  Object detection in remote sensing is a crucial computer vision task that has
seen significant advancements with deep learning techniques. However, most
existing works in this area focus on the use of generic object detection and do
not leverage the potential of multimodal data fusion. In this paper, we present
a comparison of methods for multimodal object detection in remote sensing,
survey available multimodal datasets suitable for evaluation, and discuss
future directions.
