Considering current problems in the identifying, locating, and supervising of construction waste, it is difficult to achieve real-time target judgement and identification in the short term. This study proposes a deep learning-based construction waste identification and classification system, named IUNet-IF, developed using unmanned aerial vehicle (UAV) images. High-resolution UAV aerial images were used to establish a sample database and train U-Net models. The U-Net model, which exhibited the highest overall performance and identification accuracy, was enhanced to develop an IUNet model. Subsequently, the texture and colour features of UAV images were extracted and input to the IUNet model to build a novel IUNet-IF model. The proposed method uses the optimal model and an input combination of texture features, colour features, and original images, which improves the segmentation results compared to those when only the original image is used. The experimental results indicate that the system can achieve a maximum pixel accuracy index, mean intersection over union (mIoU), and mean pixel accuracy (MPA) of 98.98%, 93.78%, and 97.26%, respectively. These results verify the feasibility of extracting construction waste covered by a dust-proof net from UAV remote sensing images and can provide reference information for the automatic extraction of construction waste.