Team Name:
Oz-One
Team Members:
Oz-One
Is an interactive educational game developed for all demographics it highlights the importance of waste plastic identification in a fun and interactive way that empowers the user.
We offer a built in rewards system that lets you earn while you scan .
We drop the good drops o0o and not in the Ocean....
As technology is evolving quickly so is the need for data collection in the plastic waste industry. As a team we discovered those datasets don't exist for identification, so in order to create this dataset we are going to have our community engaged in the research by simply asking them to click a picture on our app and submit it.
The submitted picture will then be processed and sent along with the rules and examples to the AWS sagemaker ground truth, where the data will be labelled.
We will use AWS lambda to create the labelling jobs for the AWS sagemaker labeler and will also use it to generate data tables using the output from sagemaker.
This data tables will then be pushed to our output bucket which will be provided to the Scientists.
Go to Challenge | 11 teams have entered this challenge.
Go to Challenge | 11 teams have entered this challenge.
Go to Challenge | 13 teams have entered this challenge.
Eligibility: Proposes to use CELLULAR IoT network connectivity (as opposed to wired connections or alternative unlicensed wireless networks like Wi-Fi, SigFox etc) and physical IoT devices. Does not require a working prototype, just a plan.
Go to Challenge | 14 teams have entered this challenge.
