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MeViT: A Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery for Agriculture in Thailand

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By Teerapong Panboonyuen, Chaiyut Charoenphon, Chalermchon Satirapod
| MDPI
1. Introduction Semantic segmentation of land use and land cover (LULC) features in remote sensing images (see ) is essential in Earth observation [1,2,3,4,5,6]. Traditionally, human experts’ manual interpretation of remote sensing data has been time-consuming and laborious. With deep learning techniques, particularly convolutional neural networks (CNNs), automatic LULC feature extraction has become much faster and more accurate [7,8,9,].
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