Faculty Profiles - YOSHIDA Keisuke

Papers - YOSHIDA Keisuke

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APPLICATION OF IMAGE GENERATION AI IN MODEL FOR DETECTING PLASTIC BOTTLES DURING RIVER PATROL USING UAV Reviewed

Daichi SHIMOE, Shijun PAN, Keisuke YOSHIDA, Satoshi NISHIYAMA, Takashi KOJIMA

Japanese Journal of JSCE 81 ( 16 ) n/a - n/a 2025.2

STUDY ON THE SURVEY OF RIVER SPACE UTILIZATION USING A RIVER MONITORING CAMERA, OBJECT DETECTION, AND A LARGE-SCALE MULTIMODAL MODEL Reviewed

Yuki YAMADA, Keisuke YOSHIDA, Shijun PAN, Takashi KOJIMA

Japanese Journal of JSCE 81 ( 16 ) n/a - n/a 2025.2

Interchangeability of Cross-Platform Orthophotographic and LiDAR Data in DeepLabV3+-Based Land Cover Classification Method Reviewed

Shijun Pan, Keisuke Yoshida, Satoshi Nishiyama, Takashi Kojima, Yutaro Hashimoto

Land 14 ( 2 ) 217 - 217 2025.1

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：MDPI AG

Riverine environmental information includes important data to collect, and the data collection still requires personnel’s field surveys. These on-site tasks still face significant limitations (i.e., hard or danger to entry). In recent years, as one of the efficient approaches for data collection, air-vehicle-based Light Detection and Ranging technologies have already been applied in global environmental research, i.e., land cover classification (LCC) or environmental monitoring. For this study, the authors specifically focused on seven types of LCC (i.e., bamboo, tree, grass, bare ground, water, road, and clutter) that can be parameterized for flood simulation. A validated airborne LiDAR bathymetry system (ALB) and a UAV-borne green LiDAR System (GLS) were applied in this study for cross-platform analysis of LCC. Furthermore, LiDAR data were visualized using high-contrast color scales to improve the accuracy of land cover classification methods through image fusion techniques. If high-resolution aerial imagery is available, then it must be downscaled to match the resolution of low-resolution point clouds. Cross-platform data interchangeability was assessed by comparing the interchangeability, which measures the absolute difference in overall accuracy (OA) or macro-F1 by comparing the cross-platform interchangeability. It is noteworthy that relying solely on aerial photographs is inadequate for achieving precise labeling, particularly under limited sunlight conditions that can lead to misclassification. In such cases, LiDAR plays a crucial role in facilitating target recognition. All the approaches (i.e., low-resolution digital imagery, LiDAR-derived imagery and image fusion) present results of over 0.65 OA and of around 0.6 macro-F1. The authors found that the vegetation (bamboo, tree, grass) and road species have comparatively better performance compared with clutter and bare ground species. Given the stated conditions, differences in the species derived from different years (ALB from year 2017 and GLS from year 2020) are the main reason. Because the identification of clutter species includes all the items except for the relative species in this research, RGB-based features of the clutter species cannot be substituted easily because of the 3-year gap compared with other species. Derived from on-site reconstruction, the bare ground species also has a further color change between ALB and GLS that leads to decreased interchangeability. In the case of individual species, without considering seasons and platforms, image fusion can classify bamboo and trees with higher F1 scores compared to low-resolution digital imagery and LiDAR-derived imagery, which has especially proved the cross-platform interchangeability in the high vegetation types. In recent years, high-resolution photography (UAV), high-precision LiDAR measurement (ALB, GLS), and satellite imagery have been used. LiDAR measurement equipment is expensive, and measurement opportunities are limited. Based on this, it would be desirable if ALB and GLS could be continuously classified by Artificial Intelligence, and in this study, the authors investigated such data interchangeability. A unique and crucial aspect of this study is exploring the interchangeability of land cover classification models across different LiDAR platforms.

DOI： 10.3390/land14020217

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Trials of Night-Time 4K-Camera-based Human Action Recognition in Riverine Environments with Multimodal and Object Detection Technologies Reviewed

Shijun PAN, Keisuke YOSHIDA, Yuki YAMADA, Takashi KOJIMA

Intelligence, Informatics and Infrastructure 5 ( 1 ) 89 - 97 2024.11

Generating 3D Models for UAV-Based Detection of Riparian PET Plastic Bottle Waste: Integrating Local Social Media and InstantMesh Reviewed

Shijun PAN, Keisuke YOSHIDA, Daichi SHIMOE, Takashi KOJIMA, Satoshi NISHIYAMA

Drones 8 ( 9 ) 471 2024.9

Assessment of spawning habitat suitability for Amphidromous Ayu (Plecoglossus altivelis) in tidal Asahi River sections in Japan: Implications for conservation and restoration Reviewed

Keisuke Yoshida, Hiroshi Yajima, Md. Touhidul Islam, Shijun Pan

River Research and Applications 40 ( 8 ) 1497 - 1511 2024.6

Authorship：Lead author,　Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Wiley

Abstract

The Amphidromous Ayu (Plecoglossus altivelis), vital for Japan's riverine commercial fisheries, is experiencing population decline due to habitat degradation. In response, Japan is implementing restoration efforts by releasing juvenile fish and cultivating spawning habitats. This study, focusing on the Asahi River's Heidan area, evaluates Ayu spawning habitat suitability, especially in tidal zones, and its conservation implications. Through field surveys and numerical analysis, the research identifies optimal conditions for spawning amid dwindling numbers. Observations from the 2020 peak spawning season showed that water temperatures below 20°C are conducive to spawning, with the best sites producing up to 48,817 eggs/m². Gravel sizes between 0.85 and 53.0 mm were identified as crucial for effective spawning. Notably, artificial spawning grounds established in 2019 showed no spawning activity, likely due to saline intrusion from tides. Using the three‐dimensional hydrodynamic‐aquatic ecosystem numerical model, the study assesses flow, substrate, and water quality's impact on spawning, corroborated by sensor data. A key finding is the negative correlation between salinity and egg density in tidal sections of the Asahi River. Furthermore, an artificial intelligence (AI)‐based YOLOv5 model, trained on underwater images, effectively detected Ayu aggregations, demonstrating an F1‐score of 0.757 in differentiating Ayu from other coexisting fish species in the same aquatic environment. This AI approach provides a nonintrusive method for monitoring Ayu populations and habitat preferences. The research underlines that successful spawning habitats require minimal saline intrusion, with optimal salinity and flow conditions. These insights are critical for spawning ground development, emphasizing the management of tidal effects and riverbed conditions to bolster Ayu populations and preserve aquatic ecosystems.

DOI： 10.1002/rra.4329

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Monitoring Human Activities in Riverine Space using 4K Camera Images with YOLOv8 and LLaVA: A Case Study from Ichinoarate in the Asahi River Reviewed

Shijun PAN, Keisuke YOSHIDA, Yuki YAMADA, Takashi KOJIMA

Intelligence, Informatics and Infrastructure 5 89 - 97 2024.5

Comprehensive Analysis of On-Site Riparian Waste Pollution: A Case Study on the Hyakken River Basin Reviewed

Shijun PAN, Keisuke YOSHIDA, Takashi KOJIMA

Intelligence, Informatics and Infrastructure 5 98 - 103 2024.5

NUMERICAL REPRODUCTION OF OVERTHROW OF BAMBOO FOREST AND WASHOUT OF WOODY VEGETATION DURING FLOODING USING ESTIMATION OF THREE－DIMENSIONAL PROFILE OF VEGETATION DRAG FORCE WITH AERIAL LASER DATA AND FLOOD SIMULATION Reviewed

Ryo FURUTANI, Keisuke YOSHIDA, Takumi OKADA, Yuki KAJIKAWA, Satoshi NISHIYAMA

Japanese Journal of JSCE 80 ( 16 ) 23-16161 2024

CLASSIFICATION OF PHYTOPLANKTON GROUPS USING A MULTI-WAVELENGTH EXCIATION FLUOROMETER WITH RANDOM FOREST Reviewed

Hiroshi YAJIMA, Yo FUJII, Shogo SUGAHARA, Keisuke YOSHIDA

Japanese Journal of JSCE 80 ( 16 ) 23-16032 2024

DEEP LEARNING OF UNDERWATER IMAGES AND TRAINING DATA CRITERIA FOR UNDERSTANDING FEEDING INJURY OF ACANTHOPAGRUS SCHLEGELII IN NORI SEAWEED FARMS Reviewed

Keisuke YOSHIDA, Shijun PAN, Takahiro YOKOYAMA, Yasushi YAMASHITA, Yuki UEDA, Hiroyuki SUGINO, Takashi KOJIMA

Japanese Journal of JSCE 80 ( 16 ) 23-16150 2024

Mutual validation of remote hydraulic estimates and flow model simulations using UAV-borne LiDAR and deep learning-based imaging techniques Reviewed

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

Results in Engineering 20 101415 - 101415 2023.12

Detection and Segmentation of Riparian Asphalt Paved Cracks Using Drone and Computer Vision Algorithms Reviewed

Shijun PAN, Keisuke YOSHIDA, Satoshi NISHIYAMA

Intelligence, Informatics and Infrastructure 4 ( 2 ) 35 - 49 2023.12

Application of the Prompt Engineering-assisted Generative AI for the Drone-based Riparian Waste Detection Reviewed

Shijun PAN, Keisuke YOSHIDA, Takashi KOJIMA

Intelligence, Informatics and Infrastructure 4 ( 2 ) 50 - 59 2023.12

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

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Wiley

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

Scopus

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Other Link： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/rra.4042

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

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

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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