Full-view coverage of a space area with a camera sensor network (CSN) is key to monitoring tasks like security monitoring. Unlike traditional CSN challenges that focus on mere target detection, the full-view area coverage problem (FVACP) demands recognition of targets irrespective of their locations or orientations. However, prior approaches often neglect real-world spatiotemporal context constraints like buildings and pedestrian dynamics, leading to inefficient CSN deployment. Moreover, FVACP’s complexity, being an NP-hard issue, underscores the need for effective optimisation strategies. Recently, quantum annealing (QA) has emerged as a promising solution, which potentially outperforms classical computing in optimisation tasks. Therefore, this study proposes a QA-based FVACP optimisation framework. It addresses spatial constraints by optimising candidate deployment points and tackles temporal constraints by optimising sensor orientations. These optimisation tasks are converted into quadratic unconstrained binary optimisation problems, which are suitable for QA techniques and benchmarking against classical methods. The effectiveness of the framework is validated through facial recognition-oriented experiments. Results demonstrate not only efficient CSN deployment with larger benefits and fewer cameras but also confirm the superiority of QA over classical computing given that it delivers approximate optimum outcomes across various scenarios. Consequently, CSN monitoring capabilities in real-world applications can be enhanced.

Dynamic changes in traffic conditions cause spatiotemporal variation in traffic monitoring demand. It is, therefore, necessary to conduct efficient road monitoring to identify dynamic abnormal situations, especially in peak traffic periods. Recently, unmanned aerial vehicles (UAVs) have become an attractive solution to this problem. However, UAV monitoring routes suffer from time limitations during peak traffic hours. To optimize UAV monitoring routes during rush hours, we develop a route planning method incorporating spatiotemporal variations in monitoring demand, in which we introduce a team orienteering arc routing problem with time-varying profits (TOARP-TP) and construct a corresponding mathematical model. The TOARP-TP is an extension of an already existing routing problem, team orienteering arc routing problem (TOARP). An iterated local search (ILS)-based algorithm is designed to solve the large instances of this problem. To verify the proposed method, we conduct sets of numerical experiments with the Sioux Falls road network in South Dakota, US, and a case study is applied using Wuhan, Hubei, PRC. The results demonstrate the efficiency and practicality of our method in optimizing UAV traffic monitoring routes during rush hours. Furthermore, we discuss a strategy for scenario determination and method selection in UAV route planning.

Scene classification of high-spatial resolution imagery (HSRI) includes various potential applications in various fields. Recently, deep convolutional neural networks (CNNs) have achieved competitive performance as a result of the powerful capability of feature extraction. In this paper, we propose a multi-level improved circle pooling (MICP) method with the pre-trained CNN-based model to enhance the discriminative power of CNN activations for scene classification. Specifically, an improved pooling strategy is presented to generate annular subregions without padding operations in traditional concentric circle pooling. Then, we extract the pooling features in these subregions under different levels and build a holistic representation by fusing these multi-level features. MICP is an effective and simple strategy enriching rotation insensitivity and multiscale spatial information. According to the experiments conducted on three challenging HRSI scene data sets, the proposed pooling method achieves similar or better classification accuracy compared to the other CNN-based scene classification methods. Moreover, a comprehensive discussion regarding the effect of data augmentation reveals that the proposed method can enhance the rotation insensitivity of CNNs for the HRSI scene classification.

Early warning of cyanobacteria bloom is very important for water ecological management of lakes. Thus, Based on Landsat8 OLI data, combining the trophic state index (TSI), cyanobacteria and macrophytes index (CMI) and floating algae index (FAI), we proposed a risk zoning framework of cyanobacteria bloom, and applied it in Lake Longgan (China). Eutrophication frequency of Lake Longgan has worsened since 2017. But the maximum eutrophication proportion occurred on 16 February 2016. With a deeper analysis on 16 February 2016, we find that the risk area of cyanobacteria bloom in Lake Longgan is distributed horizontally from the east bank to the center of the lake, and mostly is mid risk. High risk areas are mainly distributed in the east coast, showing a southwest direction. Therefore, the proposed risk zoning framework provides a useful approach to obtain the pre-warning information of cyanobacteria bloom in some Eutrophic and macrophytic lakes.

When facing a specific emergent geographical environment observation task (GeoTask), people need to be able to handle reliable and comprehensive disaster information in the shortest possible time. The lack of effective cognition of multi-sensor collaborated observation capability is a hindrance to performance. By adopting the GIS object field concept as the bottom framework, we propose a sensor observation capability object field (SOCO-Field) with sensor observation capability particle (SOC-Particle) as its core. SOCO-Field integrates SOC-Objects and GeoField for the discovery and association of sensors. SOC-Particle objectively exists on every location point in the geospatial environment, and SOC-Particles in space-continuous areas can further aggregate into SOC-Particle cluster to represent single- or multi-sensor-associated observation capability information. SOCO-Field includes three basic association behaviours and four further association behaviours to solve associated observation capability, in which the dynamic GeoField is the influential factor. An experiment on flood monitoring in the lower reaches of Jinsha River Basin is conducted. The sensor planner can view any sensor combination’s associated observation capability under a specific association mode and can effectively dispatch a multi-sensor for collaborated observation due to the effective modelling of associated sensor observation capability information (SOCInfo).

The sensor web is a new method in the earth observation field, comprising sensors that can sense, compute, and correspond with the World Wide Web. The Chinese Integrated Earth Observation Sensor Web (CIEOSW) project is a five-year national basic research program conducted by Wuhan University since 2011 and is aimed to address the following major challenges: (1) the lack of collaboration within the satellite observation system, (2) the absence of a coupling mechanism in heterogeneous spaceborne -- airborne -- ground sensors, and (3) the limited connection between monitoring and decision-support services. For two years, the CIEOSW project has strived to achieve the following: (1) a theory on the coupling and modeling of the Earth observation sensor web (EOSW), (2) an event-driven multi-sensor collaborative observation method, (3) an EOSW fusion and assimilation method, (4) an EOSW-based information extraction and rapid change detection method, (5) a task-oriented EOSW focusing service model, and (6) an epicontinental environment observation and analysis of typical areas in China.

In the era of high-frequency occurrence of natural disasters, users are more urgently concerned with the sharing of the satellite sensor resources information and coordinating the complement of sensor observation. However, the capacity of discovering, retrieving and visualizing the sensor resource information accurately based on heterogeneous sensors over sensor network is very limited. This paper proposes the system architecture for effectively managing those heterogeneous and multiple sensors and their information, which is inspired by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) Initiative and based on one of its information model- Sensor Model Language (SensorML) of which Process Model is the core. The prototype "SensorModel VI. 0" is designed and implemented used to construct the standard model for unified management of multiple remote sensing satellite sensor resources information and demonstrate the model-based retrieval and visualization of related remote sensors and their information, which promotes the comprehensive accessing and collaborative planning/controlling the available remote sensor's information in time-critical disaster emergency.

The Sensor Web is a coordinated observation infrastructure composed of distributed resources that can behave as a single, autonomous, task-able, reconfigurable observing system that provides observed and derived data along with the associated metadata by using a set of standards-based service oriented interfaces. But these resources, including sensor, data, platform etc., with different characteristics are hard to be fused well. This paper analyzes concepts of various types of sensor web resources for atmospheric observing, focusing on how to access different types of resources expediently, abstract essential features of these sensor web resources, and construct a Sensor Web Resources Ontology for Atmospheric Observation (SWRO-AO) represented by Web Ontology Language. The SWRO-AO could serve as a knowledge repository of sensor web resources for the research and application community in atmospheric science.