As a geospatial image analytics provider, this year might just be the worst possible year in history for your customers, especially if they are insurance companies. Or federal agencies responsible for spending hundreds of billions of taxpayer dollars because extreme weather events are on the rise. Or energy companies and utilities needing to meet demand cleanly and reliably.
Extreme weather events, as well as conflicts triggered by climate change and resource security concerns, are only expected to continue to rise. Disruptions to critical infrastructure can have cascading effects through a multitude of supply chains, ultimately affecting your customers who need timely and accurate information to decide their best plan of action.
According to data from the Federal Emergency Management Agency (FEMA), hundreds of billions of dollars were spent since 2017 to recover from extreme events. Such spending has ballooned in response to climate change-linked extreme weather events and other disruptions.
When such events happen and money must be spent, these are opportunities to rebuild with greater sustainability, security, and resilience firmly in mind. That saves more money and resources for the communities that really need it, when they need it. And when later events and needs arise, the receipt of timely and accurate imagery to aid with decisions can speed up both evacuation and response times, leading to a more positive outcome.
TerraNexum is presently seeking investment and partnering opportunities to extend our previous work toward a plug-and-play product offering for government agencies and their contractors. Our patent-pending solution: the Quantum Module (Q-Module).
Our first Q-Modules, Q-Ortho, Q-Join, and Q-Class, will be specifically designed to enhance the speed and accuracy of corresponding image processing steps that require the use of feature selection in a typical workflow. At present, these steps are bottlenecks that delay the receipt of timely, actionable information from satellite or aerial flyovers. Future Q-Modules could be mass-produced and configured for a variety of different tasks.
The quality of critical decisions that rely on timely and accurate imagery and analytics should never take a back seat to processing time and costs.
