CONSTRUCTION AND OPTIMIZATION OF SAMPLE SET OF CONSTRUCTION WASTE DUMP

LI Si-qi; LIU Yang; DU Ming-yi; ZHANG Min; XIN Chao; YAO Yi; MA Teng-yue

doi:10.13205/j.hjgc.202003007

Volume 38 Issue 3

Jun. 2020

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LI Si-qi, LIU Yang, DU Ming-yi, ZHANG Min, XIN Chao, YAO Yi, MA Teng-yue. CONSTRUCTION AND OPTIMIZATION OF SAMPLE SET OF CONSTRUCTION WASTE DUMP[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(3): 39-45,8. doi: 10.13205/j.hjgc.202003007

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CONSTRUCTION AND OPTIMIZATION OF SAMPLE SET OF CONSTRUCTION WASTE DUMP

doi: 10.13205/j.hjgc.202003007

1. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China;
2. Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Received Date: 2019-11-07

Abstract

Abstract

The unregulated stacking of urban construction waste endangered the environment and the safety of citizens. In order to identify the stacking points of construction waste, aiming at the problem that there was no stacking point sample set for construction waste, this paper used the pixel based remote sensing classification method to build a sample set. On this basis, the adaptive histogram averaging, CS-LBP operator constraints and migration learning were combined to optimize the Wasserstein generative adversarial networks(WGAN) and generate samples to expand the sample set. The experimental results showed that the pixel based remote sensing classification method improved the efficiency of the artificial sample set, and the WGAN optimized method could effectively inherit the color and texture features of the original data to meet the needs of expanding the sample set.
- construction waste dump,
- sample set construction,
- WGAN,
- data enhancement,
- CS-LBP operator

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References(15)

References

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