Urban Air Land Mobility Project with Labelbox
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.
Session ID: Presentation Type: On-Demand Session (Recorded)
Date / Time: [Content On-Demand] @ On Demand ET (US)
Presented by: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.) Click for more details and additional sessions and content.
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