Topical Collection on "Geospatial Big Data Management Strategies for Sensor Network-Driven Information Systems"
Deadline for manuscript submissions: 25 December 2024
Collection Editors
School of Information and Communication Engineering, Hainan University, Haikou, China
E-Mail: uzairbhatti86@hotmail.com/uzair@hainanu.edu.cn
Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
E-Mail: muhammadasim.s@chula.ac.th
Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology, Mang, Haripur, 22621, Pakistan
E-Mail: maqbool.khan@scch.at
Balochistan University of Information Technology Engineering and Management Sciences,Quetta, Pakistan
E-Mail: sibghat.ullah@buitms.edu.pk
Department of Management Information Systems, King Faisal University, Al-Ahsa, Saudi Arabia
E-Mail: ygulzar@kfu.edu.sa
Topical Collection Information
Dear Colleagues,
Geospatial database management systems have all of the features of a database management system plus additional geographic information about each data point, like identification, location, shape, and orientation. a system of information that makes it possible to manage, organise, visualise, and analyse geographic data. Smart digital map systems are designated as GIS. The capability of a GIS to run queries on the data, which often have a spatial or geographical component, is one of its main features. Spatial data collections larger than the capabilities of contemporary computing systems are referred to as geospatial big data. GIS integrates location data with all kinds of descriptive information and links data to a map. This offers a basis for research and mapping that is utilised in nearly all fields of study and business. Organisations are dealing with greater geographic information system data than ever before, therefore data management is crucial because they require effective and safe methods to collect, organise, and safeguard this data.
Computer data gathered by satellites that displays land usage, including the locations of farms, towns, and woods, is an example of this type of information. An additional technique that can be incorporated into a GIS is remote sensing. Images and other data gathered from satellites, balloons, and drones are included in remote sensing. Information with a geographic component is referred to as spatial data, or geospatial data. Stated differently, coordinates, an address, city, postal code, or zip code are attached to the data in this kind of information set. A road map is the most obvious example. Among other things, supervising the creation of publications, maps, open data, graphics, and presentation materials falls under the purview of the work. Under the general supervision of an administrative superior who possesses knowledge or experience in geographic information systems, work is completed. Information logged with a location indicator of any kind qualifies as geospatial data. Geospatial data comes in two main forms such as vector data and raster data. Features like properties, cities, highways, mountains, and bodies of water are represented by points, lines, and polygons in vector data. Complexer types of geospatial data analysis are employed in urban planning and environmental monitoring, among other applications. Additionally, location intelligence derived from geospatial data is applied in the commercial sector to enhance decision making and carry out market research. While not every geospatial technology is a form of GIS, all geospatial technology is a type of GIS. More precisely, the term geospatial refers to a broad category that comprises several kinds of mapping and geographic imagery technologies, of which GIS is one.
An organisation plan for leveraging data to accomplish its objectives constitutes a data management strategy. This roadmap makes sure that every step of the data management process from gathering to collaboration works together effectively and efficiently to produce data that is both as useful and simple to manage as possible. The process of gathering, arranging, safeguarding, and preserving an organization data so that it may be examined for business choices is referred to as data management. Contributions are invited from a range of disciplines and perspectives, including, but not restricted to: Geospatial Big Data Management Strategies for Sensor Network-driven Information Systems. Topics for this special issue include, the following:
- Deep learning based big data analysis of the internet of things in smart city digital twins.
- Statistical verifications of energy efficient routing schemes to enhance wireless sensor network performance.
- The potential and obstacles associated with social media and big data for human dynamics study.
- Vehicle recognition using fuzzy wavelet neural networks using remote sensing images.
- Cyber enabled high performance computing accelerates hyperparameter tuning of neural network driven spatial models.
- Utilising massive social media platforms as a scalable sensing system to simulate patterns of energy consumption in real time.
- The difficulties and things to think about when deploying a delay tolerant wireless sensor network.
- Using social media to model real time energy usage patterns as a scalable sensing system.
- Grey filter convolutional based cyber physical system for smart healthcare and quality of service in IoT.
- Entire disaster prevention system built on a wireless sensor network.
- System interoperability in network decision support systems to provide smart mobile system services.
- The real time modelling of a crisis situation through automated event interpretation and contextualization.
Collection Editors
Dr. Uzair Aslam Bhatti
Dr. Muhammad Asim Saleem
Dr. Maqbool Khan
Dr. Sibghat Ullah Bazai
Dr. Yonis Gulzar
Keywords:
- Geospatial
- Sensor etwork
- Remote sensing
- Disaster prevention
- Real time modelling
- Big Data management
- Information system