People around the world touch intelligence powered by geospatial data every day. From enterprise-scale operations to consumer applications on a smartphone, we (businesses and consumers) are increasingly reliant on geospatial data — and we want it in real-time. Today’s demand for data requires gathering information from every possible endpoint, analyzing the data and turning it into actionable intelligence. Geospatial intelligence is now a requirement for the internet of things because it exposes critical spatiotemporal information that businesses and end-users need to make day-to-day decisions.
While there is proven value in geospatial intelligence, identifying the value of big data can be a complex practice. With the emergence of IoT and big data, the industries that operated in silos are now being connected. Big Data is so large in volume and complexity that it often requires advanced tools and skills to manage, process and analyze. Additionally, while we often have visibility into geospatial data virtualization, we don’t always see the sheer number of touchpoints on that data that were required to make it deployable.
A world of sensors
The number of connected sensors is rapidly increasing. There are billions of connected devices around the world. For example, the utilities market uses smart meters and rail monitoring systems use tilt sensors. On the consumer side, smartphone and wearable technology markets have become major economy drivers. Each of these devices, industrial and consumer, are IoT devices, and all of those connected sensors are producing data that is leveraged for intelligent decision-making.
In terms of geospatial big data, positioning systems such as GNSS and inertial measurement units will be part of a broader hierarchy network of sensors, control devices and user interaction. Positioning and visualization technologies, already in use for autonomous vehicles, will soon be expanded into other transportation applications. Freight haulers, for example, can use wireless, internet-connected sensors for position, temperature and other data to track the location and status of perishable cargo.
The intersection of social, business, marketing and volunteered data
As IoT adoption grows, so does our use of geospatial data. For example, if you are leaving your house to pick up a friend from the airport and you don’t want to be late, you can look at an app on your phone to determine the route with the least amount of traffic delays, and get an estimated arrival time down to the minute. As geospatial data becomes more prevalent our personal lives, we are seeing it become more widespread in commercial and industrial settings as well. An early example of volunteered information, commonly referred to as crowdsourcing, was the use of a mobile app during the 2010 Gulf Coast oil spill. It was used by residents and the public to capture and chronicle what they were seeing happening to the land, sea and wildlife in their areas. Using geospatial technology, citizens were taking part in a mass data collection process that was used by scientists, government agencies and non-governmental agencies to assist in the clean-up efforts.
Business decision processes based on spatial information often extend beyond traditional geospatial professionals. Disciplines such as operations, finance, asset/facility management and construction use information on the location of assets and materials for day-to-day management and planning. For example, defining a corridor for an electricity transmission line brings together factors in engineering, environment, finance and land use regulations. Working from the same data set, each discipline can extract the information that it needs. Comprehensive geospatial information enables the project team to examine how changes in one factor can affect the others. While they understand the value of the geospatial information they rely on, non-geospatial professionals may not know — or care — how the information comes to their desktops.
How is geospatial big data collected?
The tools for collecting data include satellites; crewed and unmanned aircraft systems; mobile and stationary cameras and scanners; and a broad range of handheld and survey-grade GNSS and optical positioning devices. Decision makers want real-time information that reflects current conditions and they rely on technologies such as wireless communications, sensors and application-specific software to obtain field information that allows them to make more informed decisions quickly. Streamlined processes for data collection and analysis are essential to providing timely, accurate information.
Most of the real-time data is collected from a source via wireless communications connected to the cloud. Networks of GNSS reference stations stream data to a powerful server where the information could be merged and analyzed. Then customized data streams could be sent to individual GNSS rovers for use in RTK positioning. Freed from the need for a reference station, surveyors could work quickly and freely over large geographic areas.
The speed, ease and flexibility of this technology helped fuel a dramatic increase in the use of real-time GNSS positioning. Today, cloud-based positioning services support applications in surveying and engineering, construction, agriculture and more. For example, structural or geotechnical monitoring solutions utilize cloud positioning and web interfaces to deliver critical real-time information to stakeholders in remote locations.
Data visualization: Organizing geospatial big data for maximum intelligence
Attempting to utilize the enormous volume and diversity of geospatial big data is like drinking from a fire hose. To handle the flood of data-specialized solutions such as automated 3D modeling and feature recognition software further increase the value of big data by extracting specialized information from large images and point clouds.
Many organizations that can benefit from aerial and satellite data do not have the capabilities to gather it themselves. As a result, they often turn to service providers for airborne photography and image processing. Imagery from satellite systems such as Landsat is available at no cost, but may lack the resolution required for many GIS applications.
Today, through a data marketplace, users can view and select from an assortment of geospatial data available for a given location. The marketplace curates data from a variety of public and private sources, including government charts and terrain models, landsat imagery, and high-resolution commercial satellite photos. Frequent updates to imagery enable users to conduct time-based analyses on natural or built features.
Geospatial data has moved far beyond the days of two-dimensional drawings and maps. Information can be produced and visualized to facilitate in-depth analysis and evaluation. Three-dimensional positions and attributes can be developed according to requirements for precision and detail. By combining multiple datasets, it’s possible to develop 4D models that enable users to view conditions over time. This approach provides the ability to detect and measure changes and provides important benefits to applications such as construction, earthworks, agriculture and land administration.
A fifth dimension, cost, also can be included with spatial information. The resulting model enables users to improve efficiency and cost effectiveness for asset deployment. A construction manager can use visualization tools to create a virtual site and examine options for moving equipment and materials during a project. Similarly, landfill operators can use 5D techniques to manage daily operations and ensure optimal long-term utilization of permitted airspace volumes.
Geospatial data and IoT
IoT and geospatial intelligence are increasingly connected as we rely more on geospatial data for our consumer and commercial needs. Geospatial big data is massive and complex, but with dedicated analysis it is extremely valuable to its user. As we see more sensors and connected technology deployed in our environment accompanied with the growth of critical geospatial data collection and the virtualization of that data, we will continue to see new IoT innovation and possibilities.
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