Modernizing GEOINT Management with Automated Conflation to Save You Time
It takes tedious time to compare new and old data and confirm the accuracy before integration. Automating geospatial data integration with L3Harris’ MapMerger™ conflation tool can help intelligence analysts get back their most valuable asset: time to get critical information to warfighters more quickly.
How does conflation work?
To explain this, let’s start with metadata. Metadata is simply a set of data that describes and gives information about the data it supports. This metadata gives users insight on things like building locations, roads, lakes, and more using geometry such as points, lines, and polygons. The process of conflation fuses data from numerous sources, like smallsats and vehicle-mounted sensors, to create the best images–or enriched data sets–while still protecting existing confirmed data. Newer data sources can also be included, such as social media information, cell phone and tablet sensors, three-dimensional LiDAR point clouds and drone sensor collection. Each of these produce a torrent of data every single day, making it extremely time consuming for intelligence analysts to physically check each potential data replacement choice. Tools like MapMerger automate conflation of these datasets to ensure accurate replacements with updated information.
Beyond the issue of timeliness
Merging datasets is where the process becomes challenging because each structure within the merged data needs to align properly with its adjacent set. With thousands of structures to match from each set, it’s tedious and time consuming to do manually, and it can impede getting information into warfighters’ hands when they need it.
In the field, decision makers and warfighters must know exact positions of provinces or villages. Sometimes these boundaries are drawn by hand on maps, taken back to an office to be uploaded to the database, and then fit to the road databases. This inefficient method can be easily fixed with MapMerger as it quickly aligns hundreds of boundary vectors to fit road databases that have been verified.
Sometimes users may have two accurate sets of data, but one could be missing several important attributes. Enriching metadata involves combining the two sets to be the best version of the two, and it is a complex process unless you have the right tool. L3Harris’ MapMerger solves this challenge by finding missing attributes and automatically extracting important features. It fixes over 95 percent of data merging problems and is over 50 percent faster than competitor conflation tools. MapMerger also remembers each “match strategy” so it can be applied again and again. When time is of the essence, warfighters and analysts can depend on MapMerger to solve the data curation challenge.
Visit L3Harris.com/MapMerger to learn more.