IUNet-IF: identification of construction waste using unmanned aerial vehicle remote sensing and multi-layer deep learning methods

Gao, Siyan; Liu, Yang; Cao, Shisong; Chen, Qiang; Du, Mingyuan; Zhang, Daixinyue; Jia, Jingjue; Zou, Weilin

doi:10.1080/01431161.2022.2155084

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…For example, Gao et al . proposed a system for identifying and categorizing construction waste using remote sensing images by unmanned aerial vehicles and a multi-layer deep learning approach 9 . Similarly, Zhao et al .…”

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%