Abstract
Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications. However, existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples. This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios. To address this limitation, we introduce the Million-scale finE-grained geospatial scEne classification dataseT (MEET), which contains over 1.03 million zoom-free remote sensing scene samples, manually annotated into 80 fine-grained categories. In MEET, each scene sample follows a scene-inscene layout, where the central scene serves as the reference, and auxiliary scenes provide crucial spatial context for finegrained classification. Moreover, to tackle the emerging challenge of scene-in-scene classification, we present the Context-Aware Transformer (CAT), a model specifically designed for this task, which adaptively fuses spatial context to accurately classify the scene samples. CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes. Based on MEET, we establish a comprehensive benchmark for fine-grained geospatial scene classification, evaluating CAT against 11 competitive baselines. The results demonstrate that CAT significantly outperforms these baselines, achieving a 1.88% higher balanced accuracy (BA) with the Swin-Large backbone, and a notable 7.87% improvement with the Swin-Huge backbone. Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping. The source code and dataset will be publicly available at this https URL.
Abstract (translated)
使用遥感图像进行精确的细粒度地理场景分类对于广泛的应用至关重要。然而，现有的方法往往依赖于手动缩放遥感图像以不同比例创建典型的场景样本，这种方法无法充分支持现实世界中固定分辨率图像解释的需求。为了解决这一局限性，我们引入了百万级细粒度地理场景分类数据集（MEET），该数据集包含超过103万张无需缩放的遥感场景样本，并由人工标注到80个细粒度类别中。在MEET数据集中，每个场景样本遵循场景内场景布局，其中中心场景作为参考，辅助场景提供对精细分类至关重要的空间上下文信息。 此外，为了应对场景内场景分类这一新兴挑战，我们提出了一个专门为该任务设计的模型——上下文感知变换器（CAT），它能够自适应地融合空间上下文以准确分类场景样本。CAT通过学习捕捉中心和辅助场景之间关系的关注特征来自适应地融合空间上下文信息。 基于MEET数据集，我们在细粒度地理场景分类方面建立了全面的基准测试，并将CAT与11个具有竞争力的基础模型进行了比较。结果表明，CAT显著优于这些基础模型，在使用Swin-Large骨干网络时，平衡准确率（BA）提高了1.88%，而在使用Swin-Huge骨干网络时，取得了7.87%的显着改进。 进一步的实验验证了CAT中每个模块的有效性，并展示了CAT在城市功能区制图中的实际应用价值。源代码和数据集将在以下网址公开发布：[https URL]。
URL
https://arxiv.org/abs/2503.11219
