Optimizing Mobile Vision Transformers for Land Cover Classification

Optimizing Mobile Vision Transformers for Land Cover Classification

Image classification in remote sensing and geographic information system (GIS) data containing various land cover classes is essential for efficient and sustainable land use estimation and other tasks like object detection, localization, and segmentation. Deep learning (DL) techniques have shown tre...

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Bibliographic Details
Main Authors: Papia F. Rozario, Ravi Gadgil, Junsu Lee, Rahul Gomes, Paige Keller, Yiheng Liu, Gabriel Sipos, Grace McDonnell, Westin Impola, Joseph Rudolph
Format: Article
Language:English
Published: MDPI AG 2024-07-01
Series:Applied Sciences
Subjects:
vision transformers
MViT
ShuffleNet
CNN
land cover classification
Online Access:https://www.mdpi.com/2076-3417/14/13/5920

Internet

https://www.mdpi.com/2076-3417/14/13/5920

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