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To improve the integrity of vegetation point clouds, the missing vegetation point can be compensated through vegetation point clouds completion technology. Further, it can enhance the accuracy of these point clouds' applications, particularly in terms of quantitative calculations, such as for the urban living vegetation volume (LVV). However, owing to factors like the mutual occlusion between ground objects, sensor perspective, and penetration ability limitations resulting in missing single tree point clouds' structures, the existing completion techniques cannot be directly applied to the single tree point clouds' completion. This study combines the cutting-edge deep learning techniques, for example, the self-supervised and multiscale Encoder (Decoder), to propose a tree completion net (TC-Net) model that is suitable for the single tree structure completion. Being motivated by the attenuation of electromagnetic waves through a uniform medium, this study proposes an uneven density loss pattern. This study uses the local similarity visualization method, which is different from ordinary Chamfer distance (CD) values and can better assist in visually assessing the effects of point cloud completion. Experimental results indicate that the TC-Net model, based on the uneven density loss pattern, effectively identifies and compensates for the missing structures of single tree point clouds in real scenarios, thus reducing the average CD value by above 2.0, with the best result dropping from 23.89 to 13.08. Meanwhile, experiments on a large-scale tree dataset show that TC-Net has the lowest average CD value of 13.28. In the urban LVV estimates, the completed point clouds have reduced the average MAE, RMSE, and MAPE from 9.57, 7.78, and 14.11% to 1.86, 2.84, and 5.23%, respectively, thus demonstrating the effectiveness of TC-Net. [ABSTRACT FROM AUTHOR]

Online shopping festivals have become powerful ways for major e-commerce platforms to conduct promotions. To explore the rule and hidden information of the change of users’ attention to online shopping festivals, it can provide scientific and effective marketing reference, and promote the sustainable development of online shopping festivals. This paper takes the “Double 11” online shopping festival as the research object. Based on the LDA+LSA+NMF integrated theme extraction model, kernel density analysis, grouping analysis and cluster outlier analysis in ArcGIS technology are adopted. We explore the evolution pattern of microblog users’ attention to the online shopping festival in the spatial and temporal dimensions. In the temporal dimension, the number of releases showed a stepwise increase, and concerns showed annual topic evolution and daily three-stage differential evolution. In the spatial dimension, the release volume showed a gradient decreasing trend from coastal to inland and from east to west, and concerns showed certain regional differences. The main contribution of this paper is to propose a funnel theme extraction model with multi-model integration, and provide a new perspective for the spatial research of online shopping festival based on ArcGIS spatial analysis technology.

The analysis of greenhouse gas emission characteristics on urban roads is of great significance for understanding the sources and sinks of urban greenhouse gases and their changing patterns. Based on tunable diode laser derivative absorption spectroscopy technology, which features high resolution, high sensitivity, and fast response, a vehicle-mounted system capable of simultaneously detecting CO2 and CH4 has been developed. The system has a response time of 0.38 s, with detection sensitivities of 5 ppb for CH4 and 0.2 ppm for CO2, power consumption of approximately 4.8 W, a weight of less than 3 kg, and dimensions of 255 mm × 275 mm × 85 mm. Using this system, monitoring campaigns were conducted on the same road in Beijing, running north–south, during different time periods in April and June 2023. The results show that there is little correlation between changes in CO2 and CH4 concentrations on the road, and these gas concentrations exhibit different influencing factors and spatiotemporal characteristics. The CO2 concentration on the road is primarily related to the degree of traffic congestion and does not exhibit significant seasonal variations. The average CO2 concentration measured on the road is much higher than the global average CO2 concentration during the same period. On the other hand, the CH4 concentration on the road is not strongly correlated with traffic congestion but is closely related to the leakage of methane from specific emission wells or covers. The CH4 concentration is higher in the morning, gradually decreases as the sun rises, and then increases again after sunset. The CH4 concentration measured at night in June is significantly lower than that in April, reflecting some seasonal variation. The CH4 concentration on the Beijing urban road is slightly higher than the global average CH4 concentration during the same period. The vehicle-mounted experiments verified the feasibility of using this self-developed system for vehicle-mounted detection of greenhouse gas concentrations on urban roads. The research results can provide data for analyzing the spatial pattern of regional carbon sources and sinks. [ABSTRACT FROM AUTHOR]