BuildWin-SAM: An Improved SAM-Based Method for Extracting Building Windows From Street View Images
BuildWin-SAM: An Improved SAM-Based Method for Extracting Building Windows From Street View Images
Blog Article
Building facade segmentation provides critical support for urban information management, precise 3D reconstruction, and energy consumption analysis.Window, as a pivotal component of building facades, plays a central role in these applications.However, accurately identifying windows in diverse urban environments poses significant challenges due to dataset limitations and variability in model performance.This study presents two primary contributions: first, we develop the Street View Building Window (SVBW) segmentation dataset, comprising 1,172 images that represent diverse urban contexts and window types, with a total of 50,321 meticulously annotated window instances.This dataset addresses existing gaps in segmenting irregular building facades.
Second, we propose BuildWin-SAM, a model for window extraction peperomia double duty based on the Segment Anything Model (SAM) architecture, which is trained on the SVBW dataset.Comparative here analysis with CNN-based semantic segmentation models and SAM demonstrates that BuildWin-SAM achieves improvements across key evaluation metrics, including Intersection over Union (IoU), F1 score, precision, and recall.Specifically, BuildWin-SAM achieves an IoU of 80.70%, precision of 89.43%, recall of 89.
20%, and an F1 score of 88.52%, demonstrating precise window localization and delineation capabilities.To further validate its robustness, we conduct evaluations on three public datasets featuring multi-scale and multi-scene images with building window annotations.BuildWin-SAM achieves Recall rates exceeding 72% and Precision rates mainly above 87% across these datasets.These results demonstrate BuildWin-SAM’s potential to significantly enhance building window recognition in diverse urban environments, ultimately contributing to advancements in building information management and other relevant applications.
The SVBW dataset will be provided at