Faculty Profiles - YOSHIDA Keisuke

Papers - YOSHIDA Keisuke

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Three-dimensional numerical modelling of floods in river corridor with complex vegetation quantified using airborne LiDAR imagery Reviewed

Keisuke Yoshida, Yuki Kajikawa, Satoshi Nishiyama, Md. Touhidul Islam, Shin Adachi, Koichi Sakai

Journal of Hydraulic Research 61 ( 1 ) 88 - 108 2023.1

Promises and uncertainties in remotely sensed riverine hydro‐environmental attributes: Field testing of novel approaches to unmanned aerial vehicle‐borne lidar and imaging velocimetry Reviewed

Md. Touhidul Islam, Keisuke Yoshida, Satoshi Nishiyama, Koichi Sakai, Shin Adachi, Shijun Pan

River Research and Applications 38 ( 10 ) 1757 - 1774 2022.12

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 Reviewed

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 2022.11

堤防越水場に設置した裏法面被覆工の不陸が流体力特性へ及ぼす影響に関する研究 Reviewed

飯干富広, 佐々木海音, 安達森, 忰熊公子, 吉田圭介

土木学会論文B1(水工学) 78 ( 2 ) I_655 - I_660 2022.11

PROJECT EVALUATION OF CROSS-SECTIONAL EXCAVATION WORK OF A SANDBAR IN THE ASAHI RIVER TIDAL ZONE FOR EXPANSION OF AYU SPAWNING BED AREA Reviewed

YOSHIDA Keisuke, ADACHI Maya, YAJIMA Hiroshi, YAMASHITA Yasushi, ADACHI Shin, FUJII Yo

Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 78 ( 2 ) I_1111 - I_1116 2022.11

Authorship：Lead author,　Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Japan Society of Civil Engineers

In order to increase the spawning bed area of Ayu in the Heidan area of the Asahi River tidal zone, a portion of a sandbar around a natural spawning ground was excavated and a new artificial spawning ground (sub-current channel) was constructed under normal flow conditions in a project. For this study, field observations and numerical calculations were conducted to examine the suitability of the hydraulic environment of the artificial spawning area creation. Additionally, we examined the impacts of the construction on the existing natural spawning habitat. Results showed that the suitable spawning area was an area where there was no salinity increase at any time, and that the river bed grain size, water depth during spawning time, and current velocity were within the suitable values proposed in previous works. Results also presented that the spawning density in the constructed area might be more dependent on the suitability of the grain size than on the flow regime. Furthermore, the results of high-resolution 2D horizontal calculations showed that the creation of the new spawning area did not significantly alter the hydraulic environment of the existing natural spawning grounds, and contributed to the increase in spawning bed area, which was the original purpose of the project.

DOI： 10.2208/jscejhe.78.2_i_1111

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旭川中原橋周辺の河道形状及び植生分布の違いが更正係数に与える影響に関する数値的検討 Reviewed

安達森, 梶川勇樹, 吉田圭介, 西山哲

土木学会論文B1(水工学) 78 ( 2 ) I_1153 - I_1158 2022.11

The application of drone-assisted deep learning technology in riverbank garbage detection Reviewed

Shijun PAN, Keisuke YOSHIDA, Afia S. BONEY, Satoshi NISHIYAMA

Journal of JSCE, B1 78 ( 2 ) I_133 - I_138 2022.11

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

Shijun PAN, Keisuke YOSHIDA, Takashi KOJIMA, Satoshi NISHIYAMA

Intelligence, Informatics and Infrastructure 3 ( 3 ) 15 - 25 2022.11

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：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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Using airborne lidar bathymetry-aided transfer learning method in riverine land cover classification Reviewed

Shijun Pan, Keisuke Yoshida, Takashi Kojima

Proc. of the 2022 Int. Symp. on Ecohydraulics 2022.10

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

Shijun Pan, Ryo Furutani, Keisuke Yoshida

Proc. of the 2022 Int. Symp. on Ecohydraulics 2022.10

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

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

Proc. of 39th IAHR World Congress 2022.6

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

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

Proc. of 39th IAHR World Congress 2022.6

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

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

Journal of Hydroinformatics 24 ( 1 ) 179 - 201 2022.1

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

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

River Research and Applications 38 ( 1 ) 44 - 58 2022.1

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 Reviewed

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 2021.12

旭川の感潮域におけるアユの好適な産卵場の検討 Reviewed

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 2021.12

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

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

Journal of Hydro-environment Research 39 39 - 59 2021.10

ALB-ASSISTED DEEP LEARNING OF AERIAL PHOTOGRAPHS FOR LAND-COVER CLASSIFICATION IN RIVERS Reviewed

YOSHIDA Keisuke, TANIGUCHI Junichi, PAN Shijun, KOJIMA Takashi, NAGATA Kimihisa, WATANABE Akihide

Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 76 ( 2 ) I_1003 - I_1008 2020.12

Authorship：Lead author,　Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Japan Society of Civil Engineers

Land cover classification (LCC) map in rivers is one of the important fundamental materials for hydraulic research works and practical tasks in terms of both flood control and water environment conservation. Recently, massive flooding because of heavy rainfall motivates hydraulic engineers to estimate the flow capacity at local river sections accurately by re-evaluating the LCC map of the cross-sectional area, vegetation height, and vegetation density, using useful remote sensing methods such as airborne laser bathymetry (ALB). For this work, we developed a new methodology for land cover classification (water area, bare ground, herbaceous species, arborous species, and bamboo grove) in rivers. We used an image processing technique of deep convolutional neural networks (DCNNs) for aerial photographs taken during ALB measurements in conjunction with the dataset of laser point cloud and vegetation height (digital surface model data minus digital terrain model data). We applied our method to two flights of ALB measurement data (in March 2017 and November 2017) for the 4.5 km-long lower reach of the Asahi River. Results showed that our method predicts the LCC map more accurately in case of support of the ALB dataset, especially for detection of herbaceous species, compared with predictions using aerial photographs only. However, if we use different season dataset for learning and prediction processes, classification performance is degraded largely for arborous species because of defoliation.

DOI： 10.2208/jscejhe.76.2_i_1003

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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 Reviewed

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 2020.12

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

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 2020.12

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Papers - YOSHIDA Keisuke