研究者詳細 - 西山　哲

論文 - 西山　哲

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マルチモーダル深層学習を用いた崩壊する可能性のある山体重力変形の抽出査読

菊地輝行, 﨑田晃基, 西山哲, 高橋謙一

応用地質 = Journal of the Japan Society of Engineering Geology / 日本応用地質学会編 63 ( 6 ) 277 - 290 2023年2月

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

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

Journal of Hydraulic Research 61 ( 1 ) 88 - 108 2023年1月

Design of falling rock preventive countermeasure based on three-dimensional simulation 査読

H Kitauchi, S Nishiyama, J Song

IOP Conference Series: Earth and Environmental Science 1124 ( 1 ) 252 - 257 2023年1月

掲載種別：研究論文（学術雑誌）出版者・発行元：IOP Publishing

Abstract

To rationally design rockfall countermeasures, detailed rockfall behavior must be predicted using numerical simulations based on the conditions of slopes and rockfalls. In this study, we perform a rockfall simulation using discontinuous deformation analysis. This method is used to analyze the motion between blocks composed of polygons of arbitrary elastic bodies; furthermore, the energy conservation law and the uniqueness/convergence of the solution are guaranteed for the entire analysis target. Additionally, the rockfall shape is reproduced in detail. Both the rockfall shape and a parameter known as the velocity energy ratio, which indicates the decay of energy at the collision time of the rockfall, significantly affect the rockfall behavior. In this study, we model the actual slope shape from data measured via airborne laser and quantitatively evaluate the effect via simulations by changing the rockfall shape and velocity energy ratio. In this paper, we consider how to use the simulation for the design of rockfall countermeasures based on the results such as the change in the amount of jumping due to the difference in slope shape, geology, and rockfall shape.

DOI： 10.1088/1755-1315/1124/1/012092

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その他リンク： https://iopscience.iop.org/article/10.1088/1755-1315/1124/1/012092/pdf

UAV写真を用いた不法投棄物の把握査読

井関禎之, 船田征, 西山哲

土木学会論文集特集号（土木情報学） 79 ( 22 ) ID: 22-22014 2023年

ウェーブレット解析図を用いた小規模な段差地形抽出手法に関する検討

今西将文, 中村公一, 西山哲

土木学会論文集 79 ( 22 ) n/a 2023年

記述言語：日本語出版者・発行元：公益社団法人土木学会

航空レーザ（LP）測量データより作成した微地形表現図や地形量図を用いて，業務に利用する事例が増加している．ここでウェーブレット解析図は傾斜量図と異なり，斜面傾斜に影響されず段差地形を抽出することができる．はじめに斜面傾斜と段差の大きさを変更した模擬地形を作成し，ウェーブレット解析図と傾斜量図が表現可能な段差の大きさについて検討した．つぎに，落石発生源を含む平均斜度40°程度の斜面を対象に，ウェーブレット解析図と傾斜量図が抽出できる段差の大きさについて，踏査結果も用いて検討を行なった．これより抽出できる段差の大きさは，ウェーブット解析図は約2m，傾斜量図は4m以上であることがわかった．したがってウェーブレット解析図は，傾斜量図よりも小さい段差地形を抽出可能である．

DOI： 10.2208/jscejj.22-22052

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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査読

安達森, 梶川勇樹, 吉田圭介, 西山哲

土木学会論文集B1（水工学） 78 ( 2 ) I_1153 - I_1158 2022年11月

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

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

Journal of JSCE, B1 78 ( 2 ) I_133 - I_138 2022年11月

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

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

River Research and Applications 38 ( 10 ) 1757 - 1774 2022年8月

掲載種別：研究論文（学術雑誌）

Recent advancements in remotely sensed techniques have markedly expanded data acquisition potential in riverine studies, but the techniques' applicability must be validated and improved because of uncertainties associated with diverse field conditions. This study is the first experimental evidence of using a newly designed unmanned aerial vehicle (UAV)-borne green lidar system (GLS) and deep learning-automated space–time image velocimetry (STIV) for remote investigation of hydraulic and vegetation quantities of the gravel-bed Asahi River in Okayama Prefecture, Japan. In addition to identifying bed deformation in waters shallower than 2 m, the GLS point clouds characterized the submerged infrastructure with block detailing patterns, thereby identifying positional displacement and severely damaged parts. This paper also presents a noncontact method of estimating incremental river discharge. Compared to benchmarked flow model estimates, remotely sensed discharges for three transects covering shallower, deeper, and partially submerged woody vegetation areas were overestimated by 1–11%, with 4% underestimation for another cross-section. The STIV analysis also showed complicated flow patterns that were reasonably confirmed by flow vectors from depth-averaged modeling. Ultimately, depth-averaged flow model estimates validated hydraulic parameters derived remotely from GLS and STIV, and vice versa. In addition to approximating vegetation growth rates, the study using GLS attributes accurately identified riparian vegetation types as herbaceous (70%), woody (86%), and bamboo groves (65%). Finally, our findings provide insight into the management of shallow clear-flowing vegetated rivers and remote sensing of streamflow to validate hydrodynamic-numerical methods.

DOI： 10.1002/rra.4042

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トンネル施工時の湧水量の予測手法の拡張と実トンネルへの適用査読

久保田恭行, 森守正, 安田亨, 小山倫史, 西山哲

地盤工学ジャーナル 17 ( 2 ) 135 - 145 2022年6月

地形量図を用いたサポートベクトルマシンによる落石発生源の検出査読

﨑田晃基, 菊地輝行, 西山哲

地盤工学ジャーナル 17 ( 2 ) 147 - 157 2022年6月

記述言語：日本語出版者・発行元：公益社団法人地盤工学会

落石調査の効率化を目的に，地形量図と機械学習による落石発生源箇所の検出手法について検討をおこなった．現在，点検個所の見落としによる落石被害が報告されており，道路防災点検を確実に実施するための手法が必要となっている．これに対して，航空レーザ測量の活用が注目されている．中でも，計測データから作成する地形量図を活用することで効果的な点検が期待できる．本研究では機械学習を応用した物体検出を地形量図へ適用し，落石発生源の机上検出を行うシステムを提案する．地形量図の 1 種であるウェーブレット解析図をベースに，サポートベクトルマシンによる物体検出を適用した結果，既存の点検個所すべてを検出することに成功し，道路防災点検の効率化に向けたツールとして有効であることが示された．

DOI： 10.3208/jgs.17.147

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SOURCE EXTRACTION OF ROCKFALL BYMICROTOPOGRAPHY HIGHLIGHT MAP VIA HIGH-DENSITY AERIAL LASER SURVEY 査読

Koki Sakita, Teruyuki Kikuchi, Satoshi Nishiyama

International Journal of GEOMATE 22 ( 94 ) 1 - 12 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

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

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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

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

Shijun PAN, Keisuke YOSHIDA, Takashi KOJIMA, Satoshi NISHIYAMA

Artificial Intelligence and Data Science Published by Japan Society of Civil Engineers 3 ( 3 ) 15 - 2 2022年

記述言語：英語出版者・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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NUMERICAL INVESTIGATION FOR THE IMPACT OF CHANNEL GEOMETRY AND VEGETATION CONDITION ON SURFACE VELOCITY COEFFICIENT AROUND THE NAKAHARA BRIDGE IN THE ASAHIKAWA RIVER 査読

Shin ADACHI, Yuki KAJIKAWA, Keisuke YOSHIDA, Satoshi NISHIYAMA

Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 78 ( 2 ) I_1153 - I_1158 2022年

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

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

Proceedings of the 39th IAHR World Congress 6567 - 6571 2022年

QUANTIFYING THE SLOPE DEFORMATION OF A LASER POINT CLOUD ACQUIRED USING MOBILE MAPPING SYSTEM 査読

Naoto Samori, Satoshi Nishiyama, Koki Sakita

International Journal of GEOMATE 21 ( 88 ) 12 - 21 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, Shinya Nigo, Satoshi Nishiyama, Md. Touhidul Islam

JOURNAL OF HYDRO-ENVIRONMENT RESEARCH 39 39 - 59 2021年11月

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

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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Automatic extraction of rockfall source based on terrain analysis map using support vector machine 査読

Koki Sakita, Teruyuki Kikuchi, Satoshi Nishiyama, Junsheng Song, Yuzo Ohnishi

IOP Conference Series: Earth and Environmental Science 861 ( 4 ) 042048 - 042048 2021年10月

River Levees Monitoring Using Three Dimensional Laser Point Clouds with SLAM Technology 査読

Nanoka Akiyama, Satoshi Nishiyama, Koki Sakita, Junsheng Song, Fumiaki Yamazaki

Advancements in Geotechnical Engineering 14 - 22 2021年7月

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