Satellite Imaging & Edge Compute: A Study on Wildfire Financial Risk
Financial Crisis to AI-Powered Solutions
In California, a renowned insurance company is facing financial instability due to wildfires.
Wildfires are becoming increasingly frequent and widespread, as evidenced by recent data. As summarized in this report by the World Economic Forum, "As climate change causes more natural disasters, the insurance industry is raising rates, reducing coverage and exiting some markets entirely".
The need of the hour is to equip these tough and very real scenarios with tech solutions. Recent advancements, such as AI, ML, and their fusion with satellite imagery and geospatial technology can empower service providers who offer analytical services on assessing wildfire risks, thus serving insurers and communities well. Used judiciously by the data scientists and analytics teams, this can really change the game for insurers!
And this is not all! More recent innovations can further power these tech solutions.
For example, imagine a deep-learning model trained to detect areas at risk from wildfires utilizing satellite imagery, running on every satellite looking at Earth, continually - with assessments delivered directly to the insurer's decision-making tools. Sounds futuristic? Not at all!
This article describes the transformative capabilities of edge-computing paired with satellite imaging and its potential.
On-board processing - Supercharging Wildfire Financial Risk Solutions
The model, trained to assess the property-centric wildfire risk, is on-boarded. The satellite on which this model is on-boarded will possess all relevant tools:
• Platform to accommodate the model
• Capacity for earth imaging
• Provisions for Geo-processing and generating input for the model
• Ability to run the model and extract the final output
• Transmitting the property-centric assessment.
The satellite will directly transmit the property-centric assessment to the insurance company's decision-making tools. Based on this assessment, the premium will be determined according to the current scenario. Similarly, for claims, damage can be assessed using the same method.
The up-to-date details and the holistic process make this a hassle-free, win-win situation for the company and customers.
What is actually happening? - Here, one does not order satellite imagery; rather the insight.
Is this just bells and whistles added to traditional remote sensing? - We have come a long way since the advent of satellite imaging as it began in the 1960s - from manual visual interpretation from printed 1:1 image, to convert insights to decision-making in an automated workflow. While the key technical processes remain largely the same, the speed at which we can arrive at meaning from pixels has increased exponentially. Executing this at the edge, on satellites eliminates the latency and with AI/ML, this process can only get better - better than the human eye!
Feasibility: Coming to the technicalities, it is possible today to connect a real-time feed of satellite images to your model and configure it to run in an automated way, with the key to governing its behavior held by you - the model's creator!
Better Perspectives: Financial resilience: The on-board edge computing is designed to smart-task processes like radiometric correction, image quality enhancement, and geometric correction on the raw satellite image and produce the cloud-free AoI image, then feed this into the model on-boarded to deliver targeted data insights.
Focus on the need - Delivering Focus of Interest: The smart tasking mentioned above helps focus on the area of interest, which could be a minimal portion of the larger image, for example, 10% of the pixel ratio over the larger tile of the high-resolution image.
Critical inputs for financial risk discussions: Delivering the Data of Interest: To make decisions on financial risk-related issues, the risk management team needs key inputs like the quantum of damage, areas to be focused on, and number of property damages. Edge computing platforms can deliver these details to the decision-making tools from the satellite.
Continuous improvements: Configurable outputs: Once the model is operational on the satellite, the platform ensures flexibility in updating the model, which may be necessary due to factors such as policy changes, climate variations and geographical reasons thereby influencing the outputs. This feature underscores the control of the model creator over the model's governance.
On-time details - Time-saving: Edge computing helps to extract pixel subsets of the area of interest, and numeric derivatives, result in fast data transmission to the decision-making tools. This faster transmission is possible by the memory footprint compression.
Value propositions from On -board model and On-satellite Analytics:
• Significant cost reduction in wildfire monitoring and Property/Area of Interest risk assessment, since the whole process concerns areas of interest.
• An insurance company's decision-making tools are updated with timely details due to a faster transmission of data, supporting quicker decision-making.
• Elements like targeted notifications and fire detection models enable proactive mitigation, reducing the financial burdens of an insurance company and reinforcing better administration.
• Remote updating the on-board model, making this process long-running and adaptable for better long-term assessment and continuous improvement.
Advancing financial stability
In response to the escalating threat of wildfires, insurance companies require comprehensive insights and timely data to ensure efficient financial management. On-board edge computing emerges as a pivotal solution, seamlessly integrating data into wildfire-related financial risk management processes. This innovative product fosters resilience within the insurance sector by providing timely insights for informed decision-making and proactive risk mitigation.
Join the innovation
SkyServe laid the foundation for this product, as detailed in this blog, "SkyServe Partners with SatRevolution for the 2023 STORK Launch."