Urban Air Land Mobility Project with Labelbox

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Presented by Labelbox

Labelbox is an end-to-end training data platform that is used to create and manage high-quality training data. The platform provides fast labeling tools, collaboration features, and supports any data type (e.g., images, videos, text, etc.)

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Stanford masters students as part of CS230: Deep Learning describe the process and workflows for their project in which they focus on identifying suitable areas to land urban air vehicles through satellite imagery. In the recap, Andrew and Seraj share their experience completing image segmentation tasks via Labelbox’s software and labeling service, as well as lessons learned and best practices for other computer vision researchers.

Randall Lin

With experience in large scale NLP on TPUs using limited labeled data and quick iterative ML experimentation tooling with Airflow and Kubernetes, Randall is currently

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