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Research on garden landscape reconstruction based on geographic information system under the background of deep learning

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Abstract

In the context of deep learning, this paper combines the arbitration mechanism to propose a GAN (Arbi-DCGAN) model based on the arbitration mechanism. First, the network structure of the proposed improved algorithm is composed of generator, discriminator and arbitrator. Then, the generator and the discriminator will conduct adversarial training according to the training plan and strengthen the ability of generating images and distinguishing the authenticity of the images according to the characteristics learned from the data set. Secondly, the arbitrator is composed of the generator, discriminator and measurement score computation module that have undergone the previous adversarial training. The arbitrator will feed back the results of the metric generator and discriminator adversarial training to the training plan. Finally, a winning limit is added to the network structure to improve the stability of model training, and the Circle loss function is used to replace the BCE loss function, which makes the model optimization process more flexible and the convergence state more clear. On the basis of geographic information system, this paper uses 325 meticulously annotated sample plans to establish a data set for deep learning, and trains the Arbi-DCGAN model to achieve the task of extracting land plots of different land types in the plan, as well as from the plane color block map to the color texture. The rendering and generation of the map complete the reconstruction task of the garden landscape. In addition, we further evaluate the results of the model's reconstruction of the garden landscape from the aspects of image quality, correct standardization and color expression. The training model has the potential to be applied to land type analysis and plane rendering in landscape architecture cases, helping designers improve the efficiency of analysis and drawing.

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References

  • Alqahtani H, Kavakli-Thorne M, Kumar G (2021) Applications of generative adversarial networks (gans): An updated review[J]. Arch Comput Methods Eng 28(2):525–552

    Article  Google Scholar 

  • Chen Y, Lai YK, Liu YJ (2018) Cartoongan: generative adversarial networks for photo cartoonization[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 9465–9474

  • Creswell A, White T, Dumoulin V et al (2018) Generative adversarial networks: An overview[J]. IEEE Signal Process Magaz 35(1):53–65

    Article  Google Scholar 

  • Gong X, Chang S, Jiang Y, et al (2019) Autogan: Neural architecture search for generative adversarial networks[C]. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 3224–3234.

  • Gupta A, Johnson J, Fei-Fei L, et al (2018) Social gan: Socially acceptable trajectories with generative adversarial networks[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2255–2264.

  • He W (2021). Research on outdoor garden scene reconstruction based on PMVS Algorithm. Sci Program

  • Helbich M, Yao Y, Liu Y, Zhang J, Liu P, Wang R (2019) Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environ Int 126:107–117

    Article  Google Scholar 

  • Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks[C]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 4401–4410.

  • Khattak GR, Vallecorsa S, Carminati F et al (2021) High energy physics calorimeter detector simulation using generative adversarial networks with domain related constraints[J]. IEEE Access 9:108899–108911

    Article  Google Scholar 

  • Li R, Xu D (2020) Distribution of landscape architecture based on 3D images and virtual reality rationality study[J]. IEEE Access 8:140161–140170

    Article  Google Scholar 

  • Liang J, Jiang L, Murphy K, et al (2020) The garden of forking paths: Towards multi-future trajectory prediction[C]. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 10508–10518

  • Liu X, Wang X, Wright G, Cheng JC, Li X, Liu R (2017) A state-of-the-art review on the integration of Building Information Modeling (BIM) and Geographic Information System (GIS). ISPRS Int J Geo-Inf 6(2):53

    Article  Google Scholar 

  • Liu L, Zhang Y, Deng J et al (2021) Dynamically grown generative adversarial networks. Proc AAAI Conf Artif Intell 35(10):8680–8687

    Google Scholar 

  • Liu Y, Fang C, Yang Z, et al (2021) Exploration on machine learning layout generation of Chinese private garden in southern Yangtze. The international conference on computational design and robotic fabrication. Springer, Singapore, pp 35–44. [17]

  • Lu X, Wang X, Wu R (2020) Urban Garden landscape design based on VR technology and internet of things system[J]. Microprocess Microsyst, 103432

  • Major J, Smith C, Mackay R (2018) Reconstructing landscape: Archaeological investigations of the royal exhibition buildings western Forecourt, Melbourne[J]. Int J Histor Archaeol 22(1):43–66

    Article  Google Scholar 

  • Mutasa S, Varada S, Goel A et al (2020) Advanced deep learning techniques applied to automated femoral neck fracture detection and classification[J]. J Digital Imag 33(5):1209–1217

    Article  Google Scholar 

  • Pamintuan KRS, Sanchez KM (2019) Power generation in a plant-microbial fuel cell assembly with graphite and stainless steel electrodes growing Vigna Radiata[C]//IOP conference series: materials science and engineering. IOP Pub 703(1):012037

    Google Scholar 

  • Pouya S, Behbahani HI (2017) Assessment of the visual landscape quality based on the subjectivist paradigm to design the memorial garden[J]. Turkish J Forest 18(3):171–177

    Google Scholar 

  • Qin P, Huang Y, Gan Y (2020) Protection and renewal strategy of historical remains landscape in urban reconstruction–a case study of guilin ring water system[c]//e3s web of conferences. EDP Sci 194:05042

    Google Scholar 

  • Rossi L, Paolanti M, Pierdicca R et al (2021) Human trajectory prediction and generation using LSTM models and GANs[J]. Pattern Recogn 120:108136

    Article  Google Scholar 

  • Seber D, Vallvé M, Sandvol E, Steer D, Barazangi M (1997) Middle East tectonics: applications of geographic information systems (GIS). GSA Today 7(2):1–6

    Google Scholar 

  • Shin YR, Yang J, Lee YH (2021) Deep generative adversarial networks: applications in musculoskeletal imaging[J]. Radiol Artif Intel 3(3):e200157

    Article  Google Scholar 

  • Wang H (2019) Landscape design of coastal area based on virtual reality technology and intelligent algorithm[J]. J Intell Fuzzy Syst 37(5):5955–5963

    Article  Google Scholar 

  • Wang X, Yu K, Wu S, et al (2018) Esrgan: Enhanced super-resolution generative adversarial networks[C]. In: Proceedings of the European conference on computer vision (ECCV) workshops. 0–0

  • Wei Z, Sun S, Ji X (2019) The inspiration of rainwater utilization of foreign sponge campus landscape planning for Beijing[C]//IOP conference series: earth and environmental science. IOP Publ 227(5):052019

    Google Scholar 

  • Xu N, Luo J, Zuo J, Hu X, Dong J, Wu T, Wu S, Liu H (2020) Accurate suitability evaluation of large-scale roof greening based on RS and GIS methods. Sustainability 12(11):4375

    Article  Google Scholar 

  • Xu X, Zhang Y, Liden L, et al (2019) Unsupervised dialogue spectrum generation for log dialogue ranking[C]. In: Proceedings of the 20th annual SIGdial meeting on discourse and dialogue. pp 143–154

  • Yang Z, Chen Y, Le Z et al (2021) GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks[J]. Neural Comput Appl 33(11):6133–6145

    Article  Google Scholar 

  • Yu H, Qian T, Liang Y et al (2020) AGTR: Adversarial generation of target review for rating prediction[J]. Data Sci Eng 5(4):346–359

    Article  Google Scholar 

  • Yu H, Qian T, Liang Y, et al (2020) Adversarial generation of target review for rating prediction[C]. In: International conference on database systems for advanced applications. Springer, Cham, pp 73–89

  • Zhang H, Xu T, Li H et al (2018) Stackgan++: Realistic image synthesis with stacked generative adversarial networks[J]. IEEE Trans Pattern Anal Mach Intell 41(8):1947–1962

    Article  Google Scholar 

  • Zhang W, Liu Y, Dong C, et al (2019) Ranksrgan: Generative adversarial networks with ranker for image super-resolution[C]. In: Proceedings of the IEEE/CVF international conference on computer vision. pp 3096–3105

  • Zhang Y, Zhou B, Cai X et al (2021) Missing value imputation in multivariate time series with end-to-end generative adversarial networks[J]. Inf Sci 551:67–82

    Article  Google Scholar 

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Authors and Affiliations

  1. Apparel & Art Desigh College, Xi’an Polytechnic University, Xi’an, 710048, Shaanxi, China

    Ying Cui

Authors
  1. Ying Cui
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Contributions

YC, is responsible for designing the framework, analyzing the performance, validating the results, and writing the article.

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Correspondence to Ying Cui.

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Authors do not have any conflicts.

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This paper complies with the ethical standards of research and methodology.

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Additional information

Dr. Nasir Saleem (GUEST EDITOR) / Dr. Michael Nones (CO-EDITOR-IN-CHIEF).

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Cite this article

Cui, Y. Research on garden landscape reconstruction based on geographic information system under the background of deep learning. Acta Geophys. 71, 1491–1513 (2023). https://doi.org/10.1007/s11600-022-00831-6

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  • DOI: https://doi.org/10.1007/s11600-022-00831-6

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