Positioning information greatly enhances the convenience of people’s lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone indoor positioning and navigation is a crucial technology for enabling indoor location services. Nevertheless, relying solely on a single positioning technique can hardly achieve accurate indoor localization. We reviewed several main methods for indoor positioning using smartphone sensors, including Wi-Fi, Bluetooth, cameras, microphones, inertial sensors, and others. Among these, wireless medium-based positioning methods are prone to interference from signals and obstacles in the indoor environment, while inertial sensors are limited by error accumulation. The fusion of multi-source sensors in complex indoor scenarios benefits from the complementary advantages of various sensors and has become a research hotspot in the field of pervasive indoor localization applications for smartphones. In this paper, we extensively review the current mainstream sensors and indoor positioning methods for smartphone multi-source sensor fusion. We summarize the recent research progress in this domain along with the characteristics of the relevant techniques and applicable scenarios. Finally, we collate and organize the key issues and technological outlooks of this field.

With the accelerating pace of urbanization, historic districts are increasingly confronted with the dual challenge of coordinating heritage preservation and sustainable development. This study proposes an intelligent evaluation framework that integrates spatiotemporal street view imagery, affective perception modeling, and scene recognition to reveal the evolutionary dynamics of built environment spatial quality in historic districts. Empirical analysis based on multi-temporal data (2013–2020) from the Wudadao Historic District in Tianjin demonstrates that spatial quality is shaped by a complex interplay of factors, including planning and preservation policies, landscape greening, pedestrian-oriented design, infrastructure adequacy, and equitable resource allocation. These findings validate the framework’s effectiveness as a tool for monitoring urban sustainability. Moreover, it provides actionable insights for the development of resilient, equitable, and culturally vibrant built environments, effectively bridging the gap between technological innovation and sustainable governance in the context of historic districts.

Traditional mobile sensing systems often experience a decline in data acquisition accuracy in dynamic environments due to the use of fixed-parameter Kalman filters, which lack adaptability to changes in motion states and sensor noise. To address this limitation, this paper proposes a gait-aware adaptive Kalman filtering method that dynamically adjusts filter parameters based on real-time gait frequency analysis. This method enables autonomous real-time data collection of multi-dimensional data from multiple sensors embedded in smartphones. By employing adaptive covariance optimization, the method enhances the system's ability to handle different motion patterns, ensuring the accuracy and robustness of data collection. Experimental results demonstrate that this method improves data quality in diverse motion scenarios, providing a reliable foundation for multi-modal sensing and intelligent mobile sensing applications.

Accurate and automated extraction of solid waste sites from remote-sensing imagery constitutes a pivotal demand for contemporary environmental regulation and risk mitigation. However, in high-resolution remote-sensing imagery, solid waste sites are typically represented as a single semantic image object (SIO), which is composed of multiple physical image parcels (PIPs) exhibiting significant variations in scale, morphology, and spectral properties. This intrinsic heterogeneity substantially increases the complexity and uncertainty of multi-class site identification. To address this challenge, this paper proposes WasteMOE Net (WMN), which is developed based on the core concept of modeling the SIO–PIP relationship. WMN adopts a heterogeneous expert selection mechanism combined with a nested mixture-of-experts architecture. It thus enables adaptive perception of complex PIPs across diverse scenarios and their integrated discrimination at the SIO level. In addition, by incorporating the explicit nonlinear representation capability of the KAN network, WMN effectively improves multi-class recognition accuracy while maintaining computational efficiency. Furthermore, this study constructs a high-resolution solid waste site dataset in accordance with the SIO–PIP-aware annotation principle, encompassing five representative categories: tailings ponds (TP), construction spoil sites (CSS), landfill sites (LS), garbage dump sites (GDS), and excavation sites (ES). Experimental results show that WMN achieves mAP50 values of 74.2% (GDS), 63.5% (CSS), 80.9% (ES), 85.4% (TP), and 83.1% (LS) in detection tasks, and 75.4%, 64.1%, 83.0%, 86.7%, and 84.1% for the corresponding categories in segmentation tasks. It achieves competitive performance compared with state-of-the-art methods in both tasks. Further, in a real-world application over Loudi City, China, WMN completed the processing of a 490.67 km² area within 1.34 h. The recognition accuracies for GDS and ES reached 54.8% and 65.3%, respectively. Finally, the proposed method has been successfully integrated into a GIS-based solid waste pollution risk prevention system, which markedly boosts the overall efficiency of environmental monitoring and on-site inspections.