Climup Tech, a company specializing in providing immediate property risk data related to climate-amplified hazards such as wildfires, floods, and extreme winds, emerged out of the necessity to help insurers address California's new wildfire regulation - Mitigation In Rating Plans and Wildfire Risk Model (REG-2020-00015).
With the regulation effective from October 14th, 2022, and filings due by April 12th, 2023, Climup Tech sought an innovative solution to identify the proximity of flammable materials near insured structures, ensuring compliance with these guidelines and regulations.
Climup Tech partnered with Amazon Web Services (AWS) and Cloud303, a leading AI/ML solution provider, to develop a cutting-edge AI/ML-based system deployed on the cloud to achieve this goal. By utilizing AWS AI services such as AWS SageMaker, AWS Textract, and AWS Rekognition, Climup Tech successfully implemented a robust solution to address proximity concerns and enhance its service offerings. This collaboration enabled Climup Tech to help insurers comply with California's new regulations and empowered them to better assess and mitigate property risks in the face of increasing climate challenges.
As California experiences an increasing number of wildfires, insurance companies have tightened their property coverage requirements. In response to this situation, the State of California Department of Insurance issued new guidelines, REG-2020-00015, aimed at promoting sustainable development, reducing greenhouse gas emissions, and requiring accurate assessments of flammable materials near insured structures.
Climup Tech saw a need for a robust and automated AI/ML solution to identify fire hazards, meet the new insurance requirements, and comply with the broader goals of this regulation. This includes adhering to energy efficiency standards, supporting renewable energy promotion, and demonstrating a commitment to waste management and public awareness initiatives.
Cloud303's engagements follow a streamlined five-phase lifecycle: Requirements, Design, Implementation, Testing, and Maintenance. Initially, a comprehensive assessment is conducted through a Well-Architected Review to identify client needs. This is followed by a scoping call to fine-tune the architectural design, upon which a Statement of Work (SoW) is agreed and signed.
The implementation phase kicks in next, closely adhering to the approved designs. Rigorous testing ensures that all components meet the client's specifications and industry standards. Finally, clients have the option to either manage the deployed solutions themselves or to enroll in Cloud303's Managed Services for ongoing maintenance, an option many choose due to their high satisfaction with the services provided.
Identifying the Challenge
Cloud303 collaborated with Climup Tech to develop a comprehensive AI/ML solution, leveraging several AWS services to create models and classifiers, manage data and files, and provide functional APIs for client interaction. The solution encompassed state-of-the-art computer vision and analysis techniques and made extensive use of AWS AI/ML services for an engaging and elaborate experience.
Assembling the Tech Stack
Cloud303 deployed Amazon SageMaker, a fully managed service that enables developers to build, train, and deploy machine learning models quickly and efficiently, for various aspects of the project.
Unveiling the Models
SageMaker was used to develop various AI/ML models and classifiers, including InExClass (for image classification), image segmentation, depth estimation (using AdaBins), and FireHazardAdjacency (for detecting flammable items within five feet of a structure). AWS Rekognition was employed for image segmentation, while AWS Textract was used for text parsing from vendor-provided data. Climup Tech also implemented semantic segmentation using recipes from AWS, and created house bounding roof-lines from images for better hazard assessment.
Managing High-Quality Datasets
SageMaker was also employed to support data labeling, driving towards an auto-labeling capability for the initial product training. This helped create a high-quality, labeled dataset to train the AI/ML models.
The Role of InExClass
InExClass, an AI/ML classifier, was built on AWS Rekognition and Python to determine whether an image is an exterior image or some other type (e.g., interior, floor plan, general outdoor). This classifier played a critical role in filtering relevant images for further analysis.
Adding Depth with AdaBins
To estimate 3D depth from 2D images, Cloud303 utilized Amazon SageMaker to deploy a pre-trained AI/ML model, AdaBins for this specific task. The AdaBins algorithm inputs a single 2D image and predicts a depth map. This was augmented with clever programming for localized depth comparisons, as AdaBins provides an imperfect depth map.
By integrating this model into the system, Climup Tech was able to gain better insights into the spatial relationships between the insured structure and nearby flammable objects. This added depth information significantly improved the system's ability to assess the proximity of flammable materials and determine the potential fire risk.
Cloud303 developed server-side code using serverless AWS Lambda to cost-efficiently support the on-demand nature of the workloads. AWS Fargate is then applied to both queue and spin up elastic compute resources to process the thousands of properties per API call in a timely manner. Each Fargate task uploads aerial and street view images from Google or Bing in addition to other real estate photos from separate image providers, invokes SageMaker endpoints for image segmentation, depth analysis, and performs fire hazard adjacency analyses on the results.
Text Parsing and Data Extraction
Climup utilized AWS Textract, a machine learning service that automatically extracts text and data from scanned documents, to create or use a text parser to gather detailed information about a property as provided by the vendors. This parser extracted the relevant data from scanned documents, which was then saved to the database for further analysis and cross-referencing with the image analysis results.
Data Storage and Retrieval
Amazon S3 and Amazon RDS were utilized to create an organized file and database structure to maintain original and processed images, metadata, interim results, and other relevant information.
Enabling User Interaction
The integrated system enabled users to input property addresses, gather images from vendor APIs, and automatically apply the AI/ML models for fire hazard proximity assessment.
Our project with Climup Tech was a challenging yet fulfilling venture that required an intricate mesh of AI, machine learning, and cloud services. Using Amazon SageMaker, AWS Rekognition, and AWS Textract, we were able to develop an efficient, automated, and scalable solution for assessing fire hazards.
Meeting New Insurance Requirements
The AI/ML solution, jointly developed by Climup Tech and Cloud303, successfully enabled Climup Tech to assess fire hazards to enable insurance companies to comply with the new insurance requirements. The system's high accuracy, automation, and scalability made it a valuable tool for insurance companies and their clients.
Leveraging AWS for a Cutting-Edge Solution
By leveraging AWS services like Amazon SageMaker, AWS Rekognition, and AWS Textract, Cloud303 was able to showcase their AI/ML expertise and build a robust, efficient, and scalable solution with Climup Tech. This collaboration enabled Climup Tech to help CA insurance companies to meet the stringent insurance requirements; it has positioned Climup Tech as pioneers in adopting cutting-edge AI/ML technologies for property risk assessment.
Attracting Insurance Companies
Furthermore, the system has enabled Climup Tech to attract insurance companies as potential clients. The solution's comprehensive features and seamless integration with existing workflows have made it attractive for insurance providers looking to automate and streamline their fire hazard assessment processes.
The partnership between Climup Tech and Cloud303 has successfully deployed an innovative AI/ML solution using AWS services to address the challenge of assessing the proximity of flammable materials to insured structures. The solution has not only helped Climup Tech clients comply with new insurance guidelines but has also enhanced Climup Tech’s reputation as a technology-driven company in the property management and insurance space.
Equipping for the Future
With Cloud303's expertise in AI/ML and the power of AWS services, Climup Tech is now better equipped to tackle the challenges of a dynamic insurance landscape while offering improved services to their clients.
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