Introducing Nearmap for Post-catastrophe Response
Post Catastrophe Imagery and AI-derived property damage and condition data unite to help insurers process customer claims more efficiently.
Post Catastrophe Imagery and AI-derived property damage and condition data unite to help insurers process customer claims more efficiently.
It’s the dawning of the age of 5G. And to get it right, network engineers are going to need more than Fresnel calculations — they’re going to need 3D. Not just any 3D; high-res 3D.
If this is all alpha-numeric gibberish to you, let’s start at the beginning.
5G is the next generation (the fifth, go figure) of cellular communications standards. The current standard, 4G, transmits data at hundreds of megabits each second. But 5G, which is set to debut by 2020 or earlier, is expected to deliver at least one gigabit each second, with the potential to transmit as many as 10 gigabits per second.
According to UC Berkeley professor Jan Rabaey writing in The Conversation, this will be the key to enabling vast networks of AR, VR, automated systems, and IoT connections. Not only does 5G promise far higher data transmission rates with lower latency (delay between when a signal is sent and when it’s received), it also offers “the possibility of providing reliable connections to massive numbers of wireless devices simultaneously.” Systems like autonomous cars, smart agriculture, real-time change detection, and machine-to-machine networks will all be dependent on the speed and efficiency 5G can offer.
In Australia, major players like Telstra are planning to unveil their initial 5G services as early as 2019.
5G networks have special high bandwidth, short range requirements that necessitate a huge number of devices and equipment to function optimally. In order to accomplish network capacity, speed, and latency, 5G networks require:
– higher radio frequencies, which are more sensitive to clutter and interference
– arrays of antennae to reduce interference, especially with multiple devices communicating simultaneously
– frequently-spaced base stations, as close together as every 250 meters (the standard for 4G is every one to five kilometres)
These specific requirements mean that 5G will rely on much denser, more equipment-heavy networks. According to a recent study by Ordnance Survey in the UK, as many as 40,000 5G access points may be required to serve the city of London, with an area of 2.9 square kilometres; whereas a similar number of access points today serve the whole of the UK, with an area of 242,000 square kilometres.
Because of these technical requirements, planning and deploying a robust 5G network is highly complex. The densification of antennae and signal sensitivity requires a much more granular level of detail to manage propagation, cellular coverage, backhaul, and other factors.
In particular, planning for 5G infrastructure requires detailed knowledge of the line of sight (LoS) between each base station, since propagation at the required radio frequencies can be very sensitive to clutter and multi-path interference. In plain speak, that means radio frequency (RF) engineers need detailed data to evaluate potential obstructions (“clutter”) that would block propagation. Awareness of unknown objects and features, and the management of change detection, is a critical challenge to making 5G deployments cost-effective and deliverable to deadline.
A viewshed model using line of sight (LoS) data to visualise which areas are visible from a particular point in space (green is visible, red is not)
Why is high-res important in a 5G context? In order to accurately assess potential obstructions and ideal placement of base stations, 5G network planning requires a high level of detail — down to roof angle, façade material, and accurate height readings of structures. Traditional geodata sets for large-scale networks may not get the job done, as they often lack high-res fidelities.
Because 5G networks rely on precise placement of millimetric wave (mmwave) sites every few hundred metres, accurate assessment of an entire city — including all features and obstacles that could interfere with radio wave propagation — is crucial. Current, accurate visual data is required to view and analyse “street furniture” and other ground features such as walls, railings, utility poles, statues, bus shelters, billboards, temporary structures (mobile offices, trailers), seasonal decorations, and vegetation. In addition, an analysis of building surface materials is vital to determine whether the signal will propagate efficiently, or reflect and scatter.
Being able to view and measure these sorts of ground features is dependent on high-resolution 2D imagery that produces high-res 3D data sets for analysing height data (DSM, or digital surface model) and surface features (textured mesh). Drones are not able to handle the scope, while the highest-resolution satellite imagery (best in class is about 50cm – 10m) does not offer resolutions sufficient to inspect and measure ground detail with confidence.
