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Urban green plastic cover extraction and spatial pattern changes in Jinan city based on DeepLabv3+ semantic segmentation model

  • role： First author第一作者
  • Affiliation：

    School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

  • Email：ctliu96@163.com
  • Introduction：刘春亭，研究方向为遥感信息提取及应用。E-mail: ctliu96@163.com
LIU Chunting1,  
  • Affiliation：

    College of Land Science and Technology, China Agricultural University, Beijing 100193, China

    State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China

FENG Quanlong23,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

  • Email：liujiantao18@sdjzu.edu.cn
  • Introduction：刘建涛，研究方向为机器学习、遥感信息提取、城市遥感。E-mail: liujiantao18@sdjzu.edu.cn
LIU Jiantao1*,  
  • Affiliation：

    Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

WANG Ying4,  
  • Affiliation：

    School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

SHI Tongguang1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Yi5,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

GONG Jianhua5,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHAO Huihui5

реферат

Green plastic covers have been widely used as the primary method of dust prevention in construction sites. The rapid acquisition of green plastic cover and spatial–temporal change information has an important guiding significance for the formulation of dust prevention and ecological environmental protection measures. This work extracted the covered area of green plastic cover by using DeepLabv3+ semantic segmentation model based on Sentinel-2 remote sensing data and realized the annual green plastic cover segmentation and extraction in Jinan from 2016 to 2020. The spatial distribution characteristics and spatial–temporal expansion trend of green plastic cover were analyzed by area statistics, landscape pattern analysis, and mean center-standard deviation ellipse. Results show that: (1) According to the accuracy evaluation, the proposed architecture reaches an acceptable accuracy, with 84.05% precision, 80.09% recall, 0.82 F1 score, and 69.72 IoU, which could quickly and accurately extract the urban green plastic cover and realize the large-scale and time series dynamic monitoring and management. (2) The accuracy of DeepLabv3+ model used in this work is the best compared with the traditional remote sensing classification method and other sematic segmentation models, and the extraction results of green plastic cover are more precise. In addition, the extraction experiments in Beijing and Tianjin also confirmed the migration ability of the model. (3) The laying range of green plastic cover significantly expanded from 2016 to 2020. The area and number of green plastic cover patches, fragmentation degree, and shape complexity increased. The average patch area increased, the cohesion and aggregation between patches fluctuated, and the landscape pattern was complex and unstable. The expansion has an obvious direction. The distribution and dynamic expansion of green plastic cover are affected by human factors, such as urban planning and project process. Urban planning determines the distribution of green plastic cover, and the current use of green plastic cover reflects the process of urban construction to a certain extent. Therefore, the dynamic monitoring and management of urban green plastic cover by remote sensing means can provide data and technical support for urban planning, ecological environment construction, and urban accurate management, which are of great significance to the management of urban expansion mode and reconstruction.

ключеви́че слова́

green plastic cover;remote sensing;semantic segmentation;DeepLabv3+;Sentinel-2;temporal and spatial variation

References

  1. 1.
    Badrinarayanan V, Kendall A and Cipolla R. 2017. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495
  2. 2.
    Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32
  3. 3.
    Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2014. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv preprint arXiv: 1412.7062
  4. 4.
    Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2018a. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4): 834-848
  5. 5.
    Chen L C, Papandreou G, Schrof F and Adam H. 2017. Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv preprint arXiv: 1706.05587
  6. 6.
    Chen L C, Zhu Y K, Papandreou G, Schrof F and Adam H. 2018b. Encoder-Decoder with atrous separable convolution for semantic image segmentation//Proceedings of 15th European Conference on Computer Vision (ECCV). Munich, Germany. 11211:833-851
  7. 7.
    Chollet F. 2017. Xception: Deep Learning with Depthwise Separable Convolutions//Proceedings of the Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 1800-1807
  8. 8.
    Cortes C and Vapnik V. 1995. Support-vector networks. Machine Learning, 20(3): 273-297
  9. 9.
    European Space Agency. 2015. ESA Standard Document[S/OL]. https://sentinel.esa.int/documents/247904/685211/Sentinel-2_User_Handbook, 2015-07-24/2021-04-25
  10. 10.
    Feng Q L, Niu B W, Zhu D H, Yao X C, Liu Y M, Ou C, Chen B A, Yang J Y, Guo H and Liu J T. 2021. National agricultural plastic greenhouses remote sensing classification data set in 2019. v1. Science Data Bank.
  11. 11.
    Feng Q L, Yang J Y, Zhu D H, Liu J T, Guo H, Batsaikhan B and Li B G. 2019. Integrating multitemporal Sentinel-1/2 data for coastal land cover classification using a multibranch convolutional neural network: a case of the Yellow River Delta. Remote Sensing, 11(9): 1006
  12. 12.
    Gu X T, Gao X H, Ma H J, Shi F F, Liu X M and Cao X M. 2019. Comparison of machine learning methods for land use/land cover classification in the complicated terrain regions. Remote Sensing Technology and Application, 34(1): 57-67
  13. 13.
    Jinan Bureau of Statistics. 2019. Statistical Communique of Jinan on 2019 National Economic and Social Development.
  14. 14.
    Jinan Municipal Government. 2016. The Master Plan of Jinan(2011—2020).
  15. 15.
    Jinan Municipal Government. 2018. Jinan City Annual Report on Government Information Disclosure in 2017.
  16. 16.
    Li D R, Yu H R and Li X. 2017. The spatial-temporal pattern analysis of city development in countries along the belt and road initiative based on nighttime light data. Geomatics and Information Science of Wuhan University, 42(6): 711-720
  17. 17.
    Li P P. 2020. Dust pollution damage analysis and control methods of building construction. Environmental Science and Management, 45(11): 84-88
  18. 18.
    Li S Q. 2020. Identification of construction waste from multi-source remote sensing image based on transfer learning. Beijing: Beijing University of Civil Engineering and Architecture (李思琦. 2020. 基于迁移学习的多源遥感影像建筑垃圾识别. 北京:北京建筑大学)
  19. 19.
    Liu J T, Feng Q L, Wang Y, Batsaikhan B, Gong J H, Li Y, Liu C T and Ma Y. 2020. Urban green plastic cover mapping based on VHR remote sensing images and a deep semi-supervised learning framework. ISPRS International Journal of Geo-Information, 9(9): 527
  20. 20.
    Liu P H, Liu X P, Liu M X, Shi Q, Yang J X, Xu X C and Zhang Y Y. 2019. Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network. Remote Sensing, 11(7):830
  21. 21.
    Liu W Y, Yue A Z, Ji J, Shi W H, Deng R R, Liang Y H and Xiong L H. 2020. Urban green space extraction from GF-2 remote sensing image based on DeepLabv3+ semantic segmentation model. Remote Sensing for Land & Resources, 32(2): 120-129
  22. 22.
    Liu Y L, Ren Y H, Wei C J, Wang A H, Zhou H Z and Chi Y B. 2009. Study on monitoring of informal open-air solid waste dumps based on Beijing-1 images. Journal of Remote Sensing, 13(2): 320-326
  23. 23.
    Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE Computer Society: 3431-3440
  24. 24.
    Ming D P, Li J, Wang J Y, Zhang M. 2015. Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example. ISPRS Journal of Photogrammetry and Remote Sensing, 106: 28-41
  25. 25.
    Panboonyuen T, Jitkajornwanich K, Lawawirojwong S, Srestasathiern P and Vateekul P. 2019. Semantic segmentation on remotely sensed images using an enhanced global convolutional network with channel attention and domain specific transfer learning. Remote Sensing, 11(1):83
  26. 26.
    Rodriguez-Galiano, V F, Ghimire B, Rogan J, Chica-Olmo M and Rigol-Sanchez J P. 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67: 93-104 [DOI ]
  27. 27.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional net-works for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer:234-241
  28. 28.
    Seydi S T, Akhoondzadeh M, Amani M and Mahdavi S. 2021. Wildfire damage assessment over Australia using Sentinel-2 imagery and MODIS land cover product within the google earth engine cloud platform. Remote Sensing, 13(2): 220-220
  29. 29.
    Shao Z F, Zhou Z F, Huang X and Zhang Y. 2021. MRENet: Simultaneous Extraction of Road Surface and Road Centerline inComplex Urban Scenes from Very High-Resolution Images. Remote Sensing, 13(2): 239
  30. 30.
    Shrestha S and Vanneschi L. 2018. Improved fully convolutional network with conditional random fields for building extraction. Remote Sensing, 10(7): 1135
  31. 31.
    Silvestri S and Omri M. 2008. A method for the remote sensing identification of uncontrolled landfills: formulation and validation. International Journal of Remote Sensing, 29(4): 975-989
  32. 32.
    Wang C, Yin S J, Meng B, Ma W L, Zhu L and Wu C Q. 2016. Analysis of the non-regular garbage sites in the region of Beijing, Tianjin and Hebei using remote sensing monitoring images. High Technology Letters, 26(Z1): 799-807
  33. 33.
    Wang J Q, Li J S, Zhou H C and Zhang X. 2019. Typical element extraction method of remote sensing image based on Deeplabv3+ and CRF. Computer Engineering, 45(10):206-265+271
  34. 34.
    Wu J G. 2000. Landscape ecology pattern process, scale and hierarchy. Beijing: High Education Press
  35. 35.
    Wu Q, Zhong R F, Zhao W J, Song K and Du L M. 2019. Land-cover classification using GF-2 images and airborne lidar data based on random forest. International Journal of Remote Sensing, 40(5-6): 2410-2426
  36. 36.
    Yan W Y, Mahendrarajah P, Shaker A, Faisal K, Luong R and Al-Ahmad M. 2014. Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites. Environmental Monitoring and Assessment, 186(12): 8161-8173
  37. 37.
    Yi Z W, Jia L and Chen Q T. 2020. Crop classification using multi-temporal Sentinel-2 data in the Shiyang River basin of China. Remote Sensing, 12(24):4052
  38. 38.
    Zhang J X, Gu H Y, Yang Y, Zhang H and Li H T. 2021. Research process and trend of high-resolution remote sensing imagery intelligent interpretation. National Remote Sensing Bulletin, 25(11): 2198-2210
  39. 39.
    Zhang T X, Su J Y, Xu Z Y, Luo Y L and Li J Y. 2021. Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Applied Sciences, 11(2): 543
  40. 40.
    Zhang X Q. 2020. Object oriented remote sensing information extraction of construction waste in wuhou district, Chengdu. Chengdu: Chengdu University of Technology
  41. 41.
    Zhang Z X, Liu Q J and Wang Y H. 2018. Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5):749-753
  42. 42.
    Zhu Y N, Gao T, Wang S D, Zhou L and Du M Y. 2021. Automatic recognition method of construction waste based on transfer learning and retraining model and high-score remote sensing data. Remote Sensing Technology and Application, 36(2): 314-323

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