ReCon1M: A Large-Scale Benchmark Dataset for Relation Comprehension in Remote Sensing Imagery

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 article, we introduce the first publicly available large-scale, million-level relation dataset in the field of remote sensing images, which is named ReCon1M. Specifically, our dataset is built upon FAIR1M and comprises 22 262 images. It includes annotations for 873 761 object bounding boxes across 60 categories and 1 052 223 relation triplets across 59 categories based on these bounding boxes. We provide a detailed description of the dataset’s characteristics and statistical information. In addition, an efficient global context-aware network (EGCAN) is proposed to improve inference efficiency in dense relation prediction through an object-pair pre-screening mechanism. By integrating visual, spatial, and semantic features, EGCAN captures fine-grained pairwise features and object-level contextual information to enhance its ability to discriminate relation. We conduct two object detection tasks and three subtasks within SGG on this dataset, assessing the performance of mainstream methods on these tasks. The experimental results show that the proposed EGCAN achieves state-of-the-art (SOTA) performance in 17 out of 24 accuracy metrics across three tasks and delivers the best performance in frames per second (FPS) for model inference. The ReCon1M dataset and related resources are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://recon1m-dataset.github.io/</uri>