AI RESEARCH
A Comparative Study of Transformer and Convolutional Models for Crop Segmentation from Satellite Image Time Series
arXiv CS.CV
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ArXi:2412.01944v2 Announce Type: replace Crop segmentation from satellite image time series (SITS) is a fundamental task for agricultural monitoring and land-use analysis. While convolutional neural networks (CNNs) have been widely used, transformer-based architectures offer alternative mechanisms for representing spatial and temporal dependencies in multispectral data.