Quality Control for GIS Maps

Geographical Information Systems increasingly leverage artificial intelligence (AI) and machine learning (ML), using location data as the connective thread to automate processes, improve predictive modeling, and develop business competencies. Location analytics for maps can help create a competitive edge by uncovering hidden patterns and delivering crucial insights.

Deep learning enables digital maps to accurately detect objects and classify pixels at scale. Aerial, satellite, drone imagery, street-view images and videos datasets are ideal candidates for deep-learning algorithms.

The Quality of GIS data is paramount to optimizing sophisticated, complex GIS applications. We have extensive experience designing and implementing effective data quality control programs that establish appropriate parameters and ensure that internal processes are optimized for the intended purpose and use of the GIS mapping data.

Our Approach

Design effective overall data quality control programs for GIS Map content.
Perform Change Analysis (aka Churn Analysis) by comparing previous and current imagery
Optimize ingest pipelines and publish along with data visualizations, including establishing protocols for working with 3rd party data collection vendors
Automate access integration with other systems or databases, or import/export of files with external databases and applications.
Develop detailed dashboards for program reporting and metrics

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