Benchmarking Deep Learning Architectures for Challenging Land cover Mapping in northern Benin using Sentinel-2 Time Series with Limited Data

The timely monitoring of land changes is of capital importance to support sustainable development, especially in regions that are particularly exposed to the effects of climate change. Deriving land cover maps from satellite image time series (SITS) is an established practice to enable the tracking of land dynamics, notably in the agricultural and environmental domain. While classical machine learning remains widely used to accomplish this task in operational settings, advances in deep learning offer new paradigms susceptible to improve the precision of these products.However, if deep learning based land cover mapping is proving its effectiveness in different contexts, including at global-scale, no clear evidence has emerged yet on the advantages that such technical shift may provide in the context of tropical agricultural landscapes. A certain number of factors, from the poor availability of exploitable satellite acquisition to the limitations of reference data bases, may hinder the potential of such techniques and make their use eventually inefficient.In order to give a full insight on a real-world application on challenging tropical agricultural landscapes, we here present a study aimed at comparing several state-of-the-art deep-learning approaches for land cover mapping using SITS on two areas located in Northern Benin, in the sub-humid West African region. Among the deep learning models explicitly exploiting the temporal dimension, TempCNN emerged as the most effective, outperforming Random Forest in overall accuracy, weighted F1-score, and computational efficiency, with notable robustness across sites and improved discrimination of minority classes. More complex architectures such as ConvTran and DuPLO provided no additional benefit, highlighting that model simplicity, when well-suited to the problem, can outperform elaborate designs for LULC classification from multi-temporal optical data.