|Table of Contents|

Application Analysis and Future Prospect of Landscape Architecture Images and Graphics in Deep Learning(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年02期
Page:
44-53
Research Field:
地理学
Publishing date:

Info

Title:
Application Analysis and Future Prospect of Landscape Architecture Images and Graphics in Deep Learning
Author(s):
Liu GuanShao JizhongWang YuqiZhang XueyinLyu Xinbei
(College of Horticulture and Forestry Sciences,Huazhong Agricultural University,Wuhan 430070,China)
Keywords:
deep learninglandscape images and graphicsimage recognitionimage generationgraph prediction
PACS:
TU984
DOI:
10.3969/j.issn.1001-4616.2024.02.006
Abstract:
New approaches and efficient methods for the collection,analysis,and forecasting of big sample data in landscape architecture research,as well as the quick creation of landscape design drawings,are made possible by the widespread use of deep learning to handle image and graphic data. This paper took landscape architecture images and graphics as its object,analyzed the types of landscape architecture images and graphics,and explored their application ways in deep learning technology. Starting from three aspects of image recognition,image generation,and graphic prediction,this paper analyzed and summarized relevant literatures at home and abroad,combed the application progress,and proposed that the future development trend can focus on the transformation from deep learning to transfer learning,the integration of artificial intelligence and creative thinking,the combination of material attributes and non-material attributes,and emphasized the significance of deep learning technology in analyzing the space environment of location,automatically generating landscape representation maps,rapid intelligent modeling,scientific prediction of crowd behavior preferences,and so on,by processing landscape architecture images and graphics. Its application in landscape architecture can effectively promote the intelligent development of this profession.

References:

[1]王宇昊,何彧,王铸. 基于深度学习的文本到图像生成方法综述[J]. 计算机工程与应用,2022,58(10):50-67.
[2]吴桥. 平面广告设计中计算机图形图像的应用[J]. 工业设计,2017(8):115-116.
[3]强斌. 浅谈图像学在视频编辑软件中的应用[J]. 江苏科技信息,2015(25):41-42.
[4]DENG M Y,YANG W,CHEN C,et al. Exploring associations between streetscape factors and crime behaviors using Google street view images[J]. Frontiers of computer science,2022,16(4):164316.
[5]叶宇,仲腾,钟秀明. 城市尺度下的建筑色彩定量化测度:基于街景数据与机器学习的人本视角分析[J]. 住宅科技,2019,39(5):7-12.
[6]LARKIN A,GU X,CHEN L Z,et al. Predicting perceptions of the built environment using GIS,satellite and street view image approaches[J]. Landscape and urban planning,2021,216:104257.
[7]KANG Y,CHO N,YOON J,et al. Transfer learning of a deep learning model for exploring tourists' urban image using geotagged photos[J]. ISPRS international journal of geo-information,2021,10(3):137.
[8]KOYLU C,ZHAO C,SHAO W. Deep neural networks and kernel density estimation for detecting human activity patterns from geo-tagged images:a case study of birdwatching on Flickr[J]. ISPRS international journal of geo-information,2019,8(1):45.
[9]袁静文,武辰,杜博,等. 高分五号高光谱遥感影像的城市土地利用景观格局分析[J]. 遥感学报,2020,24(4):465-478.
[10]陈嘉浩,邢汉发,陈相龙. 融合级联CRFs和U-Net深度学习模型的遥感影像建筑物自动提取[J]. 华南师范大学学报(自然科学版),2022,54(1):70-78.
[11]张凌峰. 基于深度学习的激光点云环境感知[D]. 北京:北方工业大学,2021.
[12]索传哲. 基于深度学习的大场景激光点云环境识别研究[D]. 南京:东南大学,2021.
[13]吴韶集,胡一可. 基于深度学习的公共空间人群行为可视化研究:以天津大学卫津路校区为例[J]. 风景园林,2022,29(2):106-111.
[14]YANG H Q,ZHANG X M,LI Z H,et al. Region-level traffic prediction based on temporal multi-spatial dependence graph convolutional network from GPS data[J]. Remote sensing,2022,14(2):303.
[15]YI S,LI H S,WANG X G. Pedestrian behavior understanding and prediction with deep neural networks[J]. European conference on computer vision,2016,9905:263-279.
[16]张帆,刘瑜.街景影像:基于人工智能的方法与应用[J]. 遥感学报,2021,25(5):1043-1054.
[17]KIM S B,KIM D Y,WISE K. The effect of searching and surfing on recognition of destination images on Facebook pages[J]. Computers in human behavior,2014,30:813-823.
[18]CAI G C,LEE K,LEE I. Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos[J]. Expert systems with applications,2018,94:32-40.
[19]赵晶,曹易. 风景园林研究中的人工智能方法综述[J]. 中国园林,2020,36(5):82-87.
[20]KIM E S,YUN S H,PARK C Y,et al. Estimation of mean radiant temperature in urban canyons using Google street view:a case study on Seoul[J]. Remote sensing,2022,14(2):260.
[21]YE N Q,WANG B W,KITA M,et al. Urban commerce distribution analysis based on street view and deep learning[J]. IEEE access,2019,7:162841-162849.
[22]MIDDEL A,LUKASCZYK J,ZAKRZEWSKI S,et al. Urban form and composition of street canyons:a human-centric big data and deep learning approach[J]. Landscape and urban planning,2019,183:122-132.
[23]ZHONG T,YE C,WANG Z,et al. City-scale mapping of urban facade color using street-view imagery[J]. Remote sensing,2021,13(8):1591.
[24]邓宁,刘耀芳,牛宇,等. 不同来源地旅游者对北京目的地形象感知差异:基于深度学习的Flickr图片分析[J]. 资源科学,2019,41(3):416-429.
[25]ZHANG K,CHEN Y,LI C L. Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model:the case of Beijing[J]. Tourism management,2019,75:595-608.
[26]李亚飞,董红斌. 基于卷积神经网络的遥感图像分类研究[J]. 智能系统学报,2018,13(4):550-556.
[27]CHENG G,XIE X X,HAN J W,et al. Remote sensing image scene classification meets deep learning:challenges,methods,benchmarks,and opportunities[J]. IEEE journal of selected topics in applied earth observations and remote sensing,2020,13:3735-3756.
[28]张铭飞,高国伟,胡敬芳,等. 基于卷积神经网络的遥感图像水体提取[J]. 传感器与微系统2022,41(1):72-74.
[29]XU K J,HUANG H,DENG P F,et al. Two-stream feature aggregation deep neural network for scene classification of remote sensing images[J]. Information sciences,2020,539:250-268.
[30]TOMBE R,VIRIRI S. Adaptive deep co-occurrence feature learning based on classifier-fusion for remote sensing scene classification[J]. IEEE journal of selected topics in applied earth observations and remote sensing,2020,14:155-164.
[31]范鑫,胡昌苗,霍连志. 耦合多源地理数据的多分辨率遥感影像场景分类方法研究[J]. 无线电工程,2021,51(12):1449-1460.
[32]QI C R,SU H,MO K C,et al. PointNet:deep learning on point sets for 3D classification and segmentation[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu,USA:IEEE,2017:77-85.
[33]HAN X,DONG Z,YANG B S. A point-based deep learning network for semantic segmentation of MLS point clouds[J]. ISPRS journal of photogrammetry and remote sensing,2021,175:199-214.
[34]HU R Z,HUANG Z Y,TANG Y H,et al. Graph2Plan:learning floorplan generation from layout graphs[J]. ACM transactions on graphics,2020,39(4):118.
[35]苏奇. 基于深度学习的地形生成方法研究[D]. 西安:西安电子科技大学,2020.
[36]孙澄,丛欣宇,韩昀松. 基于 CGAN 的居住区强排方案生成设计方法[J]. 哈尔滨工业大学学报,2021,53(2):111-121.
[37]林文强. 基于深度学习的小学校园设计布局自动生成研究[D]. 广州:华南理工大学,2020.
[38]YE Y,ZHUANG Y,ZHANG L,et al. Designing urban spatial vitality from morphological perspective:a study based on quantified urban morphology and activities' testing[J]. International urban plan,2016,31:26-33.
[39]KIM Y L. Seoul's Wi-Fi hotspots:Wi-Fi access points as an indicator of urban vitality[J]. Computers,environment and urban systems,2018,72:13-24.
[40]OOSTERLINCK D,BENOIT D F,BAECKE P,et al. Bluetooth tracking of humans in an indoor environment:an application to shopping mall visits[J]. Applied geography,2017,78:55-65.
[41]GUO D Y,WANG J,CUI Y,et al. SiamCAR:siamese fully convolutional classification and regression for visual tracking[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle,USA:IEEE,2020:6268-6276.
[42]WANG Q,ZHANG L,BERTINETTO L,et al. Fast online object tracking and segmentation:a unifying approach[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach,USA:IEEE,2019:1328-1338.
[43]李瀚,刘坤华,刘嘉杰,等. 实时视觉目标跟踪与视频对象分割多任务框架[J]. 中国图象图形学报,2021,26(1):101-112.
[44]赛斌,曹自强,谭跃进,等. 基于目标跟踪与轨迹聚类的行人移动数据挖掘方法研究[J]. 系统工程理论与实践,2021,41(1):231-239.
[45]AITHAL B H,DAS S K,SUBRAHMANYA P P. Urban 3D structure reconstruction through a generative adversarial network model[J]. Arabian journal for science and engineering,2020,45(12):10731-10741.
[46]KIM S,KIM D,CHOI S,et al. CityCraft:3D virtual city creation from a single image[J]. Visual computer,2020,36(5):911-924.
[47]黄骞,史洪芳,于洪斌. 基于实景三维的美丽乡村智能规划协同平台[J]. 公路,2019,64(4):233-238.
[48]ESLAMI S M A,REZENDE D J,BESSE F,et al. Neural scene representation and rendering[J]. Science,2018,360(6394):1204-1210.
[49]KINGMA D P,WELLING M. Auto-encoding variational bayes[DB/OL].[2019-07-02]. https://doi.org/10.48550/arXiv.1312.6114.
[50]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems.Montreal,Canada:MIT Press,2014:2672-2680.
[51]吴劲,陈树沛,杨庆,等. 基于图神经网络的用户轨迹分类[J]. 电子科技大学学报,2021,50(5):734-740.
[52]朱光辉,王喜文. ChatGPT的运行模式、关键技术及未来图景[J]. 新疆师范大学学报(哲学社会科学版),2023,44(4):113-122.
[53]王德祥,王建波. 新一代人工智能对数字经济的影响:以ChatGPT为例[J]. 特区实践与理论,2023(2):34-39.
[54]荆林波,杨征宇. 聊天机器人(ChatGPT)的溯源及展望[J]. 财经智库,2023,8(1):5-36.
[55]冯志伟,张灯柯,饶高琦. 从图灵测试到ChatGPT:人机对话的里程碑及启示[J]. 语言战略研究,2023,8(2):20-24.
[56]WU T Y,HE S Z,LIU J P,et al. A brief overview of ChatGPT:the history,status quo and potential future development[J]. IEEE/CAA journal of automatica sinica,2023,10(5):1122-1136.

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Last Update: 2024-06-15