研究者詳細 - 吉田　圭介

論文 - 吉田　圭介

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水中カメラの画像と深層学習による魚道を遡上する稚アユの検出に関する検討査読

潘是均, 古谷崚, 吉田圭介, 山下泰司, 小島崇, 白神義章

土木学会論文B1(水工学) 78 ( 2 ) I_127 - I_132 2022年11月

Drone-LiDAR-assisted Image Fusion methodology for Deep Learning-based Land Cover Classification 査読

Shijun PAN, Keisuke YOSHIDA, Takashi KOJIMA, Satoshi NISHIYAMA

Intelligence, Informatics and Infrastructure 3 ( 3 ) 15 - 25 2022年11月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Japan Society of Civil Engineers

This study uses an unmanned aircraft mounted green Light Detection and Ranging (LiDAR) system to verify the learnability associated with deep learning regarding land cover classification (i.e., evaluated by the average and the absolute difference value of label-based F1, overall accuracy or Macro F1 under the premise of cross-seasonal mutual-prediction). Image fusion method in this study mainly attempts to superimpose the visualized LiDAR data with the aerial photographs to provide new features. LiDAR data are visualized using high contrast color scale (default), average high contrast color scale (same color, different split points as default), and high contrast gray color scale (different color, same split points as default). The reason of setting these methods is to compare the impact of split points and point-associated color in the color scale on learnability of cross-seasonal data. It is worth noting, however from the view of the results, that relying solely on aerial photographs is not sufficient, comparing with image fusion method, especially when training and predicting using cross-seasonal data. From the comparison of the label-based F1, this study has proved the necessary impact of LiDAR on vegetation label-based land cover classification.

DOI： 10.11532/jsceiii.3.3_15

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YoloV5 analysis for juvenile ayu detection in underwater images of fishway 査読

Shijun Pan, Ryo Furutani, Keisuke Yoshida

Proc. of the 2022 Int. Symp. on Ecohydraulics 2022年10月

Using airborne lidar bathymetry-aided transfer learning method in riverine land cover classification 査読

Shijun Pan, Keisuke Yoshida, Takashi Kojima

Proc. of the 2022 Int. Symp. on Ecohydraulics 2022年10月

Developing 3-D numerical flood flow model for vegetated rivers: A case study for lower Asahi River flooding in Japan 査読

Md. Touhidul Islam, Keisuke Yoshida, Yuki Kajikawa, Satoshi Nishiyama

Proc. of 39th IAHR World Congress 2022年6月

Novel UAV-borne green LiDAR versus traditional airborne LiDAR: A case study to characterize hydraulic and riparian vegetation attributes 査読

Md. Touhidul Islam, Keisuke Yoshida, Satoshi Nishiyama, Koichi Sakai

Proc. of 39th IAHR World Congress 2022年6月

Airborne LiDAR-assisted deep learning methodology for riparian land cover classification using aerial photographs and its application for flood modelling 査読

Keisuke Yoshida, Shijun Pan, Junichi Taniguchi, Satoshi Nishiyama, Takashi Kojima, Md. Touhidul Islam

Journal of Hydroinformatics 24 ( 1 ) 179 - 201 2022年1月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：IWA Publishing

<title>Abstract</title>
In response to challenges in land cover classification (LCC), many researchers have experimented recently with classification methods based on artificial intelligence techniques. For LCC mapping of the vegetated Asahi River in Japan, the current study uses deep learning (DL)-based DeepLabV3+ module for image segmentation of aerial photographs. We modified the existing model by concatenating data on its resultant output port to access the airborne laser bathymetry (ALB) dataset, including voxel-based laser points and vegetation height (i.e. digital surface model data minus digital terrain model data). Findings revealed that the modified approach improved the accuracy of LCC greatly compared to our earlier unsupervised ALB-based method, with 25 and 35% improvement, respectively, in overall accuracy and the macro F1-score for November 2017 dataset (no–leaf condition). Finally, by estimating flow-resistance parameters in flood modelling using LCC mapping-derived data, we conclude that the upgraded DL methodology produces better fit between numerically analyzed and observed peak water levels.

DOI： 10.2166/hydro.2022.134

Scopus

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Characterizing vegetated rivers using novel unmanned aerial vehicle‐borne topo‐bathymetric green lidar: Seasonal applications and challenges 査読

Md. Touhidul Islam, Keisuke Yoshida, Satoshi Nishiyama, Koichi Sakai, Tomoyuki Tsuda

River Research and Applications 38 ( 1 ) 44 - 58 2022年1月

旭川の感潮域におけるアユの好適な産卵場の検討査読

足立真綾, 吉田圭介, 矢島啓, 山下泰司

土木学会論文B1(水工学) 77 ( 2 ) I_1429 - I_1434 2021年12月

UAVレーザ点群と航空写真を併用した深層学習による河道の地被分類法の検討査読

野村碧都, 吉田圭介, 谷口純一

土木学会論文B1(水工学) 77 ( 2 ) I_913 - I_918 2021年12月

Flood risk assessment in vegetated lower Asahi River of Okayama Prefecture in Japan using airborne topo-bathymetric LiDAR and depth-averaged flow model 査読

Keisuke Yoshida, Kimihisa nagata, Shiro maeno, Koji mano, Satoshi nishiyama, Md. Touhidul Islam

Journal of Hydro-environment Research 39 39 - 59 2021年10月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Elsevier BV

This paper describes application of airborne LiDAR bathymetry (ALB) with near-infrared and green pulsed lasers for gathering distributed vegetation conditions and topo-bathymetric data for rivers. For the lower Asahi River of Okayama Prefecture in Japan, the ALB data validity was verified using field observation data. This study also examined the applicability of ALB data for numerical simulations of the lower Asahi River flooding in early July 2018 in Japan, comparing simulated and observed data. Results demonstrated that the methodology for this study works well for parameterization of distributed vegetation on a reach scale. This study also applied numerical tests to investigate the effects of vegetation establishment on flood control plans for the lower Asahi River using parameters validated for flood flow simulations. Results demonstrate that the predicted water level markedly exceeds the high water level because of thick vegetation presently established along few-kilometerlong upstream sections of the targeted river reach. Therefore, we conclude that the present findings can support cost-effective management tasks for vegetated rivers.

DOI： 10.1016/j.jher.2021.06.005

Web of Science

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Model-induced comparative study of ALB and UAV-borne green LiDAR systems on vegetated lower Asahi river assessment in Japan 査読

Md. Touhidul ISLAM, Keisuke YOSHIDA, Koichi SAKAI, Satoshi NISHIYAMA, Tomoyuki TSUDA, Kimishisa NAGATA and Shiro MAENO

土木学会論文集B1(水工学) 76 ( 2 ) I_529 - I_534 2020年12月

ALB 点群データを援用した河川航空写真の深層学習による地被分類法の検討査読

吉田圭介,谷口純一,潘是均,小島崇,永田貴美久,渡邊明英

土木学会論文集B1(水工学) 76 ( 2 ) I_1003 - I_1008 2020年12月

担当区分：筆頭著者,　責任著者記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：公益社団法人土木学会

河道の地被分類は河川の治水や水環境の研究や実務において重要な基礎資料である．近年では豪雨による河川災害が危惧され，特に，地先単位の洪水疎通能力の評価において河積や，河道内の植生の高さおよび密生度が必要となり，例えば航空レーザー測深(ALB)データの更なる利用が期待される．本研究では岡山県旭川下流の国管理の約4.5kmの区間を対象に，ALB計測時に撮影される航空写真を活用し，ALB点群データから得られる植生高とレーザー点数を組み合わせることで新たな深層学習による地被分類方法を開発した．解析の結果，航空写真だけよりもALB点群データを併用することで，竹林・樹木と草本の分類性能が向上した．しかし，時期の異なるデータセットを相互に学習や推論に用いると，落葉などの影響で樹木と草本の分類性能が低下することが分かった．

DOI： 10.2208/jscejhe.76.2_i_1003

CiNii Article

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平成30 年7月豪雨時の旭川分流堰周辺の植生分布を考慮した3次元洪水流解析査読

吉田圭介,梶川勇樹,永田貴美久,西山哲,間野耕司,堺浩一,Md. Touhidul ISLAM,児子真也

土木学会論文集B1(水工学) 76 ( 2 ) I_985 - I_990 2020年12月

Verification of STIV analysis of flood discharge at Nakano observatory in the Ota River by flow analysis using ALB data 査読

K. Yoshida, S. Nigo, S. Maeno, K. Mano, M. Nakano

Proc. 22nd IAHR-APD Congress 2020年9月

Flood simulation model coupled with woody vegetation wash-out in the lower Asahi River 査読

K. Nagata, K. Yoshida, S. Maeno, S. Nigo, MD. T. Islam

Proc. 22nd IAHR-APD Congress 2020年9月

Estimation of distributed flow resistance in vegetated rivers using airborne topo-bathymetric LiDAR and its application to risk management tasks for Asahi River flooding 査読

Keisuke Yoshida, Shiro Maeno, Shuhei Ogawa, Koji Mano and Shinya Nigo

Journal of Flood Risk Management 13 ( 1 ) e12584 2020年3月

2018 年7 月西日本豪雨時の岡山県での降雨特性と旭川水系での洪水被害の調査解析査読

吉田圭介,前野詩朗,工藤亮治,近森秀高,赤穗良輔,小川修平,永田貴美久

土木学会論文集B1(水工学) 75 ( 1 ) 172 - 183 2019年12月

2018 年豪雨による倉敷市真備地区の被災状況と包括型氾濫解析モデルによる氾濫シナリオの検討査読

前野詩朗,赤穗良輔,二瓶泰雄,赤松良久,吉田圭介

土木学会論文集B1(水工学) 75 ( 1 ) 387 - 402 2019年12月

記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：公益社団法人土木学会

<p> 2018年7月豪雨災害により，倉敷市真備町を流下する小田川及びその支流の末政川，高馬川，真谷川の複数箇所で堤防が決壊し，真備町では1200haの地域が浸水し，多くの方が犠牲となった．本研究では，現地調査により浸水状況を明らかにした．また，河道の洪水流と決壊箇所からの氾濫流を同時に解く包括型氾濫解析モデルを用いて，氾濫シナリオを検討した．解析では，最初に，決壊を生じさせない条件で解析を行い，氾濫開始時刻の推定を行い氾濫シナリオを検討した．次に，決壊させた条件で解析を行い，氾濫水の挙動を明らかにするとともに，解析により得られた浸水深は現地調査結果をよく再現できることを明らかにした．</p>

DOI： 10.2208/jscejhe.75.1_387

CiNii Article

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太田川中野観測所におけるSTIV流量観測とALBデータを用いた水理解析による精度検証査読

児子真也,吉田圭介,前野詩朗,間野耕司,中野光隆

土木学会論文集B1(水工学) 75 ( 4 ) I_613 - I_618 2019年12月

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