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

TL;DR

This paper introduces ReCon1M, the first large-scale, publicly available dataset for relation comprehension in remote sensing imagery, enabling advanced scene graph generation research in this domain.

Contribution

The paper presents ReCon1M, a million-level annotated dataset for remote sensing images, facilitating the development and evaluation of scene graph generation methods in aerial imagery.

Findings

Mainstream SGG methods evaluated on ReCon1M.

Dataset contains over 21,000 images and 1.1 million relation triplets.

Benchmark results highlight challenges and opportunities in remote sensing SGG.

Abstract

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.