OpenEarthSensing: Large-Scale Fine-Grained Benchmark for Open-World Remote Sensing
Xiang Xiang, Zhuo Xu, Yao Deng, Qinhao Zhou, Yifan Liang, Ke Chen, Qingfang Zheng, Yaowei Wang, Xilin Chen, Wen Gao

TL;DR
OpenEarthSensing (OES) is a comprehensive large-scale benchmark designed to evaluate open-world remote sensing models across diverse tasks, categories, and data domains, addressing the lack of such benchmarks in the field.
Contribution
The paper introduces OES, a novel large-scale, fine-grained benchmark with 189 categories and multiple data domains for evaluating open-world remote sensing models.
Findings
Existing models struggle with diverse semantic and covariate shifts.
OES provides a challenging testbed for evaluating generalization.
Baseline methods show significant room for improvement.
Abstract
The advancement of remote sensing, including satellite systems, facilitates the continuous acquisition of remote sensing imagery globally, introducing novel challenges for achieving open-world tasks. Deployed models need to continuously adjust to a constant influx of new data, which frequently exhibits diverse shifts from the data encountered during the training phase. To effectively handle the new data, models are required to detect semantic shifts, adapt to covariate shifts, and continuously update their parameters without forgetting learned knowledge, as has been considered in works on a variety of open-world tasks. However, existing studies are typically conducted within a single dataset to simulate realistic conditions, with a lack of large-scale benchmarks capable of evaluating multiple open-world tasks. In this paper, we introduce \textbf{OpenEarthSensing (OES)}, a large-scale…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRemote-Sensing Image Classification · Domain Adaptation and Few-Shot Learning · Advanced Neural Network Applications
