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Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has shown promise, but it often faces difficulties in distinguishing building photons from other ground objects. To address this challenge, we propose a novel method that incorporates building footprints, relative positions of building and ground photons, and a self-adaptive buffer for building photon selection. We employ the Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) photon-counting LiDAR, specifically the ICESat-2/ATL03 data, along with building footprints obtained from the New York City (NYC) Open Data platform. The proposed approach was applied to estimate the heights of 17,399 buildings in NYC, and the results showed strong consistency with the reference building heights. The root mean square error (RMSE) was 8.1 m, and for 71% of the buildings, the mean absolute error (MAE) was less than 3 m. Furthermore, we conducted an extensive evaluation of the proposed approach and thoroughly investigated the influence of terrain, region, building height, building density, and parameter selection. We also verified the effectiveness of our approach in an experimental area in Beijing and compared it with other existing methods. By leveraging ICESat-2 LiDAR data, building footprints, and advanced selection techniques, the proposed approach demonstrates the potential to accurately estimate building heights over broad areas. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

New government policy in China has facilitated research on the hazards in open-pit mine slopes. Remote sensing, geological, geophysical, and computer simulation techniques have been applied to obtain better information in slope investigation. In this study, we investigated the slope of the Qianlingshan open-pit mine area in Beijing, China. Electrical resistivity tomography and ground-penetrating radar were used, and the results were compared with borehole data to ascertain the slope structure, potential slip surface, and deformation zone. A geological model of the slope was established, and the slope stability was analyzed by numerical simulation. The results showed that the slope had an unstable surface layer over bedrock, and probable creep sliding-tension deformation. In addition, the slope would become less stable in wet conditions. The geological methods used in this study will provide a useful reference for further slope stability analysis and prevention. We took the Qianlingshan open-pit slope in Beijing, China, as the research object, by comprehensive application of historical remote sensing images analysis, field investigation, electrical resistivity tomography (ERT) and ground penetrating radar (GPR), and comparative analysis with borehole data, to obtain the slope parameter such as resistivity and velocity of electromagnetic wave, and to ascertain the slope structure, potential slip surface and deformation zone. On this basis, the geological generalisation model of slope was established, and the failure mechanism and stability of slope were analyzed by numerical simulation. [ABSTRACT FROM AUTHOR]

Understanding storm splitting is of great importance for improving severe weather prediction and risk management. To further understand the mechanisms that cause hailstorm splitting, synoptic, dynamical and microphysical properties for a splitting hailstorm occurred on 19 June 2017 and a non-splitting hailstorm on 16 July 2014 in Beijing in the middle latitudes are investigated and compared based on synoptic maps, radar and sounding observations, as well as a three-dimensional cloud model with hail-bin microphysics. The results indicate that both hailstorms occurred under conditions with quite similar synoptic patterns, a tilting westerly trough at 500 hPa and a cold vertex at 700 hPa, and a clockwise-curved hodograph. However, the intensities of the westerly trough and low-level vertical wind shear were much stronger for the splitting hailstorm case than those for the non-splitting hailstorm case, so that the interactions between dynamical and microphysical processes in the splitting storm are also much stronger. It is found that for the splitting storm case the strong low-level vertical wind shear can cause large asymmetric pressure perturbation, pressure gradient forces and selective development in the storm and induce an obvious main updraft core splitting in the developing stage. The descending of high graupel/hail loading in the stable tropopause and lower stratospheric layer can excite strong downward propagating gravity waves (GWs) that induce a rapid storm splitting in the mature and decaying stages. In contrast, for the non-splitting storm, the relevant processes in the developing stage and the GWs excited by the descending of graupel/hail in the mature and decaying stages are too weak to cause an obvious splitting in the storm. • Splitting and non-splitting storms are compared in dynamics and microphysics. • Low-level wind shear intensity has crucial role in main updraft core splitting. • Strong downward gravity waves excited by high graupel/hail loading above the tropopause can induce rapid storm splitting. [ABSTRACT FROM AUTHOR]