Parks, as a major infrastructure that provide public service for urban residents, play a vital role in promoting urban livability and public health. Under the framework of spatial equity, more sophisticated accessibility methods were used on measuring urban park accessibility such as multi-mode 2SFCA. However, the accessibility of residential areas near parks was seriously underestimated by using the multi-mode 2SFCA method. Thus, this study aimed to propose an improved multi-mode 2SFCA method to measure urban park accessibility with a more appropriate approach, by taking residential areas of Tianjin central city as the spatial unit. The results indicate that all residential areas can obtain urban park accessibility, but the spatial distribution of urban park accessibility is heterogeneous. The numerical value of urban park accessibility decreases as the travel time from residential areas to urban parks increases; it is shown that the proposed method can provide a more realistic evaluation compared to the traditional multi-mode 2SFCA method. This study provides a comprehensive and realistic insight into acquainting with urban park accessibility and helps urban planners formulate effective policies and strategies to ease spatial imbalance.

The frequent occurrence of extreme high temperature weather and heat waves has greatly affected human life. This paper analyzes population exposure and its influencing factors during a heat wave incident in Zhuhai from 6 to 12 September 2021 based on real-time mobile phone data and meteorological data. The results show that the most areas of Zhuhai are affected by high temperature during this heat wave incident. The hourly population exposure is directly proportional to hourly heat wave coverage. In terms of time dimension, the overall population exposure shows a trend of decreasing and then increasing. In terms of spatial dimensions, high population exposure is concentrated in areas such as primary and secondary schools, colleges and universities, office buildings, and residential areas. Low exposure is distributed in most of the mountainous areas along the southern coast. In addition, the leading factors that cause changes in population exposure in different periods of the heat wave cycle are different, which rely more on either climatic factors or population factors.

Accurate geolocation in GNSS-denied mountainous environments remains a significant challenge for navigation and field operations. While vision-based methods using terrain features like skylines offer a promising passive solution, their performance is often hindered by unknown camera orientation, low feature discriminability across vast areas, and severe degradation under nighttime conditions. To address these limitations, this paper presents a novel visual geolocation framework that synergistically couples ephemeris-based orientation correction with multi-scale skyline feature matching. First, an Edge-Enhanced SegFormer-B3 (EESFormer-B3) network is developed for robust skyline extraction from query images, where a lightweight Edge Gate mechanism is integrated to refine boundary precision, particularly under low-light scenarios. Second, we propose a camera heading estimation method utilizing solar and lunar ephemeris, eliminating reliance on external heading sensors and drastically reducing the matching search space. Third, for matching, a hierarchical composite feature is designed, encoding the skyline's global shape trends, local directional variations, and dominant structural distributions to ensure robustness against repetitive terrain patterns. Extensive experiments were conducted in a complex 700 km² mountainous region. The results demonstrate that our method achieves a localization success rate of 82% (daytime) and 76% (nighttime) within a 250-meter error threshold. The ephemeris-based correction alone accelerates the matching process by over 30 times compared to an unconstrained orientation search. This work provides a reliable, efficient, and illumination-invariant geolocation solution, with strong potential for applications in autonomous navigation, search and rescue, and other field missions in GNSS-compromised natural environments.

Increasing function of mobile phone, with availability of traffic information publishing, has created a demand for techniques and methodologies that assist in the automated generation of traffic maps specifically tailored to mobile phone. This paper concerns itself with the problem of automatic generation of schematic maps for traffic information. Schematic maps are effective tools for representation information about the physical environment; they depict specific information in an abstract way. In the context of mobile mapping, schematic maps are seen as being particularly useful means of displaying transportation networks. This paper presents a method that automates the production of schematic maps of traffic information. The method takes into account road type, interesting area of user, current location of user and topologic of the road network, et.al. An implementation of the method is also presented, together with experimental results.