Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers
Abstract
Plant phenology-the study of recurrent life cycle events-is essential for understanding ecosystem dynamics and their responses to climate change impacts. While Unmanned Aerial Vehicles (UAVs) and near-surface cameras enable high-resolution monitoring, identifying plant species across time remains computationally challenging. State-of-the-art approaches, specifically Multi-Temporal Convolutional Networks (CNNs), rely on rigid multi-branch architectures that scale poorly with longer time series and require large spatial context windows. In this paper, we present an extensive study on optimizing Vision Transformers (ViTs) for efficient spatio-temporal vegetation pixel classification. We conducted a comprehensive ablation study analyzing seven key design dimensions, including: (i) data normalization; (ii) spectral arrangement; (iii) boundary handling; (iv) spatial context window shape and size; (v) tokenization strategies; (vi) positional encoding; and (vii) feature aggregation strategies. Our method was evaluated on two datasets from the Brazilian Cerrado biome, Serra do Cip\'o (aerial imagery) and Itirapina (near-surface imagery). Experimental results demonstrate that our ViT approach offers a substantial improvement in computational efficiency while maintaining competitive classification performance. Notably, our ViT reduces Floating Point Operations (FLOPs) by an order of magnitude and maintains constant parameter complexity regardless of the time series length, whereas the CNN baseline scales linearly. Our findings confirm that ViTs are a robust, scalable solution for resource-constrained phenological monitoring systems.
Cite
@article{arxiv.2605.00296,
title = {Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers},
author = {Alan Gomes and Anderson Gonçalves and Samuel Felipe dos Santos and Nathan Felipe Alves and Magna Soelma Beserra de Moura and Bruna de Costa Alberton and Leonor Patricia C. Morellato and Ricardo da Silva Torres and Jurandy Almeida},
journal= {arXiv preprint arXiv:2605.00296},
year = {2026}
}