04 Apr Whitepaper: Geovisual Data Management for Utilities
The Value of Geovisual Data
Download this whitepaper to answer these questions:
- What is geovisual data?
- What are the challenges of managing geovisual data?
- What is a geovisual data lake?
- What solutions does a geovisual data lake provide?
- How does a geovisual data lake help extract insights from your data?
For many years, enterprises have understood the value that lies locked within geovisual data. Advances in remote sensing technologies have made the collection of this data more cost-efficient. At the same time, enterprises are under increasing pressure to proactively monitor and manage risk, driving the need for more frequent visual assessment. These factors are driving a rapid increase in geovisual data collection.
As the volume of data has increased exponentially, the challenges of managing these data types have been difficult to overcome with traditional data management approaches and platforms. As a result, most of the data that is collected is relegated to siloed network shares or USB hard drives where few enterprise users can even access it.
The Challenges of Extracting Geovisual Intelligence
What makes extracting geovisual intelligence difficult at scale boils down to data management challenges. Geovisual data such as RGB imagery, LiDAR point clouds, geoTIFF orthomosaics and others are large binary files that often need to be maintained as groups of related files or datasets (e.g. a UAV survey consisting of many RGB images). Efficiently moving and organizing these very large files and datasets to a centralized location and making them easily accessible for viewing and analyzing can be prohibitively challenging and cumbersome using traditional approaches.
Transforming Geovisual Data into Geovisual Intelligence
Rapid advancements in AI have created new capabilities in computer vision and visual data analysis that were unthinkable just a few years ago. Deep Learning techniques are well suited for analyzing massive amounts of geovisual data and have rapidly matured into commercial grade analytics.
For these analytics to provide true value to an enterprise, they must be deployed within an analysis pipeline that connects a robust geovisual data management platform with a configurable workflow orchestrator that can execute a sequence of analytics against the right data set and deliver the intelligence extracted from that data directly to enterprise systems downstream.