  Scene Graph Generation (SGG) is a high-level visual understanding and
reasoning task aimed at extracting entities (such as objects) and their
interrelationships from images. Significant progress has been made in the study
of SGG in natural images in recent years, but its exploration in the domain of
remote sensing images remains very limited. The complex characteristics of
remote sensing images necessitate higher time and manual interpretation costs
for annotation compared to natural images. The lack of a large-scale public SGG
benchmark is a major impediment to the advancement of SGG-related research in
aerial imagery. In this paper, we introduce the first publicly available
large-scale, million-level relation dataset in the field of remote sensing
images which is named as ReCon1M. Specifically, our dataset is built upon
Fair1M and comprises 21,392 images. It includes annotations for 859,751 object
bounding boxes across 60 different categories, and 1,149,342 relation triplets
across 64 categories based on these bounding boxes. We provide a detailed
description of the dataset's characteristics and statistical information. We
conducted two object detection tasks and three sub-tasks within SGG on this
dataset, assessing the performance of mainstream methods on these tasks.
