## 1 Introduction  
Panoramic imaging offers a promising solution for comprehensive visual monitoring in indoor construction environments. This study aims to evaluate and improve segmentation model performance for such imagery to support practical tasks in construction site management. In indoor construction sites, panoramic images are gaining attention as a valuable visual tool for process and quality management, as they can comprehensively capture the entire environment in a single shot [1]. With recent advancements in deep learning, segmentation models have been integrated with panoramic images to enable automated indoor construction monitoring [2; 3; 4] and dimensional quality assessment (DQA) [5], thereby supporting site management and accelerating the digital transformation of the construction industry. However, unlike outdoor construction sites where large-scale objects can be relatively easily captured and analyzed in open spaces, indoor construction sites are structurally complex, spatially constrained, and require the precise identification of small-scale objects under limited visibility conditions [2; 6]. In particular, various environmental factors in indoor construction sites, such as uneven lighting due to artificial illumination, occlusions from obstacles (workers, machinery, etc. ), and complex backgrounds with unfinished interiors, make precise segmentation challenging, requiring careful consideration during data acquisition [6; 7; 8]. Moreover, segmentation models specifically tailored for indoor construction environments remain scarce, and publicly available datasets are highly limited [2]. These limitations create a gap between technological advancement and practical application, thereby impeding the effective deployment of panoramic segmentation in construction site contexts. To achieve effective panoramic image segmentation, a large-scale dataset for model training must be constructed. However, constructing datasets for indoor construction sites is a labor-intensive and time-consuming process [9]. To address these issues, research has explored synthetic dataset generation using building information modeling (BIM) [10; 11], game engines [12], and generative AI [13]. These approaches allow the generation of high-quality synthetic data that can be structured into various formats, such as panoramic images. However, generating synthetic datasets requires additional modeling processes, which involve considerable manual labor [10; 11; 12]. Furthermore, synthetic datasets may not fully capture the detailed characteristics of real-world objects, and if the original dataset contains biases, those biases may be amplified in the generated synthetic data [13; 14]. Meanwhile, the Segment Anything Model (SAM) [15], a representative foundation model for image segmentation, has garnered considerable attention from researchers due to its strong generalization performance, even when applied to construction-related data. For example, [PERSON] et al. proposed Omni-Scan2 BIM, which integrates SAM and DINOv2 to automate the scan-to-BIM process in mechanical, electrical, and plumbing (MEP) construction sites [16]. [PERSON] et al. utilized SAM to develop a segmentation framework for detecting both land and underwater cracks in bridges [17], while [PERSON] et al. combined SAM with the RANSAC algorithm to address quality degradation issues in 3D reconstruction of indoor buildings, particularly in texture-deficient areas [18]. These studies have demonstrated that SAM can be effectively applied even in complex construction environments, yielding higher-quality segmentation results compared to conventional methods, without the need to construct additional datasets. In response to these challenges, this study proposes a novel SAM-based approach to improve panoramic image segmentation in indoor construction environments, addressing challenges posed by dataset limitations and environmental complexities.