Satellite‐based thermal infrared (TIR) land surface temperature (LST) is hindered by cloud cover and is applicable solely under clear‐sky conditions for estimating surface urban heat island intensity (SUHII). Clear‐sky SUHII may not accurately represent all‐sky conditions, potentially introducing quantitative biases in assessing urban heat islands. However, the differences between clear‐sky and all‐sky SUHIIs and their spatiotemporal variations are still poorly understood. Our analysis of over 600 global cities demonstrates that clear‐sky SUHII is mostly higher than all‐sky SUHII, particularly in summer, daytime, and precipitation‐rich regions. Besides, clear‐sky SUHII typically exhibits stronger seasonal and diurnal contrasts than all‐sky SUHII, especially for cities located in humid regions. These discrepancies can be attributed mainly to the increased missing LST data caused by cloud enhancement in urban areas. Our findings highlight the tendency for clear‐sky observations to overestimate SUHII, providing valuable insights for standardizing the quantification of surface urban heat islands.

Realization of open online control of diverse in-situ sensors is a challenge. This paper proposes a Cyber-Physical Geographical Information Service-enabled method for control of diverse in-situ sensors, based on location-based instant sensing of sensors, which provides closed-loop feedbacks. The method adopts the concepts and technologies of newly developed cyber-physical systems (CPSs) to combine control with sensing, communication, and computation, takes advantage of geographical information service such as services provided by the Tianditu which is a basic geographic information service platform in China and Sensor Web services to establish geo-sensor applications, and builds well-designed human-machine interfaces (HMIs) to support online and open interactions between human beings and physical sensors through cyberspace. The method was tested with experiments carried out in two geographically distributed scientific experimental fields, Baoxie Sensor Web Experimental Field in Wuhan city and Yemaomian Landslide Monitoring Station in Three Gorges, with three typical sensors chosen as representatives using the prototype system Geospatial Sensor Web Common Service Platform. The results show that the proposed method is an open, online, closed-loop means of control.

The SDGSAT-1 satellite, independently developed and launched by China, features a higher spatial resolution in its Glimmer Imager (GLI) data than the currently widely used nighttime light (NTL) remote sensing data, making it possible for high-precision extraction of urban built-up areas (UBAs). However, recent empirical studies have revealed that the higher resolution of the NTL data does not inherently guarantee enhanced accuracy of the UBA extraction, as urban morphology and city scale significantly influence the performance. Therefore, comprehensively investigating the UBA extraction performance of NTL data across cities with different morphologies and scales offers valuable guidance for optimizing urban remote sensing methodologies in diverse city contexts. Across 15 representative cities with various urban characteristics in China, this study conducted a comprehensive evaluation of the UBA extraction performance of SDGSAT-1 GLI data for the first time, with the NPP-VIIRS NTL data for comparison, and discussed how urban morphology and city scale affect the performance in UBA extraction. The results indicated that (1) In cities with different morphologies, the UBA extraction performance of SDGSAT-1 data follows from high to low: striped cities, clustered cities, and agglomerate cities. In cities with different scales, the UBA extraction performance of SDGSAT-1 data is megacities > super cities > medium cities > small cities > large cities. (2) Compared with NPP-VIIRS data, SDGSAT-1's performance in UBA extraction is not always superior and varies with the urban morphology and city scale as well as the extraction method, which necessitates context-sensitive selection for NTL data and method in UBA extraction.

Solid waste detection is of great significance for environmental protection. In recent years, object detection methods based on deep learning have progressed rapidly. However, it is often extremely difficult to collect sufficient data to train a model with a good performance. In this article, a data augmentation strategy was introduced to generate sufficient synthetic high-quality images for solid waste detection. First, a modified pix2pix model was proposed, in which a local-global discriminator was designed to improve the detailed and global information of the generated images, which are commonly fuzzy with the original pix2pix model. Second, a copy-paste operator was utilized, which simply pastes the bounding box of the generated objects into different images to enhance the diversity of the samples. In this manner, the expanded dataset can be utilized to train different object detection models, for which FPN and Yolo-v4 were introduced as the validation models in this article. The experimental results show that the proposed strategy outperforms the traditional pix2pix method and the generated synthetic images can effectively improve the performance of object detection methods.

The smart city has become a popular topic of investigation. How to focus large amounts of distributed information resources to efficiently cope with public emergencies and provide support for personalized decision-making is a vitally important issue in the construction of smart cities. In this paper, an event-driven focusing service (EDFS) method that uses cyber-physical infrastructures for emergency response in smart cities is proposed. The method consists of a focusing service model at the top level, an informational representation of the model and a focusing service process to operate the service model in emergency response. The focusing service method follows an event-driven mechanism that allows the focusing service process to be triggered by public emergencies sensed by wireless sensor networks (WSNs) and mobile crowd sensing, and it integrates the requirements of different societal entities with regard to response to emergencies and information resources, thereby providing comprehensive and personalized support for decision-making. Furthermore, an EDFS prototype system is designed and implemented based on the proposed method. An experiment using a real-world scenario—the gas leakage in August 2014 in Taiyuan, China—is presented demonstrating the feasibility of the proposed method for assisting various societal entities in coping with and efficiently responding to public emergencies.