The UK Geospatial Commission has identified eight emerging technologies that could impact the geospatial industry and grow UK economy. This is part of a new report which came out on Tuesday, August 28, 2019. The eight technologies identified are:
- Cameras, Imaging and Sensing;
- Unmanned Vehicle Systems and Drones;
- Survey, Measurement and Scanning;
- Artificial Intelligence;
- Smart Sensors and Internet of Things;
- Immerse Technologies;
- Simulation; and
The report is funded by the Geospatial Commission and published by PUBLIC, an organization that helps technology start-ups work better with the public sector. It analyzes commercial opportunities for use of geospatial data, considers the maturity of each technology in the UK, and provides numerous case studies and success stories.
The UK government identifies location data as a valuable tool for both the public and private sector, helping them make better decisions, that could range from tackling crime hotspots or finding the quickest routes for emergency services, to deciding where best to locate warehouses. Consequently, the Geospatial Commission was launched in 2017, and supported by £80 million of funding over that time to drive the move to use this data more productively.
Cameras, Imaging and Sensing
Definition: “The platforms used to collect EO data and the equipment and instruments used to collect, store and process EO data.”
The ongoing digital revolution is unleashing an era of near-persistent observation. The range of imaging sensors are becoming as diverse as they are different with space, airborne, drone, vehicle, and ground-based imaging and sensing, as well as new sophisticated camera networks.
This has opened up tremendous opportunities for the geospatial industry and the advances in technology in this area have been tremendous. First, we have seen the development and widespread adoption of new platforms for collecting EO data, including High Altitude Pseudo Satellites (HAPS), small satellites and drones, which led to resulted in greater coverage across satellite constellations, more accurate targeting, increased capture options (time and place) for customers of EO data and the ability to undertake persistent observation and monitoring.
Second, there have been significant improvements in the resolution and accuracy of cameras and sensors, which has unlocked a number of new geospatial use cases.Other than the most obvious defense and security sectors, the greater accuracy that comes with the improved EO capabilities is crucial for sectors such as infrastructure, asset management and agriculture.
The increase in the range of sensing capabilities available in Multi‑spectral and Oblique Imaging, Thermal IR and LiDAR will benefitindustries that require highly granular ground level data, such as agriculture and farming. Additionally, there will be opportunities in new sectors with the combination of aerial imagery with other map data and land use data, for instance deeper and more nuanced understanding of urban environments, enabling highly accurate 3D models.
At the ground-level, vehicle sensors are becoming increasingly common, with a number of different public and private sector organizations undertaking vehicle drive-by imaging and mapping surveys. These projects are being completed for a wide range of applications, including asset management, 3D city modelling, street canyon imaging and utility surveys. In addition, drive-by surveys are used as data sources for global on-line mapping, imaging and navigation platforms.
Static and Backpack Imaging is another emerging trend in ground-based imaging. The emergence of backpack-mounted imaging systems and static camera options has been brought about by the requirement to image and texture 3D city models, provide imagery context for indoor spaces and support for floor plan mapping.
Unmanned Vehicle Systems and Drones
Definition: Unmanned Vehicle Systems are vehicles that are either controlled remotely or operate autonomously, by sensing their environment and navigating including without human intervention.
Currently, there are three areas of major geospatial interest relating to drones. First, drones are being used as an aerial platform for EO and mapping projects. Second, they are being used as delivery systems for the delivery of lightweight packages, postal services and medicines. In recent times drones are being developed to carry and transport passengers within urban areas.
Drones are also increasingly being used to support the monitoring and inspection of the condition of energy distribution assets and networks. In this, drones are leveraging Computer Vision to recognize images of infrastructural assets, using deep networks and convolutional neural networks to identify different types of faults and anomalies.
As drones become an integral part of urban environments, they are likely to be more frequently as part of the wider urban traffic management initiatives. Further, research is also underway to develop future Swarm Capabilities in command and control systems. In addition to policing and security, Drone Swarms will become increasingly applied to the monitoring of urban infrastructure.
Over the next decade, we should expect to see the use of drones to extend from EO, mapping and monitoring into the domains of parcel and passenger delivery. For the geospatial community, the primary goal will be to provide first and last mile geospatial reference and navigation data to support these systems. This includes, as a minimum, systems such as geo-awareness, geofencing and GNSS- based locational requirements.
Survey, Measurement and Scanning
Definition: Survey, Measurement and 3D Scanning systems provide the foundation upon which the geospatial ecosystem is built. The report focuses on downstream GNSS chipsets, GIS data capture, 3D scanning hardware, software and value-add services.
These technologies underpin above-ground, underground and indoor positioning systems, in addition to being crucial for enabling numerous emerging technologies, such as Internet of Things, Autonomous Vehicles, Building Information Modelling, Virtual Reality, Artificial Reality and Mixed Reality.
More accurate and precise positioning systems are central to operation of complex sensors systems at scale, such as IoT systems, where connected devices collect and communicate location intelligence by transmitting signals in real-time.
At the ground-level, there are a number of sensors that are becoming more widespread in surveying and measurement. Telemetry is the traditional process by which ground-based remote sensors transmit data to stationary data acquisition systems. Advances in ground sensors can enable Ubiquitous Positioning, where positioning can extend to indoor and more remote locations. This allows surveying and measurement to be conducted using multiple remote sensors, and is an important enabler of IoT systems and an ‘always on’ concept of operation, which allows different sensors to communicate continuously.
LiDAR is a well-established geospatial technology that involves using pulses of light to capture and model a feature or an area environment in three dimensions. It can be applied across multiple sectors and requirements, including mapping an industrial site in granular detail, providing ground model data for flood modelling, and providing the spatial context for developing immersive environments.
Indoor positioning is a particularly significant opportunity for the geospatial community since developments in this area has struggled to match the levels of innovation and widespread uptake seen in outdoor positioning. However, applications utilizing a mixture of bluetooth, WiFi and magnetic positioning can connect to smartphone without requiring any additional infrastructure.
Crowdsourcing or Volunteered Geographic Information (VGI) is another key emerging geospatial technology area. Other new techniques such as Analysis Ready Data (ARD), where EO data is processed into a useable form in real-time, and Robotic Process Automation (RPA) are being leveraged to support automated Feature Extraction for thematic and national mapping requirements.
Definition: Artificial Intelligence refers to systems or programs that can complete tasks that would normally require human intelligence, such as data analysis, visual perception, speech recognition or decision-making.
AI will fundamentally change how analysis supports the day-to-day business operations, providing enhanced intelligence opportunities for entities a multitude of sectors. Machine Learning (ML) involves building statistical models based on sample data to make predictions or decisions without being explicitly programmed to perform the task. This has been effectively leveraged by geospatial companies to allow systems to derive insights and make decisions from structured and unstructured datasets with minimal human intervention. The EO industry in recent times has been able to apply change detection algorithms to automatically identify areas of change, including the identification of new areas of deforestation, urban development, or to support damage assessment mapping following disasters.
A number of service providers in the geospatial community have also moved towards supporting insurance intelligence requirements as ML becomes more effective at assigning risk- profiles to certain customer behaviors. Within the car insurance market, for example, patterns of driver behavior are now being processed and analyzed by ML systems with onboard devices, and linked to location to provide tailored analysis.
In Location Intelligence, GNSS and positioning technologies are being positively affected by AI, affecting industries such as logistics and navigation systems. For instance, through processing millions of GPS points in real time, systems will be able to forecast changing road and traffic conditions for truckers and haulers.
LiDAR and Radar will also require enhanced automated analytical capabilities if future applications such as Connected and Autonomous Vehicles (CAV) are to become a reality. The E-CAVE (Enabling Connected and Autonomous Vehicle Environments) project, led by Ordnance Survey, aims to explore how CAVs can share positioning and safety information in real-time. Further, through this project, OS is also engaged in supporting and collaborating with CAV testing across four CAV test bed projects, announced in 2018.
Combining AI with other emerging technologies such as IoT will also bring about a number of new opportunities for the sector. The increasing number of sensors within Smartphones and other tracking devices require advanced analytics to draw meaning from the information that they transmit. For example, embedded health trackers and wearables increasingly assist health professionals in recognizing the signs of illness, aided by advanced diagnostic algorithms. Going forward, analytical systems will potentially be able to map population health metrics across entire communities, the adoption of advanced analytics will only improve such capabilities.
Smart Sensors and Internet of Things
Definition: Smart Sensors and the Internet of Things are the networks of physical objects that contain embedded technology to sense changes in their internal states or in the external environment, and communicate this information with other connected devices.
To realize the true value of IoT, it is necessary to have greater integration between multiple sensors and smart devices, especially in the context of ‘Smart Cities’ or other large connected ecosystems. Perhaps the most highly anticipated use of geospatial technologies and sensors is in the Smart City domain. Smart City applications rely on an integrated IoT network of devices across a set of different services and businesses.
Collation and consolidation of data from multiple geolocated and timestamped sensors and public sector datasets are becoming crucial to monitoring the security and resilience of assets in urban areas. Similarly, data-gathering motion sensors offer opportunities for continuous and real-time footfall analysis. Similarly, it is now becoming increasingly common to see mobile phone sensor data being used to analyze transport activity. There are a number of emerging companies using Crowdsourced mobile data to provide transport insights to cities and local councils.
In agriculture and farming, IoT solutions using geospatial technologies are seeing significant results. The main aim of IoT in farming is to monitor important environmental factors such as water quality, soil condition, ambient temperature, moisture, irrigation, and fertilizer for improving crop production.
There is an opportunity for geospatial analysis used in sensors for monitoring and supporting the management of future smart energy grids. These grids are composed of connected devices and sensors (including smart meters and smart appliances) and are capable of detecting changes in local energy usage in real-time. The roll-out of the smart grid relies on highly accurate geospatial information to ensure that demand analysis is accurate and reliable.
Definition: Immersive technologies emulate physical environments through the creation of a digital space allowing visualisation and interaction with an environment.
Within the geospatial community, these technologies are typically used to produce advanced geovisualisation environments and to allow presentation of geospatial data. Any level of geospatial detail can be presented within an immersive reality, from a skeleton geometry in 3D, through to an Intelligent Point Cloud which updates as a real-time digital representation of a building. Geospatial acquisition technologies, such as LiDAR, provide a greater level of detail and context in the immersive model. Some GIS technologies are also becoming integrated with immersive systems, and we are starting to see the early adoption of these solutions in certain niche sectors.
A significant amount of Virtual Reality and Augmented Reality innovation is taking place in the military domain. In particular, these technologies are being used to improve the training conditions for soldiers in their preparations for conflict. Using wearable mobile devices, AR can transport users into new geographic locations to bring about context-awareness remotely. This is now broadening out to a whole new set of services within the armed forces, with pilots underway to assess whether maintenance engineers can give their expertise from anywhere in the world. However, these are early trials and this technology will take a number of years to reach full maturity.
While in its relative early stages today, more R&D and innovation work in the geospatial enablement of immersive technologies over the next five years, could just make this a highly important domain area for geospatial exploitation going forward.
Definition: Simulation technologies allow a user to model various scenarios within a digital environment. This involves building digital representations, or ‘Twins’, of a specified geographical area, and manipulating relevant variables to model likely effects.
Today, maps are increasingly becoming digital representations of a specific time and place, with users able to interact with and manipulate the underlying data. Geospatial simulations are already being widely applied in specific thematic areas such as flooding simulation and road traffic movement. The most significant future opportunities relate to the integration of thematic models at a city-wide, or even national, level.
3D Modelling is now a commonplace service offered by geospatial companies, and is expected to grow as the emerging technologies in this area mature. With advances in AI and ML, multi-dimensional modelling and simulation will improve maintenance and decision-making processes for organizations. GIS technologies also offer a number of benefits for modelling facility management scenarios, including space management, visualization and planning. Facility Operating Systems, which manage large operations such as airports, industrial and power plants will be the main beneficiaries of such developments. Increasingly, Smart Cities will coordinate these systems with smart sensor capabilities which will model and simulate urban planning. Fields such as construction and development are also increasingly adopting continual sensor monitoring and reporting on assets.
The greatest opportunity, and also challenge for the sector, lies in scaling these thematic modelling efforts to undertake city-wide scenario simulations. In general, however, for these simulations to be effective, there is a need for greater access to public and private datasets across a city. Digital Twin and Simulation technologies are growing rapidly in the geospatial sector. These technologies have the potential to support multiple future
Definition: Connectivity technologies refer to the communications infrastructure across which geospatial data is transferred and exchanged. This includes satellite communications, as well as fixed and mobile telecommunications networks.
The higher speed and lower latency offered by 5G will allow data to be transferred more efficiently, cost-effectively and securely. Further, the geospatial community and geospatial data also has a key role supporting the planning, roll out and operation of our communications infrastructure. In addition to the ongoing investments to achieve full-fiber networks, going forward, the single largest game-changer in geospatial connectivity is likely to be 5G. 5G technologies use existing and high-frequency spectrum, enabling rapid data transfer speeds, making it easier to download and upload on mobile devices.
In addition, there have been dramatic advances in the network capacity of satellites over the past decade. Going forward, small and miniaturized satellite constellations — such as those operated by OneWeb — will present new global network and communications opportunities. Two further important connectivity assets in the UK are the cross-governmental Public Service Network (PSN) and Emergency Services Network (ESN), which will support communications between front-line officers and bluelight services. These emerging high-capacity, fast, and low latency communications channels will enable geospatial data to be transferred at volume, at speed and securely.
Improved network capacity and data speeds will allow the expansion of the use real-time image and video streaming, including HD and Ultra‑HD Video. One clear use case for improving network capacity in public spaces is for persistent monitoring and surveillance using video and image streaming for specialized policing and security services. Better connectivity will also allow more geospatial players to leverage the scalability and computing power offered by Cloud computing. Improved connectivity to the cloud will allow data to be stored and processed in Cloud centres in real-time, allowing constant connection between field, office and Cloud.
Improved local connectivity will also allow geospatial actors to develop new Edge Computing strategies. Edge Computing relies on local networks of micro data centers close to the sensor or scanner, and is particularly important for delivering lower-cost data processing and storage for IoT systems.
The reduced latency of 5G technology will be crucial to the future adoption of Autonomous Vehicles and drones. Since autonomous vehicles require extremely fast networks with no delay or lag to operate, this reduced latency will also better support the effective streaming of real-time imaging data on the move, as well as continuous sensor availability to support spatial analysis at scale in real time. Constant and secure connectivity will also be crucial for drones and other unmanned vehicles when performing missions covering large land areas, including surveying, mapping and the monitoring of energy grids.
Improvements in Satellite Communications are enabling high- volume geospatial data to be transmitted across these networks, which could previously only handle low volume audio data such as voice. To support the significant investments and operation of our communication infrastructure, telecommunications and utility companies require a wide range mapping, survey and geospatial analysis services. These not only represent significant future opportunities for the geospatial community, but they also demonstrate how the geospatial community can both benefit from and enable the development of key emerging technologies.