Identifying and tracking objects in surveillance videos is an important means of mining information during surveillance. Currently, most object‐tracking methods rely only on image features, which cannot accurately express the motion of the tracked object in real geographical scenes and are easily influenced by occlusion and surrounding objects having similar features. However, tracked objects, such as pedestrians and vehicles, usually move in geographical space with fixed patterns of motion, and the motion in a short time is constrained by geographical space and time, the motion trajectory is predictable, and the range of motion is limited. Therefore, based on the SiamFC object tracking framework, this study introduces geographical spatiotemporal constraints into the tracking framework and proposes the GeoSiamFC method. The objective of this is to: (1) construct the mapping relationship between geographical space and image space to solve the problem that the pixel movement within the image after perspective imaging cannot accurately express the motion of the tracked object in a real geographical scene; (2) add candidate search areas according to the predicted trajectory location to correct the tracking errors caused by the occlusion of the object; and (3) focus on the search for the range of motion of the mapped pixel within the image space according to the limited geographical range of motion of the tracked objects in a certain time to reduce the interference of similar objects within the search area. In this study, separate experiments were conducted on a common test dataset using multiple methods to deal with challenges such as occlusion and illumination changes. In addition, a robust test dataset with noise addition and luminance adjustment based on the common test dataset was used. The results show that GeoSiamFC outperforms other object‐tracking methods in the common test dataset with a precision score of 0.995 and a success score of 0.756 compared with most other object‐tracking algorithms under the base condition of using only shallow networks. Moreover, GeoSiamFC maintained the highest precision score (0.970) and high success score (0.734) in the more challenging robust test dataset as well. The tracking speed of 59 frames per second far exceeds the real‐time requirement of 25 FPS. Geographical spatiotemporal constraints were considered to improve tracker performance while providing real‐time feedback on the motion trajectory of the target in geographical space. Thus, the proposed method is suitable for real‐time tracking of the motion trajectory of a target in real geographical scenes in various surveillance videos.

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.