東北大学研究者紹介

研究者詳細

ホーム

日本語 English

ユアン　ウエイ

袁　巍

Wei Yuan

所属

災害科学国際研究所　附属災害レジリエンス共創センター　災害レジリエンス数量化研究領域

職名

准教授

学位

博士（東京大学）
修士（東京大学）
博士（武漢大学）

researchmap

https://researchmap.jp/milowei304

J-GLOBAL ID

202101007307066759

e-Rad 研究者番号

60837475

ORCID

https://orcid.org/0000-0002-1370-0079

経歴 4

2026年7月～継続中

東北大学　学際科学フロンティア研究所　フェロー（兼務）
2024年2月～継続中

東北大学
2020年3月～ 2024年2月

東京大学
2018年10月～ 2020年3月

東京大学

学歴 3

武漢大学　リモートセンシング情報工学部　写真測量とリモートセンシング

2012年9月～ 2020年6月
東京大学　大学院工学系研究科　社会基盤学専攻

2015年10月～ 2018年9月
東京大学　大学院工学系研究科　社会基盤学専攻

2014年10月～ 2015年9月

所属学協会 4

電気電子学会

2018年7月～継続中
IEEE 地球科学とリモートセンシング学会

2018年6月～継続中
米国写真測量・リモートセンシング協会

2018年6月～継続中
国際写真測量とリモートセンシング学会

2016年6月～継続中

研究キーワード 1

写真測量;リモートセンシング;３次元再構築; 災害評価; GeoAI;

研究分野 1

社会基盤（土木・建築・防災） / 土木計画学、交通工学 /

受賞 3

ISPRS WEC Kennert Torlegård Award

2026年7月　国際写真測量とリモートセンシング学会　Flood Depth Mapping from SAR Imagery Using CS-Mamba with DEM Sensitivity Analysis
最優秀若手研究者賞

2024年10月　国際写真測量とリモートセンシング学会
最優秀若手作家論文賞

2022年6月　国際写真測量とリモートセンシング学会　Learning Social Compliant Multi-Modal Distributions of Human Path in Crowds

論文 48

Kinematic Characteristics and Risk Analysis of Potential Rockfall based on 3D Point Clouds 査読有り

Wei Yuan, Changqing Liu, Han Bao, Weihang Ran, Zhongyuan Yang, Xiuxiao Yuan, Ryosuke Shibasaki, Shunichi Koshimura

XLIX-B4-2026　223-228　2026年8月4日
出版者・発行元：
DOI： 10.5194/isprs-archives-xlix-b4-2026-223-2026 　

eISSN：2194-9034
Flood Depth Mapping from SAR Imagery Using CS-Mamba with DEM Sensitivity Analysis 査読有り

Zhongyuan Yang, Wei Yuan, Weihang Ran, Changqing Liu, Bruno Adriano, Ryosuke Shibasaki, Shunichi Koshimura

Zhongyuan Yang, Wei Yuan, Weihang Ran, Changqing Liu, Bruno Adriano, Ryosuke Shibasaki, and Shunichi Koshimura　XI-3-2026　493-500　2026年7月8日
出版者・発行元：
DOI： 10.5194/isprs-annals-xi-3-2026-493-2026 　

eISSN：2194-9050
An efficient cost calculation method for disparity estimation of stereo matching considering shadow occlusion 査読有り

Hongjun Sha, Wei Yuan, Xunping Wang, Xiuliu Yuan, Shunichi Koshimura

Geo-spatial Information Science　2026年1月2日

DOI： 10.1080/10095020.2025.2487138 　
Taming Spatial Heterophily and Temporal Irregularity: A Curriculum Learning Approach for Traffic Forecasting 査読有り

Hongjun Wang, Zhiwen Zhang, Jiyuan Chen, Zipei Fan, Renhe Jiang, Wei Yuan, Ryosuke Shibasaki, Xuan Song

IEEE Transactions on Intelligent Transportation Systems　1-17　2026年
出版者・発行元：
DOI： 10.1109/tits.2026.3685281 　

ISSN：1524-9050

eISSN：1558-0016
Visibility-Aware Disparity Estimation for Aerial Images by Fusing Line Features in Shadow Areas 査読有り

Hongjun Sha, Wei Yuan, Siyuan Zou, Ryosuke Shibasaki, Shunichi Koshimura

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing　1-26　2026年
出版者・発行元：
DOI： 10.1109/jstars.2026.3683686 　

ISSN：1939-1404

eISSN：2151-1535
Quantitative identification and hazard assessment of potentially unstable rock masses along mountainous transportation corridors 査読有り

Changqing Liu, Han Bao, Jingfeng Zhang, Jinghao Yang, Xuanyan Dong, Hengxing Lan, Shunichi Koshimura, Wei Yuan

Engineering Failure Analysis　2026年1月

DOI： 10.1016/j.engfailanal.2025.110273 　
Intelligent characterization of discontinuities and heterogeneity evaluation of potential hazard sources in high-steep rock slope by TLS-UAV technology 査読有り

Changqing Liu, Han Bao, Tianyi Wang, Jingfeng Zhang, Hengxing Lan, Shengwen Qi, Wei Yuan, Shunichi Koshimura

Journal of Rock Mechanics and Geotechnical Engineering　2026年1月

DOI： 10.1016/j.jrmge.2025.03.023 　
PortVIS: An Interactive Platform for Port-to-Port Trajectory Imputation and Visual Analytics 査読有り

Zhiwen Zhang, Zipei Fan, Wei Yuan, Shun Iwazaki, Ryosuke Shibasaki

Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems　812-815　2025年11月3日
出版者・発行元： ACM
DOI： 10.1145/3748636.3762787 　
Accurate Digital Reconstruction of High-Steep Rock Slope via Transformer-Based Multi-Sensor Data Fusion 査読有り

Changqing Liu, Han Bao, Jingfeng Zhang, Hengxing Lan, Bruno Adriano, Shunichi Koshimura, Wei Yuan

Remote Sensing　17　(21)　3555-3555　2025年10月28日
出版者・発行元： MDPI AG
DOI： 10.3390/rs17213555 　

eISSN：2072-4292

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Accurate and comprehensive characterization of high-steep slopes is crucial for real-time risk prediction, disaster assessment, and damage evolution monitoring. The study focused on a high-steep rocky slope along the Yanjiang Expressway in Sichuan Province, China. A novel digital reconstruction method was introduced, which integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry through a Transformer-based method combining GeoTransformer with the Maximal Cliques (MAC) algorithm. The results indicated that TLS excels in capturing fine-scale features, whereas UAV demonstrates superior performance in large-scale terrain reconstruction. However, multi-sensor data exhibit heterogeneity in terms of partial overlap, large outliers, and density differences. To address these challenges, the GeoTransformer-MAC framework extracts geometrically invariant features from cross-source point cloud (CSPC) to establish initial correspondences, followed by rigorous screening of high-quality locally consistent correspondences to optimize transformation parameters. This method achieves accurate digital reconstruction of the high-steep rock slope. Global and local error analyses verify the model’s superiority in both overall slope characterization and fine-scale feature representation. Compared with the TLS-only model and the conventional method, the Transformer-based method improves the slope model integrity by 85.58%, increases the data density by 9.71%, and improves the accuracy by nearly threefold. This study provides a novel approach for the digital modeling of complex terrains, which serves the refined identification and modeling of geohazards for high-steep slopes in complex mountainous regions.
A Generalized Deep Learning Method for Rooftop Condition Assessment via Monocular Imagery 査読有り

Xinyu Li, Zhiling Guo, Jian Xu, Wei Yuan, Xiaoya Song, Haoran Zhang, Jinyue Yan

IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium　790-793　2025年8月3日
出版者・発行元：
DOI： 10.1109/igarss55030.2025.11243709 　
Multi-source 3D point clouds fusion for potential rock mass hazard evaluation in high-steep rock slopes 査読有り

Wei Yuan, Changqing Liu, Tianyi Wang, Bruno Adriano, Han Bao, Ryosuke Shibasaki, Shunichi Koshimura

XLVIII-G-2025　1663-1668　2025年8月2日
出版者・発行元：
DOI： 10.5194/isprs-archives-xlviii-g-2025-1663-2025 　

eISSN：2194-9034
Tiered Spatio-Temporal Difficulty: Curriculum Scheduler for Multi-Sensor Traffic Flow Prediction 査読有り

Zhiwen Zhang, Hongjun Wang, Zipei Fan, Renhe Jiang, Wei Yuan, Xuan Song, Ryosuke Shibasaki

IEEE Transactions on Mobile Computing　1-15　2025年
出版者・発行元： Institute of Electrical and Electronics Engineers (IEEE)
DOI： 10.1109/tmc.2025.3608620 　

ISSN：1536-1233

eISSN：1558-0660 2161-9875
AISFuser: Encoding Maritime Graphical Representations With Temporal Attribute Modeling for Vessel Trajectory Prediction 査読有り

Zhiwen Zhang, Wei Yuan, Zipei Fan, Xuan Song, Ryosuke Shibasaki

IEEE Transactions on Knowledge and Data Engineering　2025年

DOI： 10.1109/TKDE.2025.3531770 　
The Performance of the Optical Flow Field based Dense Image Matching for UAV Imagery 査読有り

Wei Yuan, Weihang Ran, Bruno Adriano, Ryosuke Shibasaki, Shunichi Koshimura

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences　X-4-2024　433-440　2024年10月18日
出版者・発行元： Copernicus GmbH
DOI： 10.5194/isprs-annals-x-4-2024-433-2024 　

eISSN：2194-9050

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Abstract. With the rapid development of sensing platforms, unmanned aerial vehicle (UAV)-based mapping has become increasingly popular because of its economic efficiency and flexibility, especially for providing 3D information to support urban growth monitoring and change detection to meet sustainable development goals (SDGs). This paper presents an improved optical flow field-based dense matching algorithm (OFFDM) for low-altitude UAV images based on the Ph.D. thesis of Yuan (Yuan, 2018). First, high-precision seed points were used to compute the optical flow field within stereo pairs, effectively minimizing redundant calculations during the fine-matching phase. Second, a fine-matching approach, integrating multiple constraints, was applied to refine the coarse matching results based on the optical flow field. Extensive dense matching experiments on UAV low-altitude aerial imagery assessed the performance of OFFDIM across four dimensions: 3D point cloud visualization, matching success rate, precision, and reliability. Extensive experiments on low-altitude UAV imagery, characterized by a resolution of 7cm per pixel over a 10,608×8,608 pixel dimension and a 60% forward overlap, evaluate the OFFDM's efficacy. The quantitative evaluation revealed that the proposed method achieved an accuracy of ±0.7 pixels in image coordinates and ±20 cm on the ground, with a matching success rate exceeding 97%. The processing time was approximately 272 seconds for handling one single stereo pair. When compared to the widely adopted PMVS algorithm, known for its effectiveness in dense matching for UAV images, the proposed method demonstrated higher completeness and improved matching efficiency by more than five times. These results demonstrated that the proposed approach is more suitable for dense matching on UAV imagery-based high-precision 3D spatial data extraction, supporting global mapping tasks more effectively.
General generative AI-based image augmentation method for robust rooftop PV segmentation 査読有り

Hongjun Tan, Zhiling Guo, Zhengyuan Lin, Yuntian Chen, Dou Huang, Wei Yuan, Haoran Zhang, Jinyue Yan

Applied Energy　368　123554-123554　2024年8月
出版者・発行元： Elsevier BV
DOI： 10.1016/j.apenergy.2024.123554 　

ISSN：0306-2619
Nuclei-level prior knowledge constrained multiple instance learning for breast histopathology whole slide image classification 査読有り

Xunping Wang, Wei Yuan

iScience　27　(6)　109826-109826　2024年6月
出版者・発行元： Elsevier BV
DOI： 10.1016/j.isci.2024.109826 　

ISSN：2589-0042
Hybrid Network-Based Automatic Seamline Detection for Orthophoto Mosaicking 査読有り

Wei Yuan, Yang Cai, Jonathan Li

IEEE Transactions on Geoscience and Remote Sensing　62　1-14　2024年4月
出版者・発行元： Institute of Electrical and Electronics Engineers (IEEE)
DOI： 10.1109/tgrs.2024.3393626 　

ISSN：0196-2892

eISSN：1558-0644
A cluster-based disambiguation method using pose consistency verification for structure from motion 査読有り

Ye Gong, Pengwei Zhou, Changfeng Liu, Yan Yu, Jian Yao, Wei Yuan, Li Li

ISPRS Journal of Photogrammetry and Remote Sensing　2024年3月

DOI： 10.1016/j.isprsjprs.2024.02.016 　
Hybrid Feature Embedding for Automatic Building Outline Extraction 査読有り

Weihang Ran, Wei Yuan, Xiaodan Shi, Zipei Fan, Ryosuke Shibasaki

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium　2023年7月16日
出版者・発行元： IEEE
DOI： 10.1109/igarss52108.2023.10282867 　
Graph Encoding based Hybrid Vision Transformer for Automatic Road Network Extraction 査読有り

Wei Yuan, Weihang Ran, Xiaodan Shi, Zipei Fan, Yang Cai, Ryosuke Shibasaki

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium　2023年7月16日
出版者・発行元： IEEE
DOI： 10.1109/igarss52108.2023.10283247 　
Few-Shot Depth Completion Using Denoising Diffusion Probabilistic Model 査読有り

Weihang Ran, Wei Yuan, Ryosuke Shibasaki

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)　2023-June　6559-6567　2023年6月
出版者・発行元： IEEE
DOI： 10.1109/cvprw59228.2023.00697 　

ISSN：2160-7508

eISSN：2160-7516
LiteST-Net: A Hybrid Model of Lite Swin Transformer and Convolution for Building Extraction from Remote Sensing Image 査読有り

Wei Yuan, Xiaobo Zhang, Jibao Shi, Jin Wang

Remote Sensing　15　(8)　1996-1996　2023年4月10日
出版者・発行元： MDPI AG
DOI： 10.3390/rs15081996 　

eISSN：2072-4292

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Extracting building data from remote sensing images is an efficient way to obtain geographic information data, especially following the emergence of deep learning technology, which results in the automatic extraction of building data from remote sensing images becoming increasingly accurate. A CNN (convolution neural network) is a successful structure after a fully connected network. It has the characteristics of saving computation and translation invariance with improved local features, but it has difficulty obtaining global features. Transformers can compensate for the shortcomings of CNNs and more effectively obtain global features. However, the calculation number of transformers is excessive. To solve this problem, a Lite Swin transformer is proposed. The three matrices Q, K, and V of the transformer are simplified to only a V matrix, and the v of the pixel is then replaced by the v with the largest projection value on the pixel feature vector. In order to better integrate global features and local features, we propose the LiteST-Net model, in which the features extracted by the Lite Swin transformer and the CNN are added together and then sampled up step by step to fully utilize the global feature acquisition ability of the transformer and the local feature acquisition ability of the CNN. The comparison experiments on two open datasets are carried out using our proposed LiteST-Net and some classical image segmentation models. The results show that compared with other networks, all metrics of LiteST-Net are the best, and the predicted image is closer to the label.
Voronoi Centerline-Based Seamline Network Generation Method 査読有り

Xiuxiao Yuan, Yang Cai, Wei Yuan

Remote Sensing　15　(4)　2023年2月

DOI： 10.3390/rs15040917 　

eISSN：2072-4292
Multiconstraint Transformer-Based Automatic Building Extraction From High-Resolution Remote Sensing Images 査読有り

Wei Yuan, Weihang Ran, Xiaodan Shi, Ryosuke Shibasaki

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing　2023年

DOI： 10.1109/JSTARS.2023.3319826 　
MetaTraj: Meta-Learning for Cross-Scene Cross-Object Trajectory Prediction 査読有り

Xiaodan Shi, Haoran Zhang, Wei Yuan, Ryosuke Shibasaki

IEEE Transactions on Intelligent Transportation Systems　1-10　2023年
出版者・発行元： Institute of Electrical and Electronics Engineers ({IEEE})
DOI： 10.1109/TITS.2023.3299112 　

ISSN：1524-9050

eISSN：1558-0016
Fully automatic DOM generation method based on optical flow field dense image matching 査読有り

Wei Yuan, Xiuxiao Yuan, Yang Cai, Ryosuke Shibasaki

Geo-Spatial Information Science　26　(2)　242-256　2023年

DOI： 10.1080/10095020.2022.2159886 　

ISSN：1009-5020
Shift Pooling PSPNet: Rethinking PSPNet for Building Extraction in Remote Sensing Images from Entire Local Feature Pooling 査読有り

Wei Yuan, Jin Wang, Wenbo Xu

Remote Sensing　14　(19)　4889-4889　2022年9月30日
出版者・発行元： MDPI AG
DOI： 10.3390/rs14194889 　

eISSN：2072-4292

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Building extraction by deep learning from remote sensing images is currently a research hotspot. PSPNet is one of the classic semantic segmentation models and is currently adopted by many applications. Moreover, PSPNet can use not only CNN-based networks but also transformer-based networks as backbones; therefore, PSPNet also has high value in the transformer era. The core of PSPNet is the pyramid pooling module, which gives PSPNet the ability to capture the local features of different scales. However, the pyramid pooling module also has obvious shortcomings. The grid is fixed, and the pixels close to the edge of the grid cannot obtain the entire local features. To address this issue, an improved PSPNet network architecture named shift pooling PSPNet is proposed, which uses a module called shift pyramid pooling to replace the original pyramid pooling module, so that the pixels at the edge of the grid can also obtain the entire local features. Shift pooling is not only useful for PSPNet but also in any network that uses a fixed grid for downsampling to increase the receptive field and save computing, such as ResNet. A dense connection was adopted in decoding, and upsampling was gradually carried out. With two open datasets, the improved PSPNet, PSPNet, and some classic image segmentation models were used for comparative experiments. The results show that our method is the best according to the evaluation metrics, and the predicted image is closer to the label.
GapLoss: A Loss Function for Semantic Segmentation of Roads in Remote Sensing Images 査読有り

Wei Yuan, Wenbo Xu

Remote Sensing　14　(10)　2422-2422　2022年5月18日
出版者・発行元： MDPI AG
DOI： 10.3390/rs14102422 　

eISSN：2072-4292

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At present, road continuity is a major challenge, and it is difficult to extract the centerline vector of roads, especially when the road view is obstructed by trees or other structures. Most of the existing research has focused on optimizing the available deep-learning networks. However, the segmentation accuracy is also affected by the loss function. Currently, little research has been published on road segmentation loss functions. To resolve this problem, an attention loss function named GapLoss that can be combined with any segmentation network was proposed. Firstly, a deep-learning network was used to obtain a binary prediction mask. Secondly, a vector skeleton was extracted from the prediction mask. Thirdly, for each pixel, eight neighboring pixels with the same value of the pixel were calculated. If the value was 1, then the pixel was identified as the endpoint. Fourth, according to the number of endpoints within a buffered range, each pixel in the prediction image was given a corresponding weight. Finally, the weighted average value of the cross-entropy of all the pixels in the batch was used as the final loss function value. We employed four well-known semantic segmentation networks to conduct comparative experiments on three large datasets. The results showed that, compared to other loss functions, the evaluation metrics after using GapLoss were nearly all improved. From the predicted image, the road prediction by GapLoss was more continuous, especially at intersections and when the road was obscured from view, and the road segmentation accuracy was improved.
LEARNING SOCIAL COMPLIANT MULTI-MODAL DISTRIBUTIONS OF HUMAN PATH IN CROWDS 査読有り

X. Shi, H. Zhang, W. Yuan, D. Huang, Z. Guo, R. Shibasaki

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences　V-4-2022　(4)　91-98　2022年5月18日
出版者・発行元： Copernicus {GmbH}
DOI： 10.5194/isprs-annals-v-4-2022-91-2022 　

ISSN：2194-9050

eISSN：2194-9050
Online trajectory prediction for metropolitan scale mobility digital twin. 査読有り

Zipei Fan, Xiaojie Yang, Wei Yuan, Renhe Jiang, Quanjun Chen, Xuan Song 0001, Ryosuke Shibasaki

SIGSPATIAL/GIS　103-12　2022年

DOI： 10.1145/3557915.3561040 　
Impact of sensor data sampling rate in gnss/ins integrated navigation with various sensor grades 査読有り

Y. Wei, Y. Li

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives　46　(3/W1-2022)　205-211　2022年

DOI： 10.5194/isprs-archives-XLVI-3-W1-2022-205-2022 　

ISSN：1682-1750
Cross-Scale Attention-based Tree Crown Detection via UAV imagery 査読有り

Wei Yuan, Xiaodan Shi, Zhiling Guo, Zipei Fan, Jianya Gong, Ryosuke Shibasaki

International Geoscience and Remote Sensing Symposium (IGARSS)　2022-July　2203-2206　2022年

DOI： 10.1109/IGARSS46834.2022.9884316 　
MSST-Net: A Multi-Scale Adaptive Network for Building Extraction from Remote Sensing Images Based on Swin Transformer 査読有り

Wei Yuan, Wenbo Xu

Remote Sensing　13　(23)　4743-4743　2021年11月23日
出版者・発行元： MDPI AG
DOI： 10.3390/rs13234743 　

eISSN：2072-4292

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The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A transformer, whose self-attention mechanism can supply each pixel with a global feature, makes up for the deficiency of the convolutional neural network. Therefore, a multi-scale adaptive segmentation network model (MSST-Net) based on a Swin Transformer is proposed in this paper. Firstly, a Swin Transformer is used as the backbone to encode the input image. Then, the feature maps of different levels are decoded separately. Thirdly, the convolution is used for fusion, so that the network can automatically learn the weight of the decoding results of each level. Finally, we adjust the channels to obtain the final prediction map by using the convolution with a kernel of 1 × 1. By comparing this with other segmentation network models on a WHU building data set, the evaluation metrics, mIoU, F1-score and accuracy are all improved. The network model proposed in this paper is a multi-scale adaptive network model that pays more attention to the global features for remote sensing segmentation.
GRAPH NEURAL NETWORK BASED MULTI-FEATURE FUSION FOR BUILDING CHANGE DETECTION 査読有り

W. Yuan, X. Yuan, Z. Fan, Z. Guo, X. Shi, J. Gong, R. Shibasaki

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences　{XLIII}-B3-2021　(B3-2021)　377-382　2021年6月28日
出版者・発行元： Copernicus {GmbH}
DOI： 10.5194/isprs-archives-xliii-b3-2021-377-2021 　

ISSN：2194-9034
END-TO-END BUILDING CHANGE DETECTION MODEL IN AERIAL IMAGERY AND DIGITAL SURFACE MODEL BASED ON NEURAL NETWORKS 査読有り

X. Lian, W. Yuan, Z. Guo, Z. Cai, X. Song, R. Shibasaki

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences　{XLIII}-B2-2020　(B2)　1239-1246　2020年8月14日
出版者・発行元： Copernicus {GmbH}
DOI： 10.5194/isprs-archives-xliii-b2-2020-1239-2020 　

ISSN：2194-9034
UNSUPERVISED MULTI-CONSTRAINT DEEP NEURAL NETWORK FOR DENSE IMAGE MATCHING 査読有り

W. Yuan, Z. Fan, X. Yuan, J. Gong, R. Shibasaki

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences　{XLIII}-B2-2020　(B2)　163-167　2020年8月12日
出版者・発行元： Copernicus {GmbH}
DOI： 10.5194/isprs-archives-xliii-b2-2020-163-2020 　

ISSN：2194-9034
Multimodal Interaction-Aware Trajectory Prediction in Crowded Space 査読有り

Xiaodan Shi, Xiaowei Shao, Zipei Fan, Renhe Jiang, Haoran Zhang, Zhiling Guo, Guangming Wu, Wei Yuan, Ryosuke Shibasaki

Proceedings of the AAAI Conference on Artificial Intelligence　34　(07)　11982-11989　2020年4月3日
出版者・発行元： Association for the Advancement of Artificial Intelligence ({AAAI})
DOI： 10.1609/aaai.v34i07.6874 　

ISSN：2159-5399
Super-resolution integrated building semantic segmentation for multi-source remote sensing imagery 査読有り

Guo, Z., Wu, G., Song, X., Yuan, W., Chen, Q., Zhang, H., Shi, X., Xu, M., Xu, Y., Shibasaki, R., Shao, X.

IEEE Access　7　99381-99397　2019年

DOI： 10.1109/ACCESS.2019.2928646 　

ISSN：2169-3536
Research developments and prospects on dense image matching in photogrammetry,航摄影像密集匹配的研究进展与展望査読有り

Yuan, X., Yuan, W., Xu, S., Ji, Y.

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica　48　(12)　1542-1550　2019年

DOI： 10.11947/j.AGCS.2019.20190453 　

ISSN：1001-1595
Dense image-matching via optical flow field estimation and fast-guided filter refinement 査読有り

Yuan, W., Yuan, X., Xu, S., Gong, J., Shibasaki, R.

Remote Sensing　11　(20)　2410-2410　2019年

DOI： 10.3390/rs11202410 　

ISSN：2072-4292
Semantic segmentation for urban planning maps based on U-Net 査読有り

Zhiling Guo, Hiroaki Shengoku, Guangming Wu, Qi Chen, Wei Yuan, Xiaodan Shi, Xiaowei Shao, Yongwei Xu, Ryosuke Shibasaki

International Geoscience and Remote Sensing Symposium (IGARSS)　2018-July　6187-6190　2018年10月31日

DOI： 10.1109/IGARSS.2018.8519049 　
An Automatic Detection Method of Mismatching Points in Remote Sensing Images Based on Graph Theory,基于图论的遥感影像误匹配点自动探测方法査読有り

Yuan, X., Yuan, W., Chen, S.

Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University　43　(12)　1854-1860　2018年

DOI： 10.13203/j.whugis20180154 　

ISSN：1671-8860
Automatic building segmentation of aerial imagery usingmulti-constraint fully convolutional networks 査読有り

Wu, G., Shao, X., Guo, Z., Chen, Q., Yuan, W., Shi, X., Xu, Y., Shibasaki, R.

Remote Sensing　10　(3)　407-407　2018年
出版者・発行元： {MDPI} {AG}
DOI： 10.3390/rs10030407 　

ISSN：2072-4292
Matching multi-sensor remote sensing images via an affinity tensor 査読有り

Chen, S., Yuan, X., Yuan, W., Niu, J., Xu, F., Zhang, Y.

Remote Sensing　10　(7)　2018年

DOI： 10.3390/rs10071104 　

eISSN：2072-4292
Poor textural image tie point matching via graph theory 査読有り

Yuan, X., Chen, S., Yuan, W., Cai, Y.

ISPRS Journal of Photogrammetry and Remote Sensing　129　21-31　2017年

DOI： 10.1016/j.isprsjprs.2017.04.015 　

ISSN：0924-2716
Optimal seamline detection for orthoimage mosaicking by combining deep convolutional neural network and graph cuts 査読有り

Li, L., Yao, J., Liu, Y., Yuan, W., Shi, S., Yuan, S.

Remote Sensing　9　(7)　701-701　2017年
出版者・発行元： {MDPI} {AG}
DOI： 10.3390/rs9070701 　

ISSN：2072-4292

eISSN：2072-4292
An aerial-image dense matching approach based on optical flow field 査読有り

Wei Yuan, Shiyu Chen, Yong Zhang, Jianya Gong, Ryosuke Shibasaki

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives　41　543-548　2016年

DOI： 10.5194/isprsarchives-XLI-B3-543-2016 　

ISSN：1682-1750
Poor textural image matching based on graph theory 査読有り

Shiyu Chen, Xiuxiao Yuan, Wei Yuan, Yang Cai

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives　41　741-747　2016年

DOI： 10.5194/isprsarchives-XLI-B3-741-2016 　

ISSN：1682-1750

︎全件表示 ︎最初の5件までを表示

MISC 1

Exploring intercity regional similarity using worldwide location-based social network data (demo paper).

Zipei Fan, Guixu Lin, Wei Yuan, Ryosuke Shibasaki, Pengpeng E, Xuan Song 0001

SIGSPATIAL/GIS　104-4　2022年

DOI： 10.1145/3557915.3561041

講演・口頭発表等 22

Flood Depth Mapping from SAR Imagery Using CS-Mamba with DEM Sensitivity Analysis

Zhongyuan Yang, Wei Yuan, Weihang Ran, Changqing Liu, Bruno Adriano, Ryosuke Shibasaki, Shunichi Koshimura

ISPRS 2026　2026年7月12日
Kinematic Characteristics and Risk Analysis of Potential Rockfall based on 3D Point Clouds

Wei Yuan, Changqing Liu, Han Bao, Weihang Ran, Zhongyuan Yang, Xiuxiao Yuan, Ryosuke Shibasaki, Shunichi Koshimura

ISPRS 2026　2026年7月12日
From 2D Imagery to 3D Spatialtemporal Understanding Advances of Photogrammetry and Remote Sensing in Disaster Assesment and Urban Change Monitoring 招待有り

Wei Yuan

2025年11月12日
PortVIS: An Interactive Platform for Port-to-Port Trajectory Imputation and Visual Analytics

Zhiwen Zhang, Zipei Fan, Wei Yuan, Shun Iwazaki, Ryosuke Shibasaki

SIGSPATIAL 2025　2025年11月5日
You Always Recognize Me (YARM): Robust Texture Synthesis Against Multi-View Corruption

Weihang Ran, Wei Yuan, Yinqiang Zheng

Forty-second International Conference on Machine Learning　2025年7月14日
Multi-source 3D point clouds fusion for potential rock mass hazard evaluation in high-steep rock slopes

Wei Yuan, Changqing Liu, Tianyi Wang, Bruno Adriano, Han Bao, Ryosuke Shibasaki, Shunichi Koshimura

ISPRS Geospatial Week 2025 Dubai　2025年4月8日
The Performance of the Optical Flow Field based Dense Image Matching for UAV Imagery

Wei Yuan, Weihang Ran, Bruno Adriano, Ryosuke Shibasaki, Shunichi Koshimura

ISPRS Technical Commission IV Symposium 2024　2024年10月22日
Active Polygon-based Building Outline Extraction from High-resolution Aerial Images

Weihang Ran, Wei Yuan, Zipei Fan, Xiaodan Shi, Ryosuke Shibasaki

ISPRS Geospatial Week 2023　2023年9月5日
Hybrid Feature Embedding for Automatic Building Outline Extraction

Weihang Ran, Wei Yuan, Xiaodan Shi, Zipei Fan, Ryosuke Shibasaki

IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium　2023年7月16日
Graph Encoding based Hybrid Vision Transformer for Automatic Road Network Extraction

Wei Yuan, Weihang Ran, Xiaodan Shi, Zipei Fan, Yang Cai, Ryosuke Shibasaki

IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium　2023年7月16日
Few-Shot Depth Completion Using Denoising Diffusion Probabilistic Model

Weihang Ran, Wei Yuan, Ryosuke Shibasaki

IEEE/CVF Conference on Computer Vision and Pattern Recognition　2023年6月
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin

Zipei Fan, Xiaojie Yang, Wei Yuan, Renhe Jiang, Quanjun Chen, Xuan Song, Ryosuke Shibasaki

The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2022　2022年11月3日
Exploring Intercity Regional Similarity using Worldwide Location-based Social Network Data

Zipei Fan, Guixu Lin, Wei Yuan, Ryosuke Shibasaki, Pengpeng E, Xuan Song

The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2022　2022年11月2日
Cross-Scale Attention-based Tree Crown Detection via UAV imagery

Wei Yuan, Xiaodan Shi, Zhiling Guo, Zipei Fan, Jianya Gong, Ryosuke Shibasaki

IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium　2022年7月19日
Learning Social Complaint Multi-Modal Distributions of Human Path in Crowds

Xiaodan Shi, Haoran Zhang, Wei Yuan, Dou Huang, Zhiling Guo, Ryosuke Shibasaki

XXIVth ISPRS congress　2022年7月8日
Graph Neural Network based Multi-Feature Fusion for Building Change Detection

Wei Yuan, Xiuxiao Yuan, Zipei Fan, Zhiling Guo, Xiaodan Shi, Jianya Gong, Ryosuke Shibasaki

XXIVth ISPRS congress　2021年7月7日
End-to-end Building Change Detection Model in Aerial Imagery and Digital Surface Model based on Neural Networks

Xinlei Lian, Wei Yuan, Zhiling Guo, Zekun Cai, Xuan Song, Ryosuke Shibasaki

XXIVth ISPRS Congress　2020年9月1日
Unsupervised Multi-Constraint Deep Neural Network for Dense Image Matching

Wei Yuan, Zipei Fan, Xiuxiao Yuan, Jianya Gong, Ryosuke Shibasaki

XXIVth ISPRS Congress　2020年9月2日
Multimodal Interaction-aware Trajectory Prediction in Crowded Space

Xiaodan Shi, Xiaowei Shao, Zipei Fan, Renhe Jiang, Haoran Zhang, Zhiling Guo, Guangming Wu, Wei Yuan, Ryosuke Shibasaki

The Thirty-Fourth AAAI Conference on Artificial Intelligence　2020年2月10日
Semantic Segmentation for Urban Planning Maps based on U-Net

Zhiling Guo, Hiroaki Shengoku, Guangming Wu, Qi Chen, Wei Yuan, Xiaodan Shi, Xiaowei Shao, Yongwei Xu, Ryosuke Shibasaki

IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium　2018年7月25日
Poor Textural Image Matching based on Graph Theory

Shiyu Chen, Xiuxiao Yuan, Wei Yuan, Yang Cai

XXIIIth ISPRS Congress　2016年7月15日
An Aerial-Image Dense Matching Approach based on Optical Flow Field

Wei Yuan, Shiyu Chen, Yong Zhang, Jianya Gong, Ryosuke Shibasaki

XXIII ISPRS Congress　2016年7月14日

︎全件表示 ︎最初の5件までを表示

共同研究・競争的資金等の研究課題 11

Physics Constraints Aware 2D Semantic Perception and Rapid 3D Gaussian Splatting for Quantitative Urban Disaster Assessment

袁巍

提供機関：Ministry of Education, Culture, Sports, Science and Technology

制度名：Strategic Professional Development Program for Young Researchers

研究種目：TI-FRIS

研究機関：Tohoku University

2026年7月～ 2031年3月
AIエージェントを活用した洪水深度予測のための標準化マルチモーダルデータセット構築

袁巍

提供機関：Ministry of Education, Culture, Sports, Science and Technology

制度名：AI for Science

研究種目：SPReAD 1000

研究機関：Tohoku University

2026年9月～ 2027年3月
Multi-task learning based post-disaster mapping via multi-modal remote sensing observations

袁巍

2023年4月1日～ 2026年3月31日
Crowd Prediction and Simulation in Disaster Scenarios Based on Geographical Foundation Models

Yao Yao, Wei Yuan

提供機関：International Research Institute of Disaster Science， Tohoku University

制度名：Disaster Resilience Co-Creation Research Project

2025年6月～ 2026年3月
Typhoon-Focused Maritime Disaster Data Platform

Zhiwen Zhang, Wei Yuan

提供機関：International Research Institute of Disaster Science， Tohoku University

制度名：Disaster Resilience Co-Creation Research Project

2025年6月～ 2026年3月
Citywide Digital Twin System for Human Moblity Simulation and Prediction under Extreme Weather

Wei Yuan, Zipei Fan

提供機関：International Research Institute of Disaster Science， Tohoku University

制度名：Disaster Resilience Co-Creation Research Project

2025年6月～ 2026年3月
Resilience patterns of multiscale human mobility under extreme rainfall events using massive individual trajectory data

Yao Yao, Wei Yuan

提供機関：International Research Institute of Disaster Science， Tohoku University

制度名：Disaster Resilience Co-Creation Research Project

2024年6月～ 2025年3月
Foundation modal based multi-modal data fusion for efficient disaster response

Wei Yuan, Zipei Fan

提供機関：International Research Institute of Disaster Science， Tohoku University

制度名：Disaster Resilience Co-Creation Research Project

2024年6月～ 2025年3月
Heterogeneous Graph Neural Network based Federated Mobile Crowdsensing

FAN ZIPEI, YUAN WEI

提供機関：Japan Society for the Promotion of Science

制度名：Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

研究種目：Grant-in-Aid for Scientific Research (B)

研究機関：The University of Tokyo

2022年4月～ 2025年3月
衛星からの不動産価格マッピングとその利用可能性に関する研究

柴崎亮介, Seetharam KE, 袁巍

提供機関：Japan Society for the Promotion of Science

制度名：Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)

研究種目：Grant-in-Aid for Scientific Research (B)

研究機関：The University of Tokyo

2019年4月～ 2022年3月

詳細を見る詳細を閉じる

新型コロナにより発展途上国の都市地価データの収集に大きな障害が生じたため、都市環境を衛星画像から詳細に取得するための衛星画像の解析技術の高度化に焦点をあて、国内での地価データ等と関連を明らかにした。高分解能画像による建物の抽出や変化検出技術の高度化し、さらにフリーの中分解能衛星画像を主に利用して住環境劣悪地域（地価が顕著に安い地域）を自動抽出する技術を開発した。これらの方法は大幅な精度向上を達成できることがわかった。さらに国内で利用可能な地価データ等を大量に取得し、衛星画像から得られる環境因子との関連分析を行った。これらから、我々は衛星画像から途上国大都市での地価推計を行える見通しを得た。
Developing a Mobile Phone Network Data-Driven Methodology to Quantify Community Resilience in Disaster Affected Areas

Shohei Nagata, Cynthia Chen, Erick Mas, Wei Yuan, Shunichi Koshimura, Lyra Chen

提供機関：Tohoku University

制度名：Tohoku University-University of Washington Strategic Partner Fund 2024-25

︎全件表示 ︎最初の5件までを表示

学術貢献活動 5

IGARSS 2026

2026年8月9日～ 2026年8月14日

学術貢献活動種別: 大会・シンポジウム等
IGARSS 2025

2025年8月3日～ 2025年8月8日

学術貢献活動種別: 大会・シンポジウム等
IGARSS 2024

2024年7月7日～ 2024年7月12日

学術貢献活動種別: 大会・シンポジウム等
ISPRS geospatial week

学術貢献活動種別: 大会・シンポジウム等
IGARSS 2023

学術貢献活動種別: 査読等