Multimodal-Ready Sentinel Dataset for Natural Hazards Supervised Learning

The fusion of multimodal Remote Sensing (RS) data exploits the complementary nature of specific datasets, enhancing their analytical qualities beyond what is achievable when data are processed in isolation. Furthermore, integration of natively disparate data types, such as imagery and geospatial data, could substantially increase the image classification and semantic segmentation performances [1] [2].This study focuses on the automatically development of a multimodal benchmarking dataset, following the methodological principles of the BigEarthNet-MM [3] dataset and their integration in a minimal multimodal database. Unlike the original set, three new classes ("Flooded Area", "Burned Area", and "Volcanic Landscape") have been added in order to ensure a consistent performance evaluation using the same models as BigEarthNet-MM.The innovative aspect of the proposed dataset lies in its automatic developing and updating, highly accurate labelling-by-design process, and the existence of an additional data layer facilitating meaningful semantic connections as a prerequisite for a minimal multimodal database implementation.