In this research, we introduce the enhanced automated quality assessment network (IBS-AQSNet), an innovative solution for assessing the quality of interactive building segmentation within high-resolution remote sensing imagery. This is a new challenge in segmentation quality assessment, and our proposed IBS-AQSNet allievate this by identifying missed and mistaken segment areas. First of all, to acquire robust image features, our method combines a robust, pre-trained backbone with a lightweight counterpart for comprehensive feature extraction from imagery and segmentation results. These features are then fused through a simple combination of concatenation, convolution layers, and residual connections. Additionally, ISR-AQSNet incorporates a multi-scale differential quality assessment decoder, proficient in pinpointing areas where segmentation result is either missed or mistaken. Experiments on a newly-built EVLab-BGZ dataset, which includes over 39,198 buildings, demonstrate the superiority of the proposed method in automating segmentation quality assessment, thereby setting a new benchmark in the field.

翻译：本研究提出增强型自动质量评估网络（IBS-AQSNet），用于评估高分辨率遥感影像中交互式建筑分割质量的创新方案。针对分割质量评估这一新挑战，本方法通过识别分割遗漏与误分割区域有效解决问题。首先，为获取稳健图像特征，我们结合预训练强骨干网络与轻量级骨干网络，对影像及分割结果进行综合特征提取。随后通过拼接、卷积层和残差连接的简单组合实现特征融合。此外，ISR-AQSNet 配备多尺度差分质量评估解码器，能精准定位分割结果中遗漏或误判的区域。在新建的包含39,198栋建筑的EVLab-BGZ数据集上的实验表明，本方法在自动化分割质量评估中具有优越性，为该领域树立了新基准。