Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification

Over the past few decades, a large collection of feature ex-traction and classification algorithms have been developed for land cover mapping using remote sensing data. Although these methods have shown the gradually-increasing performance, their potential inevitably meets the bottleneck due to the lack of high-quality and diversified remote sensing bench-mark datasets, particularly for the multimodal cases. Accordingly, this, to a larger extent, limits the development of the corresponding methodologies and the practical application of land cover classification. To this end, we aim in this pa-per to introduce and build several multimodal remote sensing benchmark datasets for land cover classification. Further-more, two new multimodal land cover classification bench-mark datasets, i.e., Berlin and Augsburg, are openly available. Experiments are conducted on the two datasets for evaluating the performance of several multimodal feature learning and classification methods.