Abstract
Geospatial intelligence has become a vital tool for businesses to navigate complex spatial relationships and make informed decisions especially in the advent of Industry 5.0 which is characterized by hyperconnectivity, automation, and human-technology collaboration. This chapter explores the intersection of Industry 5.0 and geospatial science and the key requirements for the integration of geospatial science in Industry 5.0 while also highlighting how advancements in location intelligence technologies are reshaping industries, enhancing operational efficiency, and driving sustainable development. From smart cities to precision agriculture, the integration of geospatial data and analytics revolutionizes how organizations optimize workflows, mitigate risks, and deliver value in an increasingly interconnected world.
TopIntroduction
Industry 5.0 represents the latest phase in the ongoing evolution of industrial practices, characterized by the convergence of digital technologies, automation, and human-centric approaches (Longo et al., 2019; Romero et al., 2016). Building on the foundations laid by Industry 4.0, which emphasized interconnectedness and data-driven decision-making (Romero et al., 2016; Moller et al., 2022), Industry 5.0 places a stronger emphasis on the symbiotic relationship between humans and machines (Guerin et al., 2019). In this paradigm, advanced technologies such as artificial intelligence (AI), Internet of Things (IoT), robotics, and augmented reality are maximized, not only to optimize processes, but also to enhance collaboration between human workers and automated systems. Unlike its predecessors, Industry 5.0 acknowledges the irreplaceable role of human creativity, intuition, and empathy in driving innovation and problem-solving within industrial environments (Carayannis & Morawska-Jancelewicz, 2022; Alves et al., 2023).
Geospatial science is a multidisciplinary field that involves the acquisition, analysis, interpretation, and visualization of spatial data pertaining to the Earth's surface and its features. It utilizes technologies such as geographic information systems (GIS), remote sensing, global positioning systems (GPS), and spatial statistics to capture, store, manipulate, and present spatial information in meaningful ways. Geospatial science enables individuals and organizations to understand spatial relationships, patterns, and trends, providing valuable insights for decision-making across various disciplines including urban planning, natural resource management, emergency response, and business operations.
Location science and geospatial intelligence represent the fusion of geospatial science with advanced analytics and decision-making methodologies (Wang et al., 2013; Yang et al., 2013). Location science is the study of spatial relationships, patterns, and phenomena, which employs techniques from geography, mathematics, and computer science to analyse and interpret spatial data. Geospatial intelligence on the other hand, involves the acquisition, integration, analysis, and visualization of geospatial information to derive actionable insights for decision-making across various domains (Bacastow & Bellafiore, 2009; Yue et al., 2015).
The intersection of Industry 5.0 and geospatial science is highly significant for driving innovation, efficiency, and sustainability across industries. The incorporation of geospatial intelligence into Industry 5.0 provides organizations with the much-needed opportunity to gain deeper insights into the spatial dimensions of their operations, thereby enabling them to optimize resource allocation, mitigate risks, and enhance situational awareness. From smart manufacturing facilities that utilize real-time location tracking to optimize production workflows, to smart cities that employs geospatial analytics to improve urban infrastructure and services, and even to smart precision agriculture, the integration of location science and geospatial intelligence in Industry 5.0 opens new possibilities for creating value and addressing multi-faceted challenges in this world with increasing interconnectedness (Ajayi, 2023).
Key Terms in this Chapter
Digital Twin: A virtual representation of a physical object, system, or process that enables real-time monitoring, simulation, and analysis to optimize performance and inform decision-making.
Geospatial Intelligence: The collection, analysis, and interpretation of geospatial data to derive actionable insights and support decision-making in various domains such as defense, security, disaster management, and urban planning.
Artificial Intelligence (AI): The development of computer systems that can perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision-making.
Location-Based Services (LBS): Applications that utilize location data from GPS, Wi-Fi, or cellular networks to provide contextually relevant information, recommendations, or services to users based on their geographic location.
Edge Computing: The decentralized processing and analysis of data at or near the source of data generation that enables low-latency, real-time insights, and actions without reliance on centralized cloud infrastructure.
Location Science: A multidisciplinary field that encompasses the study of spatial patterns, relationships, and processes to analyze and understand the geographic distribution of phenomena and inform decision-making in various domains such as urban planning, transportation, and environmental management.
Geospatial Analytics: Involves the analysis of spatial data to uncover patterns, trends, and relationships that provide insights into geographic phenomena, enabling informed decision-making and predictive modeling.
Virtual Reality (VR): A computer-generated simulation of a three-dimensional environment that users can interact with and explore using immersive technologies such as headsets or goggles, enabling realistic, interactive experiences for training, visualization, and decision support.
Spatial Data Infrastructure (SDI): A framework of policies, standards, technologies, and institutional arrangements that facilitate the collection, management, sharing, and use of geospatial data across organizations and sectors which enables interoperability and collaboration in geospatial information management.