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.

我们介绍了增强型自动质量评估网络（IBS-AQSNet），这是一种创新的解决方案，用于评估高分辨率遥感图像中的交互式建筑分割质量。我们的方法通过识别遗漏和错误的分割区域来解决这个分割质量评估的新挑战。实验证明，我们提出的方法在自动化分割质量评估方面具有优越性，从而在该领域树立了新的标杆。