Location intelligence: enabling technology
The technology that's enabling location intelligence
This briefing explains how new, accessible sources of highly accurate location data are becoming available. It describes the applications that utilise them, including the Internet of Things, predictive analytics, virtual reality and the latest satellite technology. Watch the video, then see the text and reference material for more detail.
35.5k datasets (mostly free) on data.gov
1,100 earth observation satellites in space by 2020
3D data points at a density of eight per square metre from latest generation LiDAR
Open data reduces public administration across the EU by €1.7bn
New 3D data sources
New and highly accurate 3D data sources are becoming available, not just for large city centres, but also for rural and coastal areas. Most of this data is collected using a technique called LiDAR (Light Detection and Ranging). LiDAR sensors use lasers to measure the distances to objects very accurately. For example, HERE, a company that provides mapping and services to the automotive sector, is using car-mounted LiDAR sensors to create very detailed 3D road maps for use in self-driving cars. The sensors are also being used to create detailed interactive 3D maps of roadside features such as street furniture and building facades.
Another source of highly accurate 3D data is a technique known as ‘structure from motion’ photogrammetry. This technique is particularly used to process data acquired using Unmanned Aerial Vehicle (UAV) or drone technology. The technique uses ‘photo-stitching’ to combine multiple images and create a detailed 3D map.
Because UAVs are able to fly lower than planes, very high resolution datasets are possible using this technique. Many of the providers of UAV surveys offer real-time feeds or frequent revisits. This is significant because it allows for the tracking of changes over time known as 4D (3D plus time) or time-series analysis. The link below shows a 4D visualisation of the construction of the new campus at the University of Applied Sciences and Arts Northwestern Switzerland.
Accessible public data
Increasing volumes of public data are now free and accessible from portals such as data.gov. The number of datasets available continues to grow, stimulating the development of new smartphone applications covering a wide variety of local authority functions.
For example, the Environment Agency (EA) has made its extensive archive of aerial LiDAR data available as open data, with digital elevation models that it uses for flood analysis. These models are available together with the raw point cloud data – records of every laser point collected. This information has been used to identify field boundaries for land registration, and to determine ideal rooftop locations for solar panel placement.
Another novel dataset is aerial thermal imagery. Slough Borough Council developed thermal mapping using airborne sensors to plot heat loss from properties, identifying outbuildings that were being used illegally as dwellings.
More detailed models known as City Models are now also becoming available. These are built by merging LiDAR and high-resolution camera data with aerial imagery. Examples of 3D City Models in the UK include Sheffield, Folkestone and Jersey.
For London, the New London Architecture Model is a physical 3D model created using 3D printing from similar sources. Additional overlays showing information such as view-sheds, a geographical area that is visible from a location, are projected onto the model using coloured light.
One of the most innovative applications of such data is where underground services and surface models are combined to aid the detection of underground pipes and cables. An example of this is in Rotterdam, where the City has ambitious plans to add all underground assets to its existing 3D Model by 2020.
The internet of things
The Internet of Things (IoT) is a term for networks of connected devices that collect and exchange data. For example, buildings with networked climate control sensors, or connected vehicles that collect information about road and traffic conditions. IoT is central Southend Borough Council’s plans to deploy 14,500 connected light control units as part of a street lighting upgrade. The system gives the Council real-time control of the lighting with automatic fault reporting. These sensors can also act as a platform for other smart city applications such as traffic and environmental monitoring and public WiFi networks.
Predictive analytics is a term for software that determines patterns in data and predicts future outcomes and trends. It is used widely by retailers to make recommendations, forecast trends and target advertising.
Peterborough Council is pioneering the use of predictive analytics and IoT in social care. The council is trialling the deployment of a mix of sensors to create IoT networks in the homes of 100 people who require care. The systems can be set up to alert carers, by monitoring movement and temperature to identify anomalies in expected parameters. The aim of the system is to enable proactive social care.
Location technology is also being used to help find missing vulnerable people. Mindme is a Chichester-based service company that provides location tracking devices to people with dementia. If a vulnerable person carrying a Mindme device strays outside of a designated safe area, an alert is sent to their carer who can then easily locate them using the information from the device’s networked GPS unit. Virtually designating a place as ‘home’ like this is known as geo-fencing.
Augumented and virtual reality
Augmented Reality (AR) is a system for overlaying digital elements on top of a view of the real world. Microsoft HoloLens is wearable AR technology currently in development; early demonstrations allow users to manipulate 3D models of furniture to see how they would look in real life. Meanwhile, Google Tango allows users to experience AR via their smartphone screen, with the first Tango-enabled smartphone released in November 2016. The device uses computer vision to determine its position and orientation within an environment. This means applications that require navigation, measurements and mapping are made possible in places where there is no GPS signal, such as inside large buildings.
AR is being used in combination with new 3D data sources, such as the Rotterdam City Model previously described, to find and locate underground utility infrastructure assets, preventing accidental excavation, increasing safety, streamlining operations and avoiding delays.
Virtual Reality (VR) is also being used with location data. VR is similar to AR but instead layers digital elements over the real world, doing so through fully-immersive headsets that track head movements, allowing the user to experience a virtual 3D world. 2016 has seen the release of many new and more sophisticated headsets, with device prices dropping and smartphone integration making the market very accessible. A location-based application of VR is Google Earth VR.
CubeSats are small, lightweight satellites that operate in low Earth orbit. This, combined with their low weight, makes CubeSats much cheaper to launch than traditional satellites, leading to a new breed of start-ups that could disrupt the Earth observation market. These new companies are using low-cost, off-the-shelf, components, small teams and rapid product iterations to develop large constellations of small satellites. Although these satellites are small, they use super-resolution techniques previously developed for microscopy to obtain high resolution imagery. CubeSat start up, Terra Bella (now owned by Google) has achieved spatial resolutions of 90cm (one image pixel equals 90cm on the ground), sufficient to identify street markings for instance.
Because these constellations – known in industry parlance as flocks – contain many satellites, they are able to revisit the same place on earth frequently making for more accurate changes over time. CubeSats make possible applications such as predicting retailer profits by counting the number of cars in store car parks, monitoring construction rates in large cities and predicting crop yields.