Impacting Global Policy by Understanding Litter Data

Explore a conference talk that delves into the global issue of litter and how data-driven approaches can impact policy and environmental change. Learn about Litterati, a platform that has collected over 10 million images of trash from 185 countries, and how they use computer vision and machine learning to analyze and categorize litter. Discover how serverless technology and step functions enable customized recognition capabilities for different scenarios and environments. Gain insights into how this data is being used to inform policymakers, influence packaging decisions, and inspire personal responsibility. Understand the scale of the litter problem, its consequences, and the potential for alternative data sources like street cameras and drone footage to expand our understanding. Join Sean Doherty, CTO of Litterati, as he shares his passion for using technology to address environmental challenges and bring about meaningful change in waste management and pollution reduction.

Introduction
What is Litter
The Scale of Litter
Consequences of Litter
What do we see
Our data
Location specific models
Whats next
Alternative sources
Questions
Types of Litter