A Machine Learning-Enhanced Method for Quantifying and Recycling Construction and Demolition Waste in India

Ramnarayan, None; Malla, Pragya

doi:10.1109/icicacs57338.2023.10099602

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…However, such methods are generally only suitable for shortterm forecasting and cannot accurately estimate the annual production of construction waste. (6) Other methods mainly include the popular shallow machine learning (SML) methods for prediction in recent years [20,21]. However, the traditional methods consume considerable manpower, resources, and time and lack high accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

The Cost of Urban Renewal: Annual Construction Waste Estimation via Multi-Scale Target Information Extraction and Attention-Enhanced Networks in Changping District, Beijing

Huang,

Lin,

Liu

et al. 2024

Remote Sensing

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Construction waste is an inevitable byproduct of urban renewal, causing severe pressure on the environment, health, and ecology. Accurately estimating the production of construction waste is crucial for assessing the consumption of urban renewal. However, traditional manual estimation methods rely heavily on statistical data and historical experience, which lack flexibility in practical applications and are time-consuming and labor-intensive. In addition, their accuracy and timeliness need to be improved urgently. Fortunately, with the advantages of high-resolution remote sensing images (HRSIs) such as strong timeliness, large amounts of information, and macroscopic observations, they are suitable for the large-scale dynamic change detection of construction waste. However, the existing deep learning models have a relatively poor ability to extract and fuse features for small and multi-scale targets, and it is difficult to deal with irregularly shaped and fragmented detection areas. Therefore, this study proposes a Multi-scale Target Attention-Enhanced Network (MT-AENet), which is used to dynamically track and detect changes in buildings and construction waste disposal sites through HRSIs and accurately estimate the annual production of urban construction waste. The MT-AENet introduces a novel encoder–decoder architecture. In the encoder, ResNet-101 is utilized to extract high-level semantic features. A depthwise separable-atrous spatial pyramid pooling (DS-ASPP) module with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. A dual-attention mechanism module (DAMM) is employed to better preserve positional and channel details. In the decoder, multi-scale feature fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. The MT-AENet is used to extract buildings and construction waste at different periods in the study area, and the actual production and landfill volume of construction waste are calculated based on area changes, indirectly measuring the rate of urban construction waste resource conversion. The experimental results in Changping District, Beijing demonstrate that the MT-AENet outperforms existing baseline networks in extracting buildings and construction waste. The results of this study are validated according to government statistical standards, providing a promising direction for efficiently analyzing the consumption of urban renewal.

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Section: Introductionmentioning

confidence: 99%

The Cost of Urban Renewal: Annual Construction Waste Estimation via Multi-Scale Target Information Extraction and Attention-Enhanced Networks in Changping District, Beijing

Huang,

Lin,

Liu

et al. 2024

Remote Sensing

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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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A Machine Learning-Enhanced Method for Quantifying and Recycling Construction and Demolition Waste in India

Cited by 2 publications

References 30 publications

The Cost of Urban Renewal: Annual Construction Waste Estimation via Multi-Scale Target Information Extraction and Attention-Enhanced Networks in Changping District, Beijing

The Cost of Urban Renewal: Annual Construction Waste Estimation via Multi-Scale Target Information Extraction and Attention-Enhanced Networks in Changping District, Beijing

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

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