Geospatial intelligence
Learn how Granica helps geospatial intelligence companies optimize AI data costs.
Digital maps and location services have become a nearly ubiquitous part of modern life with a wide range of applications — from navigation and geomarketing to supply chain management and infrastructure planning. Geospatial intelligence companies are investing in AI and machine learning to improve the accuracy and functionality of their services. Common use cases include:
- Creating immersive 3D maps
- Identifying and labeling map features
- Predicting traffic patterns
- Optimizing routing
The challenge
Geospatial intelligence companies capture, aggregate, and analyze data from a wide range of sensors and sources such as high resolution aerial cameras, LiDAR, RADAR, IoT, and mobile devices. The volume of data is tremendous and can easily grow to tens of petabytes, costing millions of dollars annually even in "low-cost" cloud object stores.
For geospatial intelligence companies, all data is hot data which must be quickly ingested, analyzed, and acted upon. These data costs consume ever more of the AI innovation budget and crowd out investment in compute, tooling, and people.
How Granica helps
Granica increases the efficiency and utility of your AI-related data and your downstream AI pipeline stages, enabling you to free up significant money, resources, and time which you can reinvest to improve AI performance and outcomes.
Granica Crunch for geospatial data
Granica Crunch reduces the storage cost associated with AI data without archival and/or deletion. Its advanced, patented ML-powered data reduction algorithms are specifically optimized for the image, LiDAR, and tabular data prevalent in the geospatial intelligence industry.
How Crunch helps AI/ML Engineers
- Optimizes S3/GCS object storage costs, so you can allocate more resources to data quality and model performance
- Supports a wide range of data types unique to location and mapping systems
- Elastically scales up and down to support dynamic workloads
How Crunch helps AI Product Owners and FP&A
- No upfront capital outlay — Crunch doesn't cost budget, it frees up budget
- Ongoing, predictable savings enable accurate forecasting and planning
Typical data reduction rates
| Type | Example files | DRR |
|---|---|---|
| Images | .png, .tiff | >50% |
| LiDAR | .las, rosbag | 25-50% |
| Tables | .parquet | >50% |
DRR varies based on customer-specific data. Estimate your savings to evaluate the DRR for your specific data.
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