In order to realize the management of various street objects in smart cities and smart transportation, it is very important to determine their geolocation. Current positioning methods of street-view images based on mobile mapping systems (MMSs) mainly rely on depth data or image feature matching. However, auxiliary data increase the cost of data acquisition, and image features are difficult to apply to MMS data with low overlap. A positioning method based on threshold-constrained line of bearing (LOB) overcomes the above problems, but threshold selection depends on specific data and scenes and is not universal. In this paper, we propose the idea of divide–conquer based on the positioning method of LOB. The area to be calculated is adaptively divided by the driving trajectory of the MMS, which constrains the effective range of LOB and reduces the unnecessary calculation cost. This method achieves reasonable screening of the positioning results within range without introducing other auxiliary data, which improves the computing efficiency and the geographic positioning accuracy. Yincun town, Changzhou City, China, was used as the experimental area, and pole-like objects were used as research objects to test the proposed method. The results show that the 6104 pole-like objects obtained through object detection realized by deep learning are mapped as LOBs, and high-precision geographic positioning of pole-like objects is realized through region division and self-adaptive constraints (recall rate, 93%; accuracy rate, 96%). Compared with the existing positioning methods based on LOB, the positioning accuracy of the proposed method is higher, and the threshold value is self-adaptive to various road scenes.

Installing photovoltaic (PV) equipment on the building's surface reduces greenhouse gas emissions and additional land acquisition. However, existing methods for estimating PV potential primarily focus on roofs and the assessment of facades and windows is not detailed. This study proposes a method based on the Building Information Model and its standard Industry Foundation Classes to effectively estimate the potential area and location of PV modules on the building's surface and explore the PV potential in detail. The method was applied to residential buildings in Xinyi County, China, and the results show that the annual average energy generated through windows (47,589.793 kWh/year), roofs (141,126.304 kWh), and facades (284,060.393 kWh) constitute a significant amount, particularly the facades, which show significant potential as sources of solar electricity even under the influence of shadows. The rationality and feasibility of this method were verified by comparing it with ArcGIS and other relevant studies available in the literature. Next, we assessed whether the installation of PV panels on the building's surface still has certain operability under the serious shadows. For this purpose, a building with serious shadow shading was selected to analyze the demand and supply of energy. The electricity consumption of the selected building was found to be 131,785.898 kWh/year, which is significantly lower than PV potential (310,632.775 kWh/year), as calculated in this study. Thus, the efficient use of the building's surface for PV generation can meet not only the local electrical energy demand but also the excess energy generated can be sold for economic gain. The method proposed can provide new insights to energy software designers and developers, and the results can provide a reference for energy investors, building owners, and engineers to focus on the installation of PV panels on facades and windows, and formulate complex schemes of PV modules on different building components.

Photovoltaic energy generation has gained wide attention owing to its efficiency and environmental benefits. Therefore, it has become important to accurately evaluate the photovoltaic energy generation potential of building surfaces. As the number of building floors increases, the area of the facades becomes much larger than that of the roof, providing improved potential for photovoltaic equipment installation. Conventional urban solar potential evaluation methods are usually based on light detection and ranging (LiDAR). However, LiDAR can only be used in existing buildings, and the lack of semantic information in the point cloud data generated by LiDAR makes it impossible to evaluate the photovoltaic potential of facades (including details such as windows) in detail and with accuracy. In this study, we developed a method to accurately extract facades and roofs in order to evaluate photovoltaic potential based on the Industry Foundation Classes. To verify the feasibility of this approach, we used a building from Xuzhou city, Jiangsu province, China. The simulation results indicate that, out of the total building photovoltaic installable area (8995 m²), that of the facade is 8240 m². The photovoltaic potential of the simulated building could reach 1054.69 MWh/year. The sensitivity studies of the grid resolution, the time interval and the computation time confirmed the reasonability of the determined conditions. The method proposed offers great potential for energy planning departments and the improved utilization of buildings.

Existing concepts and frameworks of soundscapes focus on the analysis and description of the sound source but do not explore geographical environment parameters and receiver characteristics in the geographical scene. Existing soundscape data models ignore the geographical environment and receiver information, which limits the comprehensive understanding and expression of soundscapes. They cannot study the relationship between the elements related to the sound source or explore the interaction mechanism between the sound and geographical environments. From the geographical perspective, this study extends soundscape to geographical soundscape (geo-soundscape), defines geo-soundscape by the cognition of the geographical scene, analyzes and expresses the conceptual framework of soundscapes through a content hierarchy structure, and expands the characteristics of the receiver, geographical environment parameters, further-obtained geographical scene elements, and scene element description dimensions. Based on the MPEG-7 data model, this study develops a geographical-MPEG-7 data model which consists of low-, medium-, and high-level feature classes. Taking as an example soundscape data collected on a university road in Nanjing, Jiangsu Province, in a real geographical environment, the concept, framework, and data model architecture of the geo-soundscape proposed in this study are demonstrated and described to validate the completeness and feasibility of the proposed model. The results show that our basic framework for a geo-soundscape is well adapted to the Geo-MPEG-7 data model. The model can store, organize, and describe all the soundscape information containing all elements and inter-element relationships. The soundscape in the real environment is fully expressed and described. This study provides a new research direction for soundscapes from a geographical perspective and provides guidance for urban planning and landscape design.