSON] [17] discussed this problem early on, and his research also recognized the difference between the road and its background area; thus, he proposed that the road boundary be extracted first before using the elevation point information available on the road surface to construct the road DEM in the form of irregular triangulation. He also considered basic issues such as road surface elevation information inspection, available elevation point screening, etc. However, the mature TIN construction method is used for DEM development in this study. Because the irregular triangular network and the regular shape of the road surface are contradictory, the constructed road DEM does not have a perfect shape expression. [PERSON] et al.