Future of urban remote sensing and new sensors

KEYWORDS:

• Smart City
• sensing
• internet of things
• enviromental monitoring
• data analytics

Remote sensing plays a prominent role in studying earth systems, where urban areas are not an exception. Presently, cities are growing rapidly, and there is an increasing need for state-of-the-art land topography, land use maps, and other spatial data related information. This information is most vital as they form the key source of land use planning, natural resource management, and various other urban planning and management processes. The data extracted from urban remote sensing have profound applications and is widely used to analyze the urban environments: it enables policymakers to identify areas of infringement and environmental sensitivity, assists in settlement categorization, infrastructure and service endowment mapping, green infrastructure improvement, and regional planning accomplishments. Hence, it is evident that urban remote sensing and advances in sensor technologies empower sustainable urban planning and pave the way for sustainable development if used appropriately.

This special issue is designed to focus on the future of urban remote sensing and new sensors. It aims to cover the major areas of urban remote sensing from future aspects. First, it aims to explore advances in alternative sources for urban feature extraction. Next, it emphasizes innovative image processing algorithms and sensor techniques to derive accurate and consistent information from remote sensor data. Finally, it intends to uncover state-of-the-art data analytics techniques and predictive modeling methods. Achieving these three objectives are most crucial as it greatly assists in dealing with the emerging demands of urban planning processes.

Following the peer-review process, three articles were qualified for publication in this special issue in accordance with the evaluation standards. The following essential characteristics highlight the recognised works’ notable technological advancement:

The first article, entitled “Geo Spatial Based Real Time Monitoring on Eutrophic Evaluation of Porunai River Basin for Pollution Risk Assessment” (Gopikumar et al., 2022) have investigated the optimization of the wastewater sources by exploiting GPS-X, the recognized software approved by International water association. The authors have initially analyzed the concentration of organic and inorganic heavy metals in the Porunai river (India) and, later, they have proposed a simulation-based methodology to impact on the nutrient removals from the designed reactors. The proposed work aims at enabling novel opportunities to create a pollution-free environment that prevents diseases and supports innovation.

The second article, entitled “Neighbourhood oriented TDMA scheme for the internet of things-enabled remote sensing” (Khan et al., 2021), instead, has been focused on the specific domain of the Internet of Things (IoT) and proposed a neighbourhood-enabled TDMA (time-division multiple access) scheme which ensures the concurrent communication of multiple devices with a common destination device, preferably with a minimum possible packet collision ratio. Such a technique is demonstrated to be effective in those IoT networks where the formation of a balanced clustering mechanism is not always feasible because the deployment of the IoT node is intrinsically random. Simulation results have verified that the proposed approach outperforms the state-of-the-art solutions in terms of slots waiting time, empty slots utilization, throughput and APDR ratio, preferably in event-driven application areas.

The third article, entitled “Research on the application of big data visualization technology in urban road congestion” (Guo & Xu, 2022), presented a study on the mechanism of traffic congestion diffusion under the condition that users have real-time traffic information and analyzed the urban road congestion situation combined with big data visualization technology. Authors have elaborated theoretical guidance for the formulation of traffic congestion management measures and focused both on the Susceptible-Infected-Susceptible (SIS) transmission model and on the method of state transition probability in order to construct an interactive dynamic model of traffic congestion propagation and early warning information propagation in a multi-layer network. The obtained experimental results show that the big data visualization technology introduced in this paper can play an important role in dealing with the urban road congestion issue.

These articles address different topics within the special issue scope, both from a theoretical and practical viewpoint and with a multidisciplinary approach. We thank all the scholars and referees for their timely and useful contributions. We are thankful to the journal’s Editor-in-Chief for permitting us to administer a special issue of this esteemed journal.

Disclosure statement

No potential conflict of interest was reported by the author(s).