The safety of public spaces on university campuses directly affects the mental and physical well-being of both faculty and students, significantly contributing to the stability and growth of the institution. Therefore, evaluating and optimizing the sense of security in these public areas is paramount. This study adopts a human-centric approach and selects five physical environmental metrics to construct a comprehensive safety perception model encompassing both day and night, using the analytic hierarchy process. By leveraging evaluation outcomes, a maximum coverage location-allocation model constrained by a facility service radius is employed to optimize the layout of the public amenities on campus, supported by empirical research conducted at the Wuchang Campus of the Wuhan Institute of Technology. The results indicate that a notable decrease in public areas provides a low sense of security to users and that an increase in public areas provides users with a high sense of security. The results demonstrate a considerable enhancement in both the daytime and nighttime sense of security due to the implemented optimization measures. This research contributes to a deeper understanding of the constituent elements shaping the sense of security in campus public spaces, offering concrete strategies for their evaluation and enhancement, thereby fostering a more scientifically grounded layout of campus public facilities and advancing the creation of a harmonious and secure campus environment.

Sensor planners find it difficult to choose the right sensor combination for observation tasks because current sensor discovery methods can only generate lists of individual sensors without considering the association between sensors that is conveyed by understanding their observation capability information. This limitation results in the failure to properly plan sensor observations and to obtain accurate observation information. The observation capability information association model (OCIAM) is proposed. An inherent state model, dynamic observation model, and correlation model are formulated in the framework of the OCIAM. The core of OCIAM is the correlation model in which four correlation modes (competitive, complementary, enhanced, and cooperative) are illustrated. A prototype system called MultisensorAssociation is developed for constructing an experiment for flood observation in the lower reaches of the Jinsha River Basin. The results of this case study verify that the OCIAM can be used for the selection of sensors or sensor combinations and has important theoretical and practical significance for multisensor observation integration management.

A multiparameter observation task includes a comprehensive theme with multiple indispensable parameters that need to be monitored simultaneously. However, with limited observation sensor resources for a multiparameter observation task, the spatial misalignment of the coverage area of each parameter decreases the observation efficiency, especially in a space-ground sensor network. To solve this problem, we developed a collaborative planning method in the sensor planning phase. With this method, a space-ground maximal coverage model with multiple parameters (SGMC-MP) was introduced. The proposed collaborative planning method cooperatively utilizes the space-ground sensors to make an observation plan. This method aims to maximize the overlay coverage range among the parameters in the task to reduce the spatial misalignment and improve the utilization of the sensors. The proposed method was applied to a multiparameter observation task in the Three Gorges Reservoir Area in Chongqing. The results indicate that the proposed method exhibits better coverage performance for sensor planning in the multiparameter observation task than the traditional separate planning method. In addition, planning strategies, coverage flexibility, model extension, and algorithm comparison are further discussed.

A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression of EO task, which results in the absence of task process management. The current study proposed an EO task representation model based on meta-object facility to address this problem. The model not only describes the static information of a task, but it also defines the dynamics of an observation task by introducing a functional metamodel. This metamodel describes the full life cycle of a task; it comprises five process methods: birth, separation, combination, updating, and extinction. An earth observation task modeling and management prototype system (EO-TMMS) for conducting a remote sensing satellite sensor observation task representation experiment on flooding was developed. In accordance with the results, the proposed model can describe various EO tasks demands and the full life cycle process of an EO task. Compared with other typical observation task information models, the proposed model satisfies the dynamic and fine-grain process representation of EO tasks, which can improve the efficiency of EO sensor utilization.

Earth observation (EO) tasks play a critical role in addressing the diverse and complex data needs of users. However, fully satisfying these demands presents significant challenges. To cope with this obstacle, cross-platform observation systems utilizing satellites, aircraft, and ground geosensors have been developed to enhance data acquisition through coordinated efforts. Despite the fact that the complementary characteristics of these platforms can improve observation quality, their heterogeneity complicates efficient coordination. To enhance the efficiency of collaborative observation and better fulfill the requirements of EO, numerous space–air–ground coordination methods have been proposed, primarily focusing on decision making and optimization during the data acquisition phase. Nevertheless, a comprehensive synthesis of these strategies remains lacking. This article, therefore, provides a systematic review of existing collaborative observation strategies and decision-making processes in observation planning. We review various sensor assessment and planning techniques, aiming to categorize existing approaches while providing insights into emerging research trends. Additionally, we comparatively discuss examples of these approaches in both routine and emergency observation tasks, offering insights into their practical application across different observation tasks.

Efficient and reliable EO sensor planning depends critically on a precise and fine-grained cognition of sensor observation capabilities, which encompass multidimensional attributes ranging from static parameters to high-frequency dynamic behaviors. However, classical representational paradigms, exemplified by the sensor observation capability object-field (SOCO-Field), face a fundamental scalability barrier: the escalating complexity that accompanies spatiotemporal refinement and the inefficient representation of time-varying observation capabilities. To overcome this bottleneck, this study introduces the Quantum-SOCO-Field (Q-SOCO-Field), a framework designed to innovatively represent and compute the observation capabilities of EO sensors within a quantum computing paradigm. Q-SOCO-Field leverages quantum superposition and entanglement to encode the observation capabilities of the entire dynamic scenario into a unified quantum state. This achieves an exponential reduction in representational complexity. Furthermore, it establishes a computational framework that integrates fundamental quantum operators with Grover's algorithm, enabling the efficient execution of heterogeneous observation capability queries essential for EO sensor planning. This achieves a quadratic speed-up for certain classes of computations. Validation in a flood monitoring-oriented sensor planning scenario demonstrates the framework's high efficacy and significant advantage in cognizing observation capabilities. Taken together, this work lays the foundation for quantum-enhanced spatiotemporal cognition, pointing towards more efficient and reliable EO sensor planning.