The Big Geospatial Data Management Lifecycle as it pertains to a typical Sanborn customer – Part 1.
Over the past few decades, as geospatial data has enabled increased efficiencies in management and operations for both government and private enterprise, there is greater understanding of the value in geospatial data. However, as geospatial data is put to more widespread use, many entities struggle with the acquisition, organization, and storage of Big Geospatial Data.
This blog series will provide an overview for each of the five primary components that make up the Big Geospatial Data Management Lifecycle.
1. Enterprise System Assessment
A successful enterprise geospatial system is based on well-defined user needs, functional requirements, and application specifications. For clients to get the most success out of Big Geospatial Data, they must first understand their current positions and decide on an approach that models the needs and purposes of their business operations. The Enterprise System Assessment is a critical stage in the lifecycle as it provides input for the entire cycle, which includes: data acquisition, data analysis, data distribution, and data maintenance.
As an example, orthophotography /orthoimagery are a key component to most customers’ base mapping needs. A thorough needs assessment prior to purchasing data will assist in determining multiple variables, such as: resolution, accuracy, refresh requirements, and storage requirements. Ensuring these variables are assessed prior to investment is an important aspect of the enterprise system assessment stage.
Existing or legacy client information can also be discovered at the Enterprise System Assessment stage and the information gathered may affect requirements for the Data Acquisition phase of the cycle. For example, conditions such as government-restricted access to a project area may play a role in the data acquisition decision making process. Sanborn recently completed a project where the client would allow only a single flight over the area of interest (AOI) – but the project required a complete aerial LiDAR survey, a complete oblique aerial imagery acquisition, and a complete multi-spectral orthoimagery acquistion. Additionally, the client had existing GPS survey points and planimetric mapping data to employ in support of the Data Acquisition stage. The project requirements, existing client data and client restrictions identified through the Enterprise System Assessment led to an acquisition plan specific to this project.
2. Data Acquisition
Data Acquisition can take many forms, including: satellite imagery, aerial photos, digital airborne systems, unmanned aerial systems (UAS), field investigations, and the import of existing databases and existing maps. Processing techniques are used to further refine acquired data into usable geospatial products such as orthomosaics and value-added data such as planimetric feature databases. The acquisition phase of the Big Data Management Lifecycle includes the development of innovative methods for integrating many different data types. Through this model, a project may leverage multiple geospatial information sources with the goal of increasing accuracy and quality.
Sanborn’s Data Acquisition phase of the Big Data Management Lifecycle includes development of mapping products. Toward completion of this stage, final base maps are generated and include, but are not limited to, planimetric maps, LiDAR-generated topographical/contour maps, geo-referenced oblique aerial imagery, land use maps, land cover maps, and 3D cities data.
3. Data Analysis
Sanborn designs and implements custom software solutions for geospatial Data Analysis, providing direct value for the client. Typically performed through a combination of commercial off-the-shelf (COTS) and custom-developed software applications, all Sanborn software is developed to work seamlessly with industry-standard mapping platforms. For example, Sanborn has developed and implemented an Ecosystem Decision Support System (EcoDSS) designed to incorporate land cover map databases and Aerial LiDAR-generated topo data with client-provided land use mapping plans. The EcoDSS identifies areas of concern and potential impacts for prescribed forestry activities and then delivers information for preserving and enhancing bio-diversity while maintaining sustainable forest resources.
EcoDSS satisfies a range of analysis requirements, including: strategic planning, tactical planning, database development and maintenance, forest economics, timberlands investment, timber sales, and operational activity tracking.
4. Data Distribution (Cloud)
With Data Acquisition and Data Analysis complete, deployment of the data is the next step…
As more customers require a single authoritative source for information for staff utilization, clients are less interested in “one-off” databases that will require significant investment for later integration into or migration back to a single database. Cloud Infrastructures are making it much easier to manage the daily workload without having to implement multiple workflow exceptions for individual staff. In addition, Cloud-Based Data Distribution may allow for significant savings for clients through a leasing pricing model versus outright hardware purchase. New cloud-based pricing models also support an OPEX budgeting cycle versus the dreaded CAPEX budgeting cycle.
Cloud Implementation for Big Geospatial Data provides multiple benefits to the customer, including: security, redundancy, scalability, and lower cost. In addition, building geospatial business solutions on Cloud Platforms allows Sanborn to eliminate potential concerns about future scalability and lack of infrastructure for client data.
5. Data Management (Example: Change Detection)
Data Management / Maintenance are critical aspects for ensuring success with Big Geospatial Data Management. The geospatial landscape changes constantly, as a result of human activity and natural forces, impacting ecosystem management, community planning / development, and property revenue assessments. These changes can be monitored through multiple methodologies, including client-provided databases (e.g. zoning permits) – or they can be more efficiently identified through remote sensing technologies. For example, when a mining company needs to determine the volume of aggregate that’s been removed from a location, Sanborn can apply advanced proprietary analysis and processing techniques to aerial imagery in order to locate areas of volumetric change quickly and efficiently. When a data management practice such as this has been developed and implemented, Data Acquisition, Data Analysis, and Data Distribution are completed to create the Big Geospatial Data Management Lifecycle.
Posted at GISCafe – October 5, 2015. Check in over the next several posts to read entries that will go into greater detail regarding each of the primary components associated with the Big Geospatial Data Management Lifecycle.
Vice President, Business Development and Sales