Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements. However, limited by the interpretability of the machine learning model, existing studies cannot fully excavate the complex nonlinear relationships between housing prices and environmental elements, as well as the spatial variations of impacts of urban environmental elements on housing prices. This study explored the impacts of urban environmental elements on residential housing prices based on multisource data in Shanghai. A SHapley Additive exPlanations (SHAP) method was introduced to explain the impacts of urban environmental elements on housing prices. By combining the ensemble learning model and SHAP, the contributions of environmental characteristics derived from street view data and remote sensing data were computed and mapped. The experimental results show that all the urban environmental characteristics account for 16 percent of housing prices in Shanghai. The relationships between housing prices and two green characteristics (green view index from street view data and urban green coverage rate from remote sensing) are both nonlinear. Shanghai’s homebuyers are willing to pay a premium for green only when the green view index or urban green coverage rate are of higher value. However, there are significant differences between the impacts of the green view index and urban green coverage rate on housing prices. The sky view index has a negative influence on housing prices, which is probably because the high-density and high-rise residential area often has better living facilities. Residents in Shanghai are willing to pay a premium for high urban water coverage. The case of Shanghai shows that the proposed framework is practical and efficient. This framework is believed to provide a tool to inform the decisions of housing buyers, property developers and policies concerning land-selling and buying, property development and urban environment improvement.

Semantic segmentation is an essential technique in remote sensing. Until recently, most related research has focused primarily on advancing semantic segmentation models based on monomodal imagery, and less attention has been given to models that utilize multimodal remote sensing data. Moreover, most current multimodal approaches consider only limited bimodal situations and cannot simultaneously utilize three or more modalities. The increase in expensive computational costs associated with previous feature fusion paradigms hinders their application in broader cases. How to design a unified method to cover a wide variety of quantity-agnostic modalities for multimodal semantic segmentation remains an unsolved issue. To address the aforementioned challenges, this study explores a feasible way and proposes a cost-effective multimodal sensing semantic segmentation model (MultiSenseSeg). MultiSenseSeg employs multiple lightweight modality-specific experts (MSEs), an adaptive multimodal matching (AMM) module, and a single feature extraction pipeline to efficiently model intramodal and intermodal relationships. Benefiting from these designs, the proposed MultiSenseSeg can serve as a unified multimodal model capable of addressing both monomodal and bimodal cases and readily extrapolating to scenarios with more modalities, thereby achieving semantic segmentation of arbitrary quantities of multimodal data. To evaluate the performance of our method, we select several state-of-the-art (SOTA) semantic segmentation models from the past three years and conduct extensive experiments on two public multimodal datasets. The results show that MultiSenseSeg can not only achieve higher accuracy but also exhibit user-friendly modality extrapolation, allowing end-to-end training for consumer-grade users based on limited hardware resources. The model’s code will be available at https://github.com/W-qp/MultiSenseSeg .

New ambient air quality standards were released in 2012 and implemented in 2013 with real time monitoring data publication of six atmospheric pollutants: particulate matter (PM)_2.5, PM₁₀, O₃, SO₂, NO₂ and CO. According to the new standards, Beijing began to publicize real-time monitoring data of 35 monitoring stations in 2013. In this study, real time concentrations of all six atmospheric pollutants of all 35 monitoring stations were collected from September 2014 to August 2015 to investigate the spatial and temporal pattern of the air quality and atmospheric pollutants. By comparing the annual and seasonal variations of all six pollutants’ concentrations, it was found that particulate matter, especially PM_2.5, is still the major contributor to the deterioration of air quality in Beijing. Although the NO₂ and O₃ concentrations of some stations were still high under certain circumstances, their contributions to air quality index (AQI) were not comparable to those of PM_2.5 and PM₁₀. SO₂ and CO concentrations have dropped to well below the qualification standards. Winter and autumn were the most polluted seasons for all pollutants except O_3, whose concentrations are higher in summer. South and southeast stations were the most polluted compared with the rest of the stations, especially for particulate matter. Wind profile analysis with heavy pollution situations indicates that low speed southwest or east wind situations have the higher possibility of heavy pollution, suggesting that it is highly possible that long-range transportation of air pollutants from south or east neighboring provinces played an important role in the worsening air conditions in Beijing.