Identification of construction and demolition waste based on change detection and deep learning

Zhao, Xue; Yang, Yang; Duan, Fuzhou; Zhang, Miao; Guo-fu, Jiang; Yan, Xing; Cao, Shisong; Zhao, Wenji

doi:10.1080/01431161.2022.2054296

Cited by 12 publications

(2 citation statements)

References 23 publications

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“…Similarly, Zhao et al . proposed a method of construction waste recognition based on change detection and deep learning 10 , while Lu et al . proposed an automatic identification method for the composition of construction waste mixtures using semantic segmentation in computer vision 11 .…”

Section: Background and Summarymentioning

confidence: 99%

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Lin,

Huang,

Liu

et al. 2024

Sci Data

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Construction waste is unavoidable in the process of urban development, causing serious environmental pollution. Accurate assessment of municipal construction waste generation requires building construction waste identification models using deep learning technology. However, this process requires high-quality public datasets for model training and validation. This study utilizes Google Earth and GF-2 images as the data source to construct a specific dataset of construction waste landfills in the Changping and Daxing districts of Beijing, China. This dataset contains 3,653 samples of the original image areas and provides mask-labeled images in the semantic segmentation domains. Each pixel within a construction waste landfill is classified into 4 categories of the image areas, including background area, vacant landfillable area, engineering facility area, and waste dumping area. The dataset contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The pixel-level semantic segmentation labels are provided to quantify the construction waste yield, which can serve as the basic data for construction waste extraction and yield estimation both for academic and industrial research.

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Section: Background and Summarymentioning

confidence: 99%

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Lin,

Huang,

Liu

et al. 2024

Sci Data

View full text Add to dashboard Cite

show abstract

“…The hardware environment is an Intel Core i9-12900KF, 32GB RAM, and an Nvidia GeForce GTX3060 Ti graphics card.We use Overall Accuracy (OA), Average Accuracy (AA), and Kappa coefficient as classification accuracy evaluation metrics to measure the performance of classification. The OA, AA, and Kappa coefficient can be defined as[42][43][44]:…”

mentioning

confidence: 99%

Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism

Zhang

Huanhuan

et al. 2023

Preprint

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Convolutional neural networks and graph convolutional neural networks are two classical deep learning models that have been widely used in hyperspectral image classification tasks with remarkable achievements. However, hyperspectral image classification models based on graph convolutional neural networks using only shallow spectral or spatial features are insufficient to provide reliable similarity measures for constructing graph structures, limiting their classification performance. To address this problem, we propose a hyperspectral image classification model combining 3D-2D hybrid convolution and a graph attention mechanism. First, a 3D-2D hybrid convolutional network is constructed and used to rapidly extract deep features that express spatial and spectral associations. Then, the graph is built based on deep spatial-spectral features to enhance the feature representation of the graph. Finally, a network of graph attention mechanisms is adopted to learn long-range spatial connections and to classify them using the extracted spatial features. The experimental results on two datasets, Indian Pine and the University of Pavia, show that the proposed method can achieve higher classification accuracy compared with other advanced methods.

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Hyperspectral Image Classification Based on 3D–2D Hybrid Convolution and Graph Attention Mechanism

Zhang,

Tu,

et al. 2024

Neural Process Lett

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Convolutional neural networks and graph convolutional neural networks are two classical deep learning models that have been widely used in hyperspectral image classification tasks with remarkable achievements. However, hyperspectral image classification models based on graph convolutional neural networks using only shallow spectral or spatial features are insufficient to provide reliable similarity measures for constructing graph structures, limiting their classification performance. To address this problem, we propose a new end-to-end hyperspectral image classification model combining 3D–2D hybrid convolution and a graph attention mechanism (3D–2D-GAT). The model utilizes the collaborative work of hybrid convolutional feature extraction module and GAT module to improve classification accuracy. First, a 3D–2D hybrid convolutional network is constructed and used to quickly extract the discriminant deep spatial-spectral features of various ground objects in hyperspectral image. Then, the graph is built based on deep spatial-spectral features to enhance the feature representation ability. Finally, a network of graph attention mechanism is adopted to learn long-range spatial relationship and distinguish the intra-class variation and inter-class similarity among different samples. The experimental results on three datasets, Indian Pine, the University of Pavia and Salinas Valley show that the proposed method can achieve higher classification accuracy compared with other advanced methods.

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Identification of construction and demolition waste based on change detection and deep learning

Cited by 12 publications

References 23 publications

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism

Hyperspectral Image Classification Based on 3D–2D Hybrid Convolution and Graph Attention Mechanism

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