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This paper introduces ResCD-CSI, a manually labeled benchmark of 16,365 bounding-box annotations across 14 CSI construction divisions, drawn from active US residential sites. Its main contribution is a standardized, industry-aligned dataset for object detection, which addresses a real bottleneck for applying computer vision in construction. Readers should note that the abstract doesn't report baseline model results, dataset size in images, or licensing terms, so its practical value will depend on those details and on how well models trained on it generalize beyond the sampled sites.
Residential Construction Image Dataset Using CSI Divisions for Deep Learning-based Object Detection
arXiv:2610.25535v1 Announce Type: cross Abstract: Accurate recognition of construction site elements is essential for the application of deep learning (DL) in the construction industry. Despite rapid advances in computer vision, the adoption of DL in construction has been limited by the lack of structured, publicly available datasets aligned with established industry standards. To address this gap, we introduce the Residential Construction Dataset Using CSI Divisions (ResCD-CSI) image dataset, a manually labeled image benchmark organized according to the divisional structure defined by the Construction Specifications Institute (CSI). The dataset comprises high-resolution images collected from active residential construction sites across the United States, capturing diverse building components, materials, and equipment under real-world conditions. In total, 16,365 objects spanning 14 major CSI divisions were precisely annotated using bounding boxes, ensuring consistency and reliability for object detection tasks. By aligning with the CSI divisional framework, ResCD-CSI establishes a standardized resource that bridges construction practice with computer vision research and supports the development of Artificial Intelligence-driven solutions for residential construction monitoring.
Abstract:Accurate recognition of construction site elements is essential for the application of deep learning (DL) in the construction industry. Despite rapid advances in computer vision, the adoption of DL in construction has been limited by the lack of structured, publicly available datasets aligned with established industry standards. To address this gap, we introduce the Residential Construction Dataset Using CSI Divisions (ResCD-CSI) image dataset, a manually labeled image benchmark organized according to the divisional structure defined by the Construction Specifications Institute (CSI). The dataset comprises high-resolution images collected from active residential construction sites across the United States, capturing diverse building components, materials, and equipment under real-world conditions. In total, 16,365 objects spanning 14 major CSI divisions were precisely annotated using bounding boxes, ensuring consistency and reliability for object detection tasks. By aligning with the CSI divisional framework, ResCD-CSI establishes a standardized resource that bridges construction practice with computer vision research and supports the development of Artificial Intelligence-driven solutions for residential construction monitoring.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2610.25535 [cs.CV]
(or arXiv:2610.25535v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.25535 Focus to learn more arXiv-issued DOI via DataCite
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