DEVELOPMENT AND VERIFICATION OF THE IMPRESSION DEDUCTION MODEL FOR CITY LANDSCAPE WITH DEEP LEARNING-Reference-Cited by-同舟云学术

DEVELOPMENT AND VERIFICATION OF THE IMPRESSION DEDUCTION MODEL FOR CITY LANDSCAPE WITH DEEP LEARNING

Published:2019 Issue:759 Volume:84 Page:1323-1331
ISSN:1340-4210
Container-title:Journal of Architecture and Planning (Transactions of AIJ)
language:en
Short-container-title:Transactions of AIJ

Author:

YAMADA Satoshi¹,ONO Kotaro²

Affiliation:

1. Dept. of Architecture and Urban Design, Ritsumeikan Univ.

2. Graduate School of Eng., Architecture and Urban Design Course, Ritsumeikan Univ.

Publisher

Architectural Institute of Japan

Subject

General Medicine,General Chemistry

Link

https://www.jstage.jst.go.jp/article/aija/84/759/84_1323/_pdf

Reference23 articles.

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2. 2) Satoshi YAMADA, Takeshi FUJI, Shinsaku Munemoto : Development and Verification of The Impression Deduction Model for Green Space with Ratio of Omnidirectional Visibility Green Space-Using Self-Organizing Map with ratio of omnidirectional visibility green space and impression evaluation experiment-, Journal of Achitecture and Planning (Transactions of AIJ), Vol. 81, No. 727, pp. 2083-2093, 2016.9(in Japanese)　山田悟史, 藤井健史, 宗本晋作 : 方位緑視率を用いた緑地環境に対する印象評価推定モデル作成と検証 - 全方位緑視率と印象評価実験をもとにした自己組織化マップを用いて -, 日本建築学会計画系論文集, 第81巻, 第727号, pp. 2083-2093, 2016.9

3. 3) Shinsaku Munemoto, Satoshi YAMADA : A Study on Probabilistic Model of Evaluation of Green Environment Based on Ratio Of Visible Green Using Bayesian Network, Journal of Achitecture and Planning (Transactions of AIJ), Vol. 80, No. 708, pp. 2411-2419, 2016.11(in Japanese)　宗本晋作, 山田悟史 : ベイジアンネットワークを用いた緑視率に基づく緑環境評価の確率モデルに関する研究, 日本建築学会計画系論文集, 第81巻, 第729号, pp. 2411-2419, 2016.11

4. 4) Lun Liu, Elisabete A. Silva, Chunyang Wu, Hui Wang, : A machine learning-based method for the large -scale evaluation of the qualities of the urban environment-, Computers, Environment and Urban Systems, Volume 65, pp. 113-125, 2017.06

5. 5) Stephen Law, Yao Shen, Chanuki Seresinhe, : An application of convolutional neural network in street image classification -the case study of london-. ACM GeoAI'17 Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic knowledge Discovery, pp5-9, 2017.11

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