TAKIZAWA Atsushi

写真a

Search Institutional Repository


Title

Professor

Laboratory location

Sugimoto Campus

Homepage URL

http://inhabitationsys.life.osaka-cu.ac.jp/

Degree 【 display / non-display

  • Kobe University -  Dr. Eng.

Research Areas 【 display / non-display

Computer system, Town planning/Architectural planning

Awards & Honors 【 display / non-display

  • CAADRIA 2014 Best Paper Award

    2014.05.16  

Current Career 【 display / non-display

  • Osaka City University   Graduate School of Human Life Science   Human Life Science Course   Professor  

Career 【 display / non-display

  • 2013.04
     
     

    Osaka City University  

  • 2007
    -
    2013

    Kyoto University  

  • 2003
    -
    2007

    Kyoto University  

  • 1997
    -
    2003

    Kobe University  

Graduate School 【 display / non-display

  •  
    -
    1997

    Kobe University  Graduate School, Division of National Science and Technology 

Graduating School 【 display / non-display

  •  
    -
    1995

    Kobe University   Faculty of Engineering   Department of Architecture

 

Published Papers 【 display / non-display

  • Deep learning model to reconstruct 3D cityscapes by generating depth maps from omnidirectional images and its application to visual preference prediction

    Takizawa Atsushi, Kinugawa Hina

    DESIGN SCIENCE  6 2020.11  [Refereed]  [Invited]

    DOI

  • Partitioning Vertical Evacuation Areas in Umeda Underground Mall to Minimize the Evacuation Completion Time

    Yamamoto Ryo, Takizawa Atsushi

    REVIEW OF SOCIONETWORK STRATEGIES  13 ( 2 ) 209 - 225 2019.10  [Refereed]

    DOI

  • The mixed evacuation problem

    Hanawa Yosuke, Higashikawa Yuya, Kamiyama Naoyuki, Katoh Naoki, Takizawa Atsushi

    JOURNAL OF COMBINATORIAL OPTIMIZATION  36 ( 4 ) 1299 - 1314 2018.11  [Refereed]

    DOI

  • 成約賃料の分散比最大化に基づくオフィスビルのグレード分類手法

    瀧澤 重志, 加藤 直樹

    公益社団法人 日本不動産学会 日本不動産学会誌  31 ( 1 ) 58 - 63 2018.06  [Refereed]

    DOI CiNii

  • 成約賃料の分散比最大化に基づくオフィスビルのグレード分類手法に関する研究

    瀧澤 重志, 池上 純代, 加藤 直樹

    日本建築学会 日本建築学会計画系論文集  81 ( 728 ) 2259 - 2268 2016  [Refereed]

     View Summary

    &nbsp;Since office buildings vary in location, size, facility, rent and so on, it is difficult to illustrate their market with the average of such indices; segmentation according to those building specifications (specs) is needed. As an example of segmentation, some real estate-related companies grade office buildings according to their specs. Although their standards differ from each other, a few companies use the height and age of buildings. Conversely, attributes such as location, gross floor area, base floor area and building age are commonly used even though their values still differ for each company. Moreover, the number of grades differs from company to company, with there being two to four grades. These standards of grades have monotonicity, with wider floor area, newer buildings, etc. being better. In foreign countries, real estate-related companies also grade office buildings and make market reports on the basis of the grade. For example, Building Owners Management Association, which is an organization of office building owners in the United States, sets three grades of buildings, as A, B and C, in descending order and defines the overall characteristics of each grade. This grade differs from that of Japan in that rent is considered to be one of the building specs. Standards of the office building grade evaluation are subjective, sensory, and differ from company to company in that one company might deem a building to be A grade and the other might deem the same building to be B grade.<br>&nbsp;With the above discussion as the backdrop, we propose a quantitative grading method for office buildings. This method optimizes the thresholds of each spec of office buildings so as to maximize the variance ratio of contracted rents of targeted office buildings for each grade. We formulate this problem as parametric mixed integer programming and validate the method with the data of office buildings located in 23 Tokyo wards in 2013 and 2014. In the experiments, we tested 11 combinations of building specs.<br>&nbsp;The results of the experiments support the following conclusions. Compared with the existing typical office building grades published by some companies, the proposed method can grade office buildings that exhibit an increased variance ratio of rent. The best combination of building spaces derived by the proposed method comprises the following specifications: a location that is within the main five wards, building age, total floor area, base floor area and ceiling height. These specs differ slightly from those of the existing standards. The existing standards tend to classify buildings that have rents close to the average of different grades; overlaps of rents between different grades are often seen. Conversely, the proposed method sets the grade to classify buildings that have high rents in different grades.<br>&nbsp;Since the term of the data used for this experiment is only 2 years, we need longer-term data to validate the stability of the derived building specs. In addition, since we limited the combination of attributes to 11 due to the constraint of computational time, we should examine the combination of attributes exhaustively.

    DOI CiNii

display all >>

Books etc 【 display / non-display

  • Advances in Mathematics Research, 25

    Albert R. Baswell ed (Part: Contributor )

    Nova Science Publishers  2019

  • 防災・避難計画の数理モデルの高度化と社会実装へ向けて

    瀧澤 重志, 小林 和博, 佐藤 憲一郎, 斎藤 努, 清水 正明, 間瀬 正啓, 藤澤. 克樹, 神山 直之, 九州大学マス・フォア・インダストリ研究所 (Part: Single Work )

    九州大学マス・フォア・インダストリ研究所, 九州大学大学院数理学府  2018

    CiNii

  • Contrast Data Mining: Concepts, Algorithms and Applications

    G. Dong, J. Bailey ed (Part: Contributor )

    Chapman & Hall/CRC  2012.12

Review Papers (Misc) 【 display / non-display

  • アーキインフォマティクス : 建築学の情報化の現在 (特集 建築情報学 アーキインフォマティクス(Archi-informatics))

    湯本 長伯, 花里 俊廣, 円満 隆平, 衣袋 洋一, 渡辺 俊, 野城 智也, 三辻 和弥, 中井 正一, 中川 貴文, 倉田 成人, 吉田 聡, 金子 智弥, 平田 京子, 栗原 伸治, 諸岡 繁洋, 中島 裕輔, 飯塚 悟, 鈴木 広隆, 湯浅 昇, 佐野 友紀, 中川 理, 加藤 孝明, 楠 浩一, 藤田 謙一, 武藤 厚, 長島 一郎, 増田 幸宏, 中野 淳太, 永田 明寛, 藤本 郷史, 武藤 正樹, 山田 哲弥, 齋藤 隆司, 瀧澤 重志, 浅野 聡, 志村 秀明, 大影 佳史, 外岡 豊, 川上 善嗣, 多田 元英, 今塚 善勝, 腰原 幹雄, 岡部 喜裕, 梅宮 典子, 赤司 泰義, 松下 眞治, 宇田 淳, 廣井 悠, 高口 洋人, 谷 明勲, 佐藤 栄治, 加藤 研一, 望月 悦子, 大久保 孝昭, 杉山 央, 本江 正茂, 齊藤 広子, 野澤 康, 長沼 一洋, 西名 大作, 甲谷 寿史, 小見 康夫, 浦江 真人, 小林 剛士, 平野 吉信, 有田 智一, 金澤 健司, 小檜山 雅之, 吹田 啓一郎, 坂本 慎一, 石橋 敏久, 長井 達夫, 蔡 成浩

    一般社団法人日本建築学会 建築雑誌 = Journal of architecture and building science  129 ( 1658 ) 3 - 32 2014.05

    CiNii

  • 避難計画問題への離散アルゴリズムの適用 (特集 データを読み解く技術 : ビッグデータ,e-サイエンス,潜在的ダイナミクス) -- (e-サイエンス時代のアルゴリズム研究)

    瀧澤 重志

    一般社団法人電子情報通信学会 電子情報通信学会誌 = The journal of the Institute of Electronics, Information and Communication Engineers  97 ( 5 ) 393 - 398 2014.05

     View Summary

    阪神・淡路大震災以降,情報通信技術を防災に活用しようとする機運が高まっている.しかし防災対策では,大規模な空間データ,災害シナリオの多様性情報の不確実性などを考慮する必要があり,計算機科学や数理科学にとって挑戦的な問題を内包している.本稿では,避難計画問題に対して離散アルゴリズムの技術を応用する試みとして,避難所の地域割当てをZDD等を用いて行った研究と,動的ネットワークによる津波などからの緊急避難計画に関する研究を紹介する.

  • Risk Discovery of Car-Related Crimes from Urban Spatial Attributes Using Emerging Patterns

    Atsushi Takizawa, Fumie Kawaguchi, Naoki Katoh, Kenji Mori and Kazuo Yoshida

    International Journal of Knowledge-based and Intelligent Engineering Systems (KES)  11 ( 5 ) 301 - 311 2007.12  [Refereed]  [Invited]

  • マルチエージェント・シミュレーションに基づく都心部における人口分布の過渡的動態モデルに関する研究,池谷直樹,谷本潤,萩島理,相良博喜(評論)

    奥俊信,瀧澤重志

    日本建築学会技術報告集  13 ( 26 ) 845 - 848 2007.12  [Refereed]  [Invited]

    DOI CiNii J-GLOBAL

display all >>

Conference Activities & Talks 【 display / non-display

  • Deep Learning Model for Predicting Preference of Space by Estimating the Depth Information of Space using Omnidirectional Images

    Kinugawa Hina, Takizawa Atsushi

    Proceedings of The City Planning Institute of Japan, Kansai Branch  2019  The City Planning Institute of Japan

     View Summary

    In this study, we developed a method for generating omnidirectional depth imagesfrom corresponding omnidirectional RGB images of streetscapes by learningeach pair of omnidirectional RGB and depth images created by computergraphics using pix2pix. Then, the models trained with different series of imagesshot under different site and weather conditions were applied to Google streetview images to generate depth images. The validity of the generated depth imageswas then evaluated visually. In addition, we conducted experiments to evaluateGoogle street view images using multiple participants. We constructed a modelthat estimates the evaluation value of these images with and without the depthimages using the learning-to-rank method with deep convolutional neuralnetwork. The results demonstrate the extent to which the generalizationperformance of the streetscape evaluation model changes depending on thepresence or absence of depth images.

    CiNii

  • Nursing Activities Measurement by Bluetooth Low Energy positioning

    Daisuke Matsushita, Atsushi Takizawa

    The 12th International Symposium on Architectural Interchanges in Asia (ISAIA 2018)  2018.10 

  • 3D Spatial Analysis Method with First-Person Viewpoint by Deep Convolutional Neural Network with Omnidirectional RGB and Depth Images

    A. Takizawa, A. Furuta

    eCAADe 2017  2017.10 

  • Extending Space Syntax with Efficient Enumeration Algorithms and Hypergraphs

    瀧澤 重志

    1th International Space Syntax Symposium  2017.07 

  • 3D Spatial Analysis Method of First Person Viewpoint by Deep Convolutional Neural Network with Omnidirectional RGB and Depth Images

    瀧澤 重志, 古田 愛理

    計算工学講演会論文集 Proceedings of the Conference on Computational Engineering and Science  2017.05 

display all >>

Grant-in-Aid for Scientific Research 【 display / non-display

  • Development of Web System for Detecting Cracks on Wall Surface using Deep Learning

    Project/Area Number : 20K12083  Grant-in-Aid for Scientific Research(C) Partaker / Other

    Project Year :

    2020.04
    -
    2023.03
     

  • Development of a new spatial analysis method that integrates texture, geometry and structural information of space

    Project/Area Number : 20K04872  Grant-in-Aid for Scientific Research(C) Representative

    Project Year :

    2020.04
    -
    2023.03
     

  • Establishing theoretical foundation of optimal evacuation planning based on dynamic networkflows

    Project/Area Number : 19H04068  Grant-in-Aid for Scientific Research(B) Partaker / Other

    Project Year :

    2019.04
    -
    2023.03
     

  • Wellness promotion based on user's behavior measure in medical and welfare institutions

    Project/Area Number : 18H01609  Grant-in-Aid for Scientific Research(B) Partaker / Other

    Project Year :

    2018.04
    -
    2021.03
     

  • Development and Evaluation of Hierarchical Data Analysis and Optimization System for Realizing Smart City

    Project/Area Number : 16H01707  Grant-in-Aid for Scientific Research(A) Partaker / Other

    Project Year :

    2016.04
    -
    2021.03
     

display all >>