Boosting Urban Openspace Mapping with the Enhancement Feature Fusion of Object Geometry Prior Information from Vision Foundation Model

Urban open spaces (UO) play a crucial role in urban environments, particularly in areas where social and economic activities are rapidly increasing. However, the challenges of high inter-class similarity, complex environmental surroundings, and scale variations often result in suboptimal performance in UO mapping. To address these issues, this paper proposes UOSAM, a novel approach that leverages the Segment Anything Model (SAM) for efficient UO mapping using high-resolution remote sensing images. Our method employs a pyramid transformer to extract feature pyramids at multiple scales, capturing multi-scale semantic context and addressing the issue of scale variation. Additionally, SAM is used to achieve the more precise geometry segmentation of ubiquitous objects within the images, effectively tackling the challenges posed by their high inter-class similarity and environmental complexity. Furthermore, we introduce a feature fusion module (FFM) that integrates multi-level features from the remote sensing images. Extensive experiments conducted on the Urban Openspace China Ten Cities (UOCTC) dataset from ten major cities in China, using manually annotated samples, demonstrate the superiority of the proposed UOSAM.