CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification - Mateus de Souza Miranda; Ronny Hansch; Valdivino Alexandre de Santiago Junior; Thales Sehn Korting; Erison Carlos dos Santos Monteiro (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025)
The Cerrado faces increasing environmental pressures, necessitating accurate land use and land cover mapping despite challenges, such as class imbalance and visually similar categories. To address this, we present CerraData-4 MM, a multimodal dataset combining Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral imagery with 10 m spatial resolution. The dataset includes two hierarchical classification levels with seven and 14 classes, respectively, focusing on the diverse Bico do Papagaio ecoregion. We benchmark two models trained on CerraData-4 MM, employing a visual transformer-based architecture and a convolutional-based architecture. The ViT achieves superior performance in multimodal scenarios, with the highest macro F1-score of 57.60% and a mean Intersection over Union of 49.05% at the first hierarchical level. Both models struggle with minority classes, particularly at the second hierarchical level, where U-Net’s performance drops to an F1-score of 18.16%. Weighted loss improves representation for underrepresented classes but reduces overall accuracy, underscoring the trade-off in weighted training. CerraData-4 MM offers a challenging benchmark for advancing deep learning models to handle class imbalance and multimodal data fusion.