Pool your information from anything and everything — Inform decision making — Let the most appropriate person make the final decision… namely, the user
This is the second of three posts on using space technology to quantify data. Guest written by Andrew Cutts
Last time I looked at how data from space, particularly Earth Observation data, is growing at a staggering rate. The challenge is now on to harness this data further, to be more than just a basemap. This time the focus falls on sensors as a whole, both in Space and also on the ground. Finally, I will look at the internet of things (IOT) and how this is changing the way we interact and analyse data.
Every man is a sensor
It is perhaps a surprising place to start, but think about it; we are all sensors capable of processing vast amounts of data at incredibly high speeds; we filter it in the blink of an eye. We make decisions, often subconsciously, about which information to act on and which to ignore. It could be argued that the armed forces are better at understanding this than anyone else.
The intensity of military operations means that no piece of information is ever discounted, but instead carefully evaluated. Anything that receives information, in whatever format, is a sensor, from the human eye to the most sophisticated technology, whether it be radios, movement detectors or imaging platforms such as radar and cameras. It requires years of experience to become this adept at harnessing information from such a range of sensors.
The aim ultimately is to synchronise a matrix of sensors, collecting data at different levels to create a three-dimensional information environment; building a complete picture. This can only be achieved by maximising the full potential of every sensor, understanding its capabilities, limitations and constraints.
Today there is an ever-increasing array of sensors or ‘things’ that can connect to the internet and can send and receive data (more commonly known as the internet of things). Care should be taken with harvesting data from these sources. Just because it is possible to do so does not necessarily mean it is reliable or valuable.
Intelligence from sensors
With a series of readymade skeleton collection plans suitable for every emergency, MIS continually monitors events worldwide for the insurance industry. Once an event occurs the suitable collection plan template is available in seconds and key questions are entered into it. The collection plan itself sets order, reducing missed inputs by pausing and asking the right questions at the start. Ultimately, we must remember that intelligence is just processed information, so if you don’t ask the right questions, no amount of data will be able to answer them. Consider the Fort McMurray wildfires. Some of the key questions might include:
· Where is the fire?
· What is the extent of the fire?
· Where is it currently burning?
· What are the emergency response plans?
· Where are the key responders?
· What is burning?
While Earth Observation data can undoubtedly answer some of the above questions, no commercial satellite is currently able to supply real-time imagery. Rather than wait for the next available image, the collection plan can guide the next actions (which may well include ordering sub 1m imagery). This again is the ‘every man is a sensor’ approach. News feeds, local authority reports, web cameras and CCTV cameras may all be available. Social media can inform and sometimes mislead but with filtering and pooling data sources inconsistent information can be flagged as the picture becomes clearer.
In its most basic sense the collection plan follows a similar pattern towards obtaining intelligence.
Multiple users mean a constant flow of information is needed
Within 24 hours of an event, though that does depend on what event is taking place, the first aim is to produce an exposure to risk for the insurers. This could be in the form of a heatmap or a series of identified areas of risks. Often, 24-hour reports contain little or no EO input; an example would be the limitations of cloud cover during a hurricane which severely limits what can be seen on the ground.
After 72 hours of an event, Earth Observation data and its use in combination with all these other data sources begins to come into its own. High resolution imagery can be used to classify levels of the extent of damage to property, ranging from no damage, light damage, heavy damage through to completely destroyed. This may not be the whole picture though. Consider an undamaged power station which is being fed by a flooded water treatment facility. There is a chance of this becoming a claim based not on physical damage but on the loss of operational activity of the plant.
The Internet of Things
Gartner estimated that “6.4 billion connected things will be in use worldwide in 2016” and then the following year revised that number up stating that “8.4 Billion Connected “Things” Will Be in Use in 2017”. They predict that over 20 billion sensors will be in place by 2020. Not all the connected things will be directly complementary to Earth Observation (EO) data (smart TV perhaps), but there will be gauges, cameras and sensors that will all be useful in combination with EO data.
We have seen that MIS takes advantage of in situ sensors, using them to provide support immediately as part of the collection plan. It is this initial layer of information that helps piece together the impact of these events. EO data helps reaffirm this information, helping to provide a more detailed picture.
IOT + EO = A perfect storm?
A recent EY report highlighted the 4 most impactful areas for IOT.
1. Wearable or personal technology (fit tech)
2. Sensors on objects (boats, cars)
3. Location based sensors (on cameras on buildings)
4. GIS (hydrological data, utility grids)
With the explosion of the number of sensors, even more sources of information are opened up to be harnessed. Smart thermostats may offer information about occupancy, river gauges used to remotely predict water surges and utility grids might suddenly spike or even appear offline. All these information sources are part of a bigger data jigsaw.
Earth Observation data can be used to complement this information. Why is it that that an electric power plant is offline? Is there damage elsewhere on a wider scale that is causing an impact that might not be directly obvious? These small-scale mapping requirements may be better suited to low resolution imagery. Whereas for large scale mapping down to the level of individual houses sub metre pixels can provide a more detailed answer. During the wildfires that recently happened in California, MIS helped insurers settle claims remotely, without the need for a person to be on the ground. In less developed parts of the world, immediate access is restricted to first responders, givers of aid and critical emergency workers. By harnessing every sensor available, by utilising the best available very high spatial resolution satellite data, the insurance industry can respond faster and help those impacted.
Pool your information from anything and everything — Inform decision making — Let the most appropriate person make the decision… namely, the user
MIS is helping insurers make fast decisions today. By utilising a collection plan, it enables them to strive for answers to key questions faster. Combining the ‘every man is a sensor’ approach with the growing trend of IOT enables layers of information to be built on. One of these layers is Earth Observation data and in the final post we will look at how MIS dealt with the 2017 Hurricane season that devastated large parts of America.
To find out more about how MIS can help you, get in contact firstname.lastname@example.org