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Sylvera brings transparency to the carbon-offset market with AI
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 应用基础设施与中间件 - 数据可视化
适用功能
- 商业运营
- 质量保证
服务
- 数据科学服务
- 系统集成
挑战
Taking on the gargantuan task of assessing carbon sinks, Sylvera needed to accurately verify the performance of the projects they rate. This required precise tracking of land use and its evolution over time, particularly focusing on mangroves, which are crucial for absorbing more carbon than regular tropical forests. Sylvera knew that bringing in students and interns was one option to get the job done, but the company also knew how much additional interviewing, hiring, onboarding, training, and management that approach would take.
关于客户
Before launching Sylvera in 2020, COO and Cofounder Samuel Gill worked at a U.S. law firm and saw how complicated carbon-offsetting projects could be, and how difficult it can be to get up-to-date data on the climate impacts they promised. To address this issue, Gill partnered with CEO and Cofounder Dr. Allister Furey, a machine learning expert, to establish Sylvera. The startup ranks carbon projects, bringing a new layer of transparency to this market by assigning ratings to projects. Companies wanting to purchase offsets to lower their carbon footprints can access rankings and monitoring services via Sylvera’s web app to choose the right project for them and track projects’ performance over time.
解决方案
As Bonnefond and her team started searching for solutions, they discovered that many annotation platforms were built for autonomous vehicles. They needed a platform that could overlay Google Maps and shift between different satellite images, including high-resolution imagery from Google Earth. CloudFactory annotators, specifically trained to handle these types of geospatial tasks, were chosen for the job. CloudFactory was also able to integrate Azavea’s GroundWork tool to meet Sylvera’s labeling requirements. The CloudFactory team uses images provided by Sylvera to form annotation data that explains exactly where mangroves sit. The goal is to ultimately train a deep learning model to help them track losses and gains in mangroves over time.
运营影响
数量效益
