The installation of curtain wall modules (CWM) in high-rise buildings is a complex task that poses significant safety risks due to manual labor, especially when working at great heights. Traditional methods are labor-intensive, time-consuming, and expose workers to hazards such as falls and equipment malfunctions. To mitigate these risks and enhance operational efficiency, automation and precise positioning of CWMs are essential. Accurate detection of installation locations becomes critical, as it enables crane operators or autonomous robots to position CWMs safely and precisely. This study introduces a novel approach utilizing semantic segmentation for detecting CWM installation locations. To address the challenges of deploying deep learning models on edge devices in construction environments, we propose Lightweight Attention Network (LANet), a lightweight, single-stream semantic segmentation architecture. LANet incorporates an optimized transformer module for global context modeling with linear complexity, enabling efficient feature extraction while maintaining computational efficiency. Additionally, we have developed a custom curtain wall dataset tailored for automating CWM installation, which was used to train and evaluate LANet. Experimental results demonstrate that LANet achieves competitive segmentation accuracy with only 1.92 million parameters, delivering real-time performance at 262 FPS on an RTX 3090 GPU and 19 FPS on a standard Intel i7 CPU. These results make LANet highly suitable for deployment in resource-constrained environments.

Effective handling of construction and demolition waste (CDW) is crucial for sustainable resource recovery in industrial settings. This study introduces WasteXtract , a vision-based AI model for class-specific segmentation of CDW designed to automate waste monitoring and sorting at material recovery facilities (MRFs). The theoretical contribution of this research lies in adapting large-scale vision foundation models by incorporating a class-specific mask decoder and a lightweight adapter in a ViT-based image encoder. This approach enables efficient and scalable fine-tuning for the industrial application of CDW recognition, achieving reliable waste detection and classification performance. The engineering applications of WasteXtract are validated across two datasets representing real-world recognition of waste composition in skip-bins and conveyor-belt scenarios for intelligent automation and optimised resource recovery. The segmentation results show that WasteXtract achieves mean weighted intersection over union (WIoU) scores of 0.66 in skip-bin waste composition monitoring and 0.96 in the conveyor-belt scenario. Compared to baseline models such as DeepLabv3+ and U-Net, WasteXtract achieves significantly higher segmentation accuracy while reducing the number of trainable parameters, demonstrating its suitability for deployment in real-world, resource-constrained environments such as MRFs. This study highlights the transformative role of informatics in advancing automation and efficiency within sustainable waste management.