[1]刘 冠,邵继中,王宇琪,等.风景园林图像与图形在深度学习中的应用分析及未来展望[J].南京师大学报(自然科学版),2024,(02):44-53.[doi:10.3969/j.issn.1001-4616.2024.02.006]
 Liu Guan,Shao Jizhong,Wang Yuqi,et al.Application Analysis and Future Prospect of Landscape Architecture Images and Graphics in Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2024,(02):44-53.[doi:10.3969/j.issn.1001-4616.2024.02.006]
点击复制

风景园林图像与图形在深度学习中的应用分析及未来展望()
分享到:

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

卷:
期数:
2024年02期
页码:
44-53
栏目:
地理学
出版日期:
2024-06-15

文章信息/Info

Title:
Application Analysis and Future Prospect of Landscape Architecture Images and Graphics in Deep Learning
文章编号:
1001-4616(2024)02-0044-10
作者:
刘 冠邵继中王宇琪张雪茵吕欣蓓
(华中农业大学园艺林学学院,湖北 武汉 430070)
Author(s):
Liu GuanShao JizhongWang YuqiZhang XueyinLyu Xinbei
(College of Horticulture and Forestry Sciences,Huazhong Agricultural University,Wuhan 430070,China)
关键词:
深度学习风景园林图像与图形图像识别图像生成图形预测
Keywords:
deep learninglandscape images and graphicsimage recognitionimage generationgraph prediction
分类号:
TU984
DOI:
10.3969/j.issn.1001-4616.2024.02.006
文献标志码:
A
摘要:
深度学习处理图像与图形数据的广泛应用,为风景园林研究的大样本数据获取、分析、预测,以及景观设计图的快速生成提供了新的解决思路与有效途径. 本文以风景园林图像与图形为研究对象,剖析风景园林图像与图形的类型,探究其在深度学习技术中的应用途径,分别从图像识别、图像生成、图形预测三个方面出发,对国内外的相关文献进行分析总结,梳理应用进展,提出未来发展趋势可聚焦深度学习向迁移学习的转变、人工智能与创意思维的融合、物质属性与非物质属性的结合,并强调深度学习技术通过处理风景园林图像与图形在分析场所空间环境、自动生成景观表现图、快速智能化建模、科学预判人群行为偏好等方面发挥着巨大的作用,将其应用于风景园林领域,能够有效推动本学科的智慧化发展.
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.

参考文献/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.

相似文献/References:

[1]郑德鹏,杜吉祥,翟传敏.基于深度学习MPCANet的年龄估计[J].南京师大学报(自然科学版),2017,40(01):20.[doi:10.3969/j.issn.1001-4616.2017.01.004]
 Zheng Depeng,Du Jixiang,Zhai Chuanmin.Age Estimation Based on Deep Learning MPCANet[J].Journal of Nanjing Normal University(Natural Science Edition),2017,40(02):20.[doi:10.3969/j.issn.1001-4616.2017.01.004]
[2]朱 繁,王洪元,张 继.基于深度学习的行人重识别研究综述[J].南京师大学报(自然科学版),2018,41(04):93.[doi:10.3969/j.issn.1001-4616.2018.04.015]
 Zhu Fan,Wang Hongyuan,Zhang Ji.A Survey of Person Re-identification Based on Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2018,41(02):93.[doi:10.3969/j.issn.1001-4616.2018.04.015]
[3]孙茹君,张鲁飞.基于动态指导的深度学习模型稀疏化执行方法[J].南京师大学报(自然科学版),2019,42(03):11.[doi:10.3969/j.issn.1001-4616.2019.03.002]
 Sun Rujun,Zhang Lufei.Dynamic Sparse Method for Deep Learning Execution[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(02):11.[doi:10.3969/j.issn.1001-4616.2019.03.002]
[4]赵文芳,林润生,唐 伟,等.基于深度学习的PM2.5短期预测模型[J].南京师大学报(自然科学版),2019,42(03):32.[doi:10.3969/j.issn.1001-4616.2019.03.005]
 Zhao Wenfang,Lin Runsheng,Tang Wei,et al.Forecasting Model of Short-Term PM2.5 ConcentrationBased on Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(02):32.[doi:10.3969/j.issn.1001-4616.2019.03.005]
[5]张新峰,闫昆鹏,赵 珣.基于双向LSTM的手写文字识别技术研究[J].南京师大学报(自然科学版),2019,42(03):58.[doi:10.3969/j.issn.1001-4616.2019.03.008]
 Zhang Xinfeng,Yan Kunpeng,Zhao Xun.Handwriting Chinese Text Recognition Using BiLSTM Network[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(02):58.[doi:10.3969/j.issn.1001-4616.2019.03.008]
[6]贾玉福,胡胜红,刘文平,等.使用条件生成对抗网络的自然图像增强方法[J].南京师大学报(自然科学版),2019,42(03):88.[doi:10.3969/j.issn.1001-4616.2019.03.012]
 Jia Yufu,Hu Shenghong,Liu Wenping,et al.Wild Image Enhancement with Conditional Generative Adversarial Network[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(02):88.[doi:10.3969/j.issn.1001-4616.2019.03.012]
[7]汤 凯,何 庆,赵 群,等.基于改进的深度残差网络的图像识别[J].南京师大学报(自然科学版),2019,42(03):115.[doi:10.3969/j.issn.1001-4616.2019.03.015]
 Tang Kai,He Qing,Zhao Qun,et al.Image Recognition Based on Improved Deep Neural Network[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(02):115.[doi:10.3969/j.issn.1001-4616.2019.03.015]
[8]汪 晨,张辉辉,乐继旺,等.基于深度学习和遥感影像的松材线虫病疫松树目标检测[J].南京师大学报(自然科学版),2021,44(03):84.[doi:10.3969/j.issn.1001-4616.2021.03.013]
 Wang Chen,Zhang Huihui,Le Jiwang,et al.Object Detection to the Pine Trees Affected by Pine Wilt Diseasein Remote Sensing Images Using Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(02):84.[doi:10.3969/j.issn.1001-4616.2021.03.013]
[9]韩 悦,张永寿,郭依廷,等.乳腺癌腋窝淋巴结超声图像分割算法研究[J].南京师大学报(自然科学版),2021,44(04):122.[doi:10.3969/j.issn.1001-4616.2021.04.016]
 Han Yue,Zhang Yongshou,Guo Yiting,et al.Research on Ultrasound Image Segmentation Algorithm forAxillary Lymph Node with Breast Cancer[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(02):122.[doi:10.3969/j.issn.1001-4616.2021.04.016]
[10]闫靖昆,黄毓贤,秦伟森,等.棉田复杂背景下棉花黄萎病病斑分割算法研究[J].南京师大学报(自然科学版),2021,44(04):127.[doi:10.3969/j.issn.1001-4616.2021.04.017]
 Yan Jingkun,Huang Yuxian,Qin Weisen,et al.Study on Segmentation Algorithm of Cotton Verticillium WiltDisease Spot in Cotton Field Under Complex Background[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(02):127.[doi:10.3969/j.issn.1001-4616.2021.04.017]

备注/Memo

备注/Memo:
收稿日期:2023-02-19.
基金项目:国家重点研发计划项目(2023YFC3807500)、住房和城乡建设部科学技术计划国际科技合作类项目(2022-H-001).
通讯作者:邵继中,博士后,教授,博士生导师,研究方向:城市规划与城市设计. E-mail:shao.j.z@hotmail.com
更新日期/Last Update: 2024-06-15