This is a guest blog post co-written with Ben Veasey, Jeremy Anderson, Jordan Knight, and June Li from Travelers.
Satellite and aerial images provide insight into a wide range of problems, including precision agriculture, insurance risk assessment, urban development, and disaster response. Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. One way to overcome this challenge is through self-supervised learning (SSL). By training on large amounts of unlabeled image data, self-supervised models learn image representations that can be transferred to downstream tasks, such as image classification or segmentation. This approach produces image representations that generalize well to unseen data and reduces the amount of labeled data required to build performant downstream models.
In this post, we demonstrate how to train self-supervised vision transformers on overhead imagery using Amazon SageMaker. Travelers collaborated with the Amazon Machine Learning Solutions Lab (now

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