Sirimewan Diani, Dayarathna Sanuwani, Raman Sudharshan, Bai Yu, Arashpour Mehrdad
Department of Civil Engineering, Faculty of Engineering, Monash University, Melbourne, Australia.
Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Australia.
Sci Data. 2025 May 28;12(1):885. doi: 10.1038/s41597-025-05243-x.
Efficient management of construction and demolition waste (CDW) is essential for enhancing resource recovery. The lack of publicly available, high-quality datasets for waste recognition limits the development and adoption of automated waste handling solutions. To facilitate data sharing and reuse, this study introduces 'CDW-Seg', a benchmark dataset for class-wise segmentation of CDW. The dataset comprises high-resolution images captured at authentic construction sites, featuring skip bins filled with a diverse mixture of CDW materials in-the-wild. It includes 5,413 manually annotated objects across ten categories: concrete, fill dirt, timber, hard plastic, soft plastic, steel, fabric, cardboard, plasterboard, and the skip bin, representing a total of 2,492,021,189 pixels. Each object was meticulously annotated through semantic segmentation, providing reliable ground-truth labels. To demonstrate the applicability of the dataset, an adapter-based fine-tuning approach was implemented using a hierarchical Vision Transformer, ensuring computational efficiency suitable for deployment in automated waste handling scenarios. The CDW-Seg has been made publicly accessible to promote data sharing, facilitate further research, and support the development of automated solutions for resource recovery.
高效管理建筑与拆除废物（CDW）对于提高资源回收率至关重要。缺乏用于废物识别的公开可用高质量数据集限制了自动化废物处理解决方案的开发与采用。为促进数据共享与重用，本研究引入了“CDW-Seg”，这是一个用于CDW按类别分割的基准数据集。该数据集包含在真实建筑工地拍摄的高分辨率图像，以野外装满各种CDW材料混合物的翻斗车为特色。它包括跨越十个类别的5413个手动标注对象：混凝土、填土、木材、硬塑料、软塑料、钢材、织物、纸板、石膏板以及翻斗车，代表总共2492021189像素。每个对象都通过语义分割进行了精心标注，提供了可靠的地面真值标签。为证明该数据集的适用性，使用分层视觉Transformer实施了基于适配器的微调方法，确保计算效率适合在自动化废物处理场景中部署。CDW-Seg已公开提供，以促进数据共享、推动进一步研究并支持资源回收自动化解决方案的开发。
