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机器学习技术在风景园林中的应用进展与展望

赵晶 陈然 郝慧超 邵壮

赵晶, 陈然, 郝慧超, 邵壮. 机器学习技术在风景园林中的应用进展与展望[J]. 北京林业大学学报. doi: 10.12171/j.1000-1522.20200313
引用本文: 赵晶, 陈然, 郝慧超, 邵壮. 机器学习技术在风景园林中的应用进展与展望[J]. 北京林业大学学报. doi: 10.12171/j.1000-1522.20200313
Zhao Jing, Chen Ran, Hao Huichao, Shao Zhuang. Application progress and prospect of machine learning technology in landscape architecture[J]. Journal of Beijing Forestry University. doi: 10.12171/j.1000-1522.20200313
Citation: Zhao Jing, Chen Ran, Hao Huichao, Shao Zhuang. Application progress and prospect of machine learning technology in landscape architecture[J]. Journal of Beijing Forestry University. doi: 10.12171/j.1000-1522.20200313

机器学习技术在风景园林中的应用进展与展望

doi: 10.12171/j.1000-1522.20200313
基金项目: 中央高校基本科研业务费专项(2018RW21),教育部人文社会科学研究青年基金项目(18YJC760146)
详细信息
    作者简介:

    赵晶,博士,副教授。主要研究方向:风景园林历史与理论、风景园林规划设计、绿色生态网络与生态修复、风景园林遗产保护与信息化呈现。Email:zhaojing850120@163.com 地址:100083北京市海淀区清华东路35号北京林业大学园林学院

  • 中图分类号: S731

Application progress and prospect of machine learning technology in landscape architecture

  • 摘要:   目的  随着风景园林数字化技术的应用发展,大数据技术和人工智能技术逐渐被应用到风景园林专业,解决了众多问题,机器学习技术同时作为大数据处理工具和人工智能核心技术之一也逐渐成为风景园林研究的热门话题。  方法  本文对近年来国内外的相关实践进行系统地总结和分析,先进行应用背景的介绍,分析机器学习在风景园林应用的适用性;再基于机器学习在风景园林中解决问题角度的不同,从场地信息提取,景观分析与评价和基于深度学习的方案自生成系统三个应用角度,对国内外已有实验的方法过程进行举例分析。  结果  最后基于对机器学习技术在风景园林应用的不同领域间的关系、同领域间的不同应用的关系的分析对未来的趋势进行了展望  结论  从技术层面上,构建基于多种数据的综合性景观评价模型,景观分析模型是未来较有前景的研究方向;从应用层面上,随着多种智能化技术的整合,多源数据的整合,结合实际规划设计项目构建基于多种人工智能方法的数字化规划设计方法是机器学习在风景园林应用领域未来的重要趋势。

     

  • 图  1  人工智能与机器学习概念图解

    Figure  1.  Concept diagram of artificial intelligence and machine learning

    图  2  机器学习技术在景观评价(上)及景观格局分析(下)中应用的流程图解

    Figure  2.  Diagram of the application of machine learning in landscape evaluation (top) and landscape pattern analysis (bottom)

    图  3  车通团队在实验中计算出不同年份间不同因子对城市扩张的不同程度影响

    引自参考文献[19] Cited from reference [19]

    Figure  3.  Influence of different factors on urban expansion to varied degrees in different years calculated by Chetong team in the experiment

    图  4  车通团队在实验中计算出不同阈值间的因子与城市扩张程度的关系

                          引自参考文献[19] Cited from reference [19]

    Figure  4.  Relationship between the factors among different thresholds and the degree of urban expansion in the experiment calculated by Chetong team

    图  5  李小江团队绘制的城市绿视率地图

         引自参考文献[30] Cited from reference [30]

    Figure  5.  Urban green visibility map drawn by Lixiaojiang team

    图  6  Zhang团队借助segnet图像分割工具将“Place Pulse 2.0”数据集图像进行分割

                        引自参考文献[40] Cited from reference [40]

    Figure  6.  Zhang team segmented the images of the “Place Pulse 2.0” dataset with the segnet image segmentation tool

    图  7  Zhang团队根据“Place Pulse 2.0”数据集构建的评价模型

                         引自参考文献[40] Cited from reference [40]

    Figure  7.  Evaluation model constructed by Zhang team based on “Place Pulse 2.0” dataset

    图  8  邓宁团队根据利用DeepSentiBank工具将网络社交图片进行关键词提取

         引自参考文献[43] Cited from reference [43]

    Figure  8.  Dengning team extracted keywords from onlinesocial images using DeepSentibank

    图  9  将城市肌理数据进行机器学习训练

         引自参考文献[44] Cited from reference [44]

    Figure  9.  Using urban texture data for machine learning training

    图  10  划定研究区域生成城市肌理

                         引自参考文献[44] Cited from reference [44]

    Figure  10.  Delimiting the research areas to generate urban fabric

    图  11  深度学习自生成系统框架(上)和生成结果(下)

                         引自参考文献[45] Cited from reference [45]

    Figure  11.  Self-generated system framework based on deep learning (top) and generated results (bottom)

    图  12  基于深度学习的植物配置方案生成框架

                         引自参考文献[46] Cited from reference [46]

    Figure  12.  Generating framework of plant configuration scheme based on deep learning

    图  13  环境模拟和环境指标参数约束下的案例生成过程图解

                         引自参考文献[46] Cited from reference [46]

    Figure  13.  Illustrating the case generation process under the constraint of environmental simulation and parameters of environmental indicators

    图  14  种植理论的定量解释和评价体系构建

                         引自参考文献[46] Cited from reference [46]

    Figure  14.  Quantitative interpretation of planting theory and construction of evaluation system

    图  15  最终结果——自动生成的种植设计模型(上)、种植设计平面图(中)、苗木表(下)

                        引自参考文献[46] Cited from reference [46]

    Figure  15.  Final results: automatically generated planting design model (top), planting design plan (middle), nursery stock table (bottom)

    图  16  基于深度学习的自然空间地理地形随机自生成图解

                         引自参考文献[33] Cited from reference [33]

    Figure  16.  Random self-generated diagram of natural spatial geographic terrain based on deep learning

    图  17  基于深度学习技术生成的风景园林平面图

    引自清华大学教育基金会http://www.tuef.tsinghua.edu.cn/info/jzgs/4235 Cited from Tsinghua University Education Foundation http://www.tuef.tsinghua.edu.cn/info/jzgs/4235

    Figure  17.  Landscape architecture plan generated based on deep learning technology

    图  18  根据场地条件自动生成规划平面图

    引自数字设计公司官网https://www.xkool.ai/Cited from digital design company’s official website https://www.xkool.ai/

    Figure  18.  Automatic generation of planningplan according to site conditions

    表  1  机器学习算法分类

    Table  1.   Classification of machine learning algorithm

    学习方式
    Learning method
    算法类型
    Algorithm type
    算法
    Algorithm
    监督学习
    Supervised learning
    回归
    Regression
    岭/套索回归、线性回归、多项式回归
    Ridge/lasso regression, linear regression, polynomial regression
    分类
    Classification
    最邻近算法、朴素贝叶斯、逻辑回归、支持向量机、决策树、分类回归树、最大熵模型、期望最大化算法、极大似然估计、条件随机场、隐马尔可夫模型、马尔可夫模型
    k-nearest neighbor (K-NN), Naïve Bayes, logistic regression,support vector machine (SVM), decision trees (DT), classification and regression tree (CART), maximum entropy model, expectation maximum, maximum likelihood estimation, CRF, hidden Markov Model (HMM), Markov Model
    无监督学习
    Unsupervised learning
    聚类
    Clustering
    模糊C均值聚类、均值偏移、K均值聚类、密度聚类、层次聚类
    Fuzzy C-means, means shift, K-means, DBSCAN, agglomerative
    关联规则学习
    Association rule learning
    关联分析算法、频繁项集算法
    FP growth, apriori
    降维
    Dimensionality reduction
    判别分析、t分布邻域嵌入、主成分分析法、潜在语义分析、奇异值分解
    Linear discriminant analysis (LDA), t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), latent semantic analysis (LSA), singular value decomposition (SVD)
    神经网络与深度学习
    Neural networks and deep learning
    卷积神经网络
    Convolutional neural networks (CNN)
    动态卷积神经网络
    Dynamic convolution neural network (DCNN)
    循环神经网络
    Recurrent neural networks (RNN)
    结构化合并树、长短期记忆网络、门控循环单元网络
    Log-structured merge tree (LSM), long short-term memory (LSTM),gated recurrent unit (GRU)
    生成对抗网络
    Generative adversarial networks (GAN)
    生成对抗网络
    Generative adversarial networks (GAN)
    BP神经网络
    Back propagation neural network (BPNN)
    BP神经网络
    Back propagation neural network (BPNN)
    自编码器
    Autoencoders
    序列到序列模型
    Seq2seq
    感知机
    Perceptrons
    感知机
    Perceptrons
    集成学习
    Ensemble learning
    堆叠法
    Stacking
    堆叠法
    Stacking
    自助聚合
    Bagging
    随机森林
    Random forest
    提升法
    Boosting
    极端梯度提升、轻量梯度提升机、分类梯度提升、算自适应增强算法
    Extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), CatBoost, adaboost
    强化学习
    Reinforcement learning
    异步优势行动者评论家算法、时序差分在线控制算法、深度Q网络、Q网络
    Asynchronous advantage Actor-Critic (A3C), SARSA, deep Q-learning (DQN), Q-learning
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-10-20
  • 修回日期:  2021-03-20
  • 网络出版日期:  2021-09-24

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