Vision Transformers for Satellite Image Time Series with Michail Tarasiou

In this video, Robin catches up with Michail Tarasiou to discuss the new paper, ViTs for SITS: Vision Transformers for Satellite Image Time Series. This paper introduces the temporo-spatial vision transformer (TSViT) architecture. The TSViT incorporates novel design choices that make it suitable for time series tasks such as crop classification. In this work, TSViT crop classification and segmentation models are trained and evaluated on Sentinel 2 datasets and achieve state of the art (SOTA) results on these tasks by a significant margin. This is an exciting step towards high accuracy and low cost & automated crop mapping using remote sensing imagery.
Paper authors: Michail Tarasiou, Erik Chavez, Stefanos Zafeiriou

- https://arxiv.org/abs/2301.04944
- https://github.com/michaeltrs/DeepSat...
- https://www.satellite-image-deep-lear...
-   / michael-tarasiou-b749725b  

To learn more about deep learning applied to satellite & aerial imagery head to https://www.satellite-image-deep-lear...

Logo animation and thumbnail credits: Mikolaj Czerkawski @mikonvergence