Geospatial data is considered to be the Big Data but it lacks velocity and variety. What is your view about it?
Like many catch phrases, the concept of “Big Data” comes with multiple definitions. From the GIS viewpoint, Big Data describes datasets that are so large — both in volume and complexity — that they require advanced tools and skills for management, processing and analysis. Such huge datasets can be a lot of work, but the extra effort pays off substantially. Geospatial Big Data provides detail and contextual information that provides immediate and long-term value across multiple disciplines and applications.
Also, the variety of geospatial data has expanded rapidly in the past decade. It is no longer limited to simple point- and feature-based locations and attributes. In addition to Imagery and LiDAR point clouds, geospatial data can now include multi-spectral aerial data as well as individual points and features. The tools for collecting data include satellites; crewed and unmanned aircraft systems (UAS); mobile and stationary cameras and scanners; and a broad range of handheld and survey-grade GNSS and optical positioning devices.
How important it is for geospatial data to become real-time or near-real time so as to be considered as Big Data?
Modern businesses operate on tight schedules and need the ability to make decisions quickly. Decisions rely on accurate data; delays or long turnaround times can affect activities and increase expenses. Schedule and cost pressures often combine to reduce the time available to gather and deliver information.
Additionally, the information to support fast-moving projects and processes often needs frequent updates. Decision makers prefer to use “fresh data” that reflects current conditions. They rely on their organization’s ability to leverage 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.
What is the relevance of including data from other sources like transactional data, social media chatter and volunteered information into the geospatial data?
In the span of just a few years, reliance upon geospatial data has become a routine part of people’s lives. For instance, before going out to a restaurant, a person might use Google Street View to plan the route, find parking or look for other restaurants in the area. As geospatial data becomes more prevalent in their personal lives, users are recognizing its potential in commercial settings as well. An early example of volunteered information, commonly referred to as crowd sourcing, 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 (NGOs) 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.
What processes are being developed to handle real-time or near-real time data?
Most of the real-time data is collected from a source via wireless communications connected to the Cloud. Many of Trimble’s geospatial customers saw their first experience with Cloud solutions through Trimble VRS technology. Using VRS, 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 the VRS technology helped fuel a dramatic increase in the use of real-time GNSS positioning. Today, cloud-based positioning services such as Trimble CenterPoint RTX and Trimble VRS Now 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.
What are the automated, semi-automated, manual processes of curating unstructured data?
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 modelling 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.
A new option, Trimble Data Marketplace, enables users to 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 modelling till now has been based on static data, hence dated. What are the methods of making these models dynamic?
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.
Can you point out some examples of such models in different areas of applications?
Consider a mining company in Australia. The company needs to conduct frequent measurements at its mine sites. It uses a Trimble UX5 HP UAS to capture mass data in the form of aerial imagery. The imagery can be collected at regular intervals and with lower cost than crewed aircraft. As an added benefit, the UAS can operate without disrupting normal mine operations or sending ground surveyors into hazardous areas.
The data from the UAS can be used locally or sent hundreds of kilometers to company offices in Perth. There, the images are processed using Inpho UAS Master or Trimble Business Center software to produce 3D models for use in volume computations. Models can be compared over time to provide information on mine construction and material movement. The information is used by mine engineers, production managers and financial and back-office teams. This approach enables the company to operate multiple mines using shared resources to increase productivity and reduce costs. Similar examples abound. Businesses involved in managing high-value sites — transportation, oil and gas, power plants and agriculture to name a few — can leverage mass data collection to improve the speed and quality with which they acquire and use geospatial information for 3D, 4D and 5D analyses.
How can the outcome of such dynamic models integrate with the Internet of Things?
Geospatial modeling of data-based technologies are definitely trending towards the Internet of Things. There are increasing numbers of connected sensors. Some enterprise-level examples are smart meters in the utilities market, and tilt sensors in rail monitoring. Then there are the consumer examples of apps for smartphones and wearable technology.
Sensors produce incredible geospatial data streams. That data can then be analyzed in order to determine patterns and enable intelligent decision-making. 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 a critical component in autonomous vehicles, will expand into other applications in transportation. 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.