Trends and Technologies Shaping the Geospatial Industry (series)

The future of geospatial technology is poised to play a pivotal role in the evolution of Web 3.0 and the Metaverse. These technologies are converging, bringing people and machines together to create a world where highly detailed digital representations of reality guide decision-making. In this landscape, the Geoverse emerges as a concept that goes beyond the Metaverse, intertwining digital worlds, predictive analytics, artificial intelligence, and real-time information. It’s a realm where geospatial data becomes foundational, offering innovative solutions to pressing global challenges like climate change, pandemics, natural disasters, sustainable development, and more.

Below we examine the key emerging technologies and trends that will influence, and in many cases are already influencing, the future of Geospatial Technologies.

In part one of this blog series, we will look at Technology and Data.  Part 2 will cover Solutions, Process, People and Politics.

Technology Trends

Technology trends have a significant influence on emerging geospatial technologies. Under the Technology section key topics and points include:

Connectivity –has been described as the most critical enabler of digital transformation over the next 10 years (Walter, 2020).   Opportunities will arise from connectivity such as the ability to further connect the consumer, utilities, transport, healthcare, financial, retail, and manufacturing sectors (amongst others).  

Platforms –provide a place for the exchange of information, goods, or services between producers and consumers, and leverage the community to provide enhanced value to everyone within the ecosystem. They will continue to be key drivers behind reaching customers, reducing costs, and optimising resources.

Capture – refers to various remote sensing and surveying technologies that allow organisations to perceive and capture reality:

• over the next decade, the technologies and data creation methods that will have the greatest impact on geospatial information management are predicted to be mobile data collection, crowdsourcing for real-time data, and social media platforms (Walter, 2020). These forms of data collection will enable accurate, near real-time applications that are increasingly demanded.  
• mobile technology such as satellites, drones and autonomous vehicles will provide ever greater variety and volume of data, at higher frequency, greater resolution, and lower cost.
• drone acquired LiDAR and oblique imagery will become the norm.
• drone and UAV-based information, enabling 3D data capture, will become more available, increasing the speed at which Smart Technology and Digital Twins can be established. 
• Earth Observation information will become more available, with more pre-processed information supply, allowing quicker and easier surveillance, change detection and disaster monitoring, for example. 
• geospatial data will increasingly be crowdsourced (active and passive), providing a greater range of data, at increased frequency. 

Positioning – satellite positioning technology is expected to improve due to a new generation of navigation satellites and receivers.  Additionally global positioning is likely to deliver higher accuracy (horizontally and vertically) over the next decade (Walter, 2020).  Increased accuracy will benefit different use cases including Digital Twins and intelligent transport systems.

Computing – already cloud computing has transformed the management of data and infrastructure. As cloud computing services continue to mature, more tools, platforms and applications will become available over the next decade.

Automation – implementation of machine repeatable processes will free up time-consuming and resource-intensive computing tasks (e.g., data capture, data management, analytics etc). Automation will increasingly be used to operate machines, answer user queries meaningfully, guide autonomous vehicles and much more.

Intelligence – Artificial Intelligence (AI) refers to computer science and systems that perform tasks generally associated with human cognition and intelligence (Mannam, 2021). It covers two fields of learning automation relevant to geospatial technology: 

• Machine Learning (ML), where an algorithm is trained using a pre-identified set of data – statistical models. 
• Deep Learning (DL), where an algorithm trains itself to identify data automatically – neural networks.

AI is predicted to evolve rapidly, speeding up processing and freeing up organisations from time-consuming and resource-intensive manual tasks (e.g., building footprint digitisation, vegetation clearance, wetland identification) and will enhance decision-making procedures. 

Visualisation – 4D geospatial visualisation using Extended Reality (XR), and Real-time 3D (RT3D will be become important tools for geospatial visualisation, key to future customer platforms.

Data

Geospatial data is the foundation of geospatial analysis, modelling, simulations, and visualisations.  As technology has advanced, so too have the dataset types, formats, and protocols, opening up new avenues for geospatial advancement.  The report looks at eight ways in which data will be changing. Under the Data section key topics and points include:

Imagery –aerial imagery will continue to be captured by manned aircraft due to its high spatial resolution and wide coverage, but Remotely Operated Aerial Systems (ROAS) technology (e.g., drones) will be used for smaller areas, for timely, accurate, high-resolution data. Additionally high-resolution rapid-revisit satellite imagery will become a valid alternative to aerial imagery.

3D GIS Data –will become more readily available from mobiles, crowdsourcing, social media (geotagged photos) and autonomous vehicles.   This data will be important for Smart Cities, Digital Twins and the framework for the Metaverse. 

Point Clouds –Point clouds are generally produced by 3D laser scanners, LiDAR, or by photogrammetry software:

• As LiDAR technology continues to improve, and costs decrease, the application of LiDAR to capture real-world phenomena is expected to increase. 
• LiDAR generated point clouds will be delivered via Content-as-a-Service (CaaS) programmes in the future, as opposed to special acquisitions (e.g., the Provincial Growth Fund LiDAR programme which will result in continuously captured data provided to customers on demand. 
• LiDAR data will potentially become the foundation of topographic and 3D asset information in the future. 
• AI-based processing and computing advances will make it easier to automatically derive useful, trusted, datasets from raw point clouds. 

Data Cubes (or geo-data cubes, or space-time cubes) –are a form of geospatial data structure where data is stored in multiple dimensions. These are becoming the most common way to integrate and utilise geospatial data sourced from Earth Observation (EO) platforms such as satellites and UAVs.  They also package and deliver analysis ready datasets in a format that is readily accessible to GIS and AI-based automated analyses for prediction, classification, and time-series clustering. 

Linked Data –is considered a significant trend that may lead to addressing issues around discovery, access, exploration, and use of geospatial data through the Web over the next five to ten years. Geospatial systems will work with Linked Data in two ways: 

• They will connect to and consume linked data, to build relationships between spatial information that is not necessarily spatial in nature. 
• They will create and produce linked data, to present relationships between spatial information that is not necessarily spatial in nature. This in turn could be used by algorithms and AI to answer spatially related questions more meaningfully. 

Big Data –because of the huge growth in data generated by machines, organisations are moving towards cloud-based big data systems. These are specifically optimised for handling the volume, velocity, and variety of data associated with big data.  Additionally, rather than trying to centralise data storage in a Data Warehouse, which requires complex and time-intensive data extraction, transformation and loading, organisations are instead moving towards Data Lakes.

At the GIS desktop and enterprise level, direct connectivity to these cloud-based services has recently been introduced and will continue to grow in terms of supported providers and analytical capability.

Networks –networks will be important in future technologies, such as Smart Cities, Smart Grids, Intelligent Transport Systems and Digital Twins. Hydrological modelling of river networks will continue to be important as climate change impacts weather patterns. Knowing where floods may occur or where irrigation may be affected, based on catchment rainfall changes, will be an important tool in mitigating and managing future climate related disasters.

Geospatial networking tools are evolving to meet the demands of future utility and trace networks e.g., developing water, storm water and wastewater functionality, as well as hydrological and rail networks, providing advanced analytic functionality such as the ability to set flow direction, run a trace on the network, or generate network diagrams.

Knowledge –knowledge graphs allow users to explore and analyse relationships, not only in spatial data but non-spatial (structured and unstructured data) as well, in a single view. Knowledge graphs are a relatively new data structure for geospatial and are seen as a key mechanism to support data discovery, collaborative investigations, link analysis, and information sharing in the future.

References:

Walter, C. (2020, August). Future Trends in geospatial information management: the five to ten year vision – Third Edition. Retrieved from UN-GGIM

Mannam, S. (2021, October). Artificial Intelligence, Machine Learning, and Deep Learning: Are They All the Same? Retrieved from Journal of Young Investigators