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Flood Assessment Method for Heritage Conservation at the Site Scale: A Case Study of PuZhou Ancient City Site, China 45

Flood Assessment Method for Heritage Conservation at the Sit...

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45th Asian Conference on Remote Sensing, ACRS 2024

作者： Zhu, Z.X. Dong, Y.Q. Hou, M.L. School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture China

The concept of "preventive conservation" has become the focus of heritage conservation worldwide, risk assessment is one of the important elements of preventive conservation. Current flood risk assessments tend to focus on larger scales such as cities or river basins and fail to adequately refine the risk to individual buildings. This paper presents a flood risk assessment methodology tailored to the scale of cultural heritage units, to enhance the accuracy of identifying site-specific risks. The method couples hazard with vulnerability to assess flood risk. Regarding hazard, to solve the issue of the minimal contribution of slight geographical variations in water and environmental indicators at the scale of cultural heritage units to the classification of storm flood danger, we employ the Storm Water Management Model (SWMM). This model is utilized to establish rainfall scenarios and conduct numerical simulations, translating these into intuitive risk parameters like depth and extent of inundation. Additionally, a vulnerability assessment procedure for cultural heritage was designed. This procedure analyzes structural stability using finite element simulations, quantifies the sensitivity of cultural objects to floods, evaluates exposure based on potential value loss, and assesses disaster prevention and mitigation capacity considering protective measures and restoration possibilities. Flood risk can be obtained by weighing the results of hazard and vulnerability analyses. The case study of the ancient city site of Puzhou verifies the feasibility of the risk assessment methodology adopted and reveals the risk level of different areas. The results of the assessment show that the Xicheng District, except for the southern city wall, has a low flood risk and good resilience to flooding. The Drum Tower area has a damaged foundation but is on higher ground and has a lower flood risk. The Eastern District has important cultural relics, the low-lying topography makes it high

关键词： Risk assessment

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Change Detection of Open Pit Mining Areas in Remote Sensing Images Based on Coupled Local-Global Features 5

Change Detection of Open Pit Mining Areas in Remote Sensing ...

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5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Wang, Xiaolei Lv, Jingguo Zhang, Xiaojuan Zhang, Aiyuan School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9798331530334

In this paper, a coupled local-global feature-based change detection method Swin-LGCNet is proposed for remote sensing image open pit mining areas The model interactively fuses local and global features extracted by ResNet and Swin Transformer through the local-global feature coupling module, which effectively improves the feature expression capability. The Mish activation function is used instead of ReLU, which reduces the information loss and enhances the model's ability to capture complex changing features. Meanwhile, the differential operation is introduced to further enhance the difference between the dual-time phase features and significantly reduce the leakage detection rate. The experimental results show that Swin-LGCNet outperforms the traditional network in both global information extraction and local detail performance, and the detection accuracy is significantly improved compared with the comparison networks, such as PSPNet, Deeplabv3+, UNet and UNet++, especially in boundary identification and noise suppression. The method makes full use of the temporal sequence of remote sensing images, and provides efficient technical support for dynamic change monitoring and ecological protection in open pit mining areas. © 2024 IEEE.

关键词： Change detection

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Urban modern architecture recognition based on Mask-RCNN and ECA attention mechanism 5

Urban modern architecture recognition based on Mask-RCNN and...

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5th International Conference on Geoscience and Remote Sensing Mapping, ICGRSM 2023

作者： Zhu, Xiaofan Hu, Lujin Wang, Jian School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9781510672789

Modern architecture plays an essential role in the fields of land surveying and mapping, urban planning and change detection. Aiming at the problems of large workload, long cycle and poor timeliness of traditional field surveying and mapping of modern architectures, this paper proposes the Mask-RCNN network model and introduce the ECA attention mechanism to reflect the self-made street view image data set of building facade information, to quickly and accurately identify urban modern architectures. And compared with SVM, U-net and Mask-RCNN building extraction algorithms. Experiments show that the proposed method can extract modern architectures efficiently and accurately. For the same data set, the extraction result is 2.6 % higher than the original Mask-RCNN algorithm and is better than the comparison algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Extraction

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Research on the center of mass calculation method for dynamic tilting of terracotta warriors from the mausoleum of Emperor Qin Shihuang

Research on the center of mass calculation method for dynami...

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2024 International Conference on Optoelectronic Materials and Devices, ICOMD 2024

作者： Wang, Jinwen Hu, Yungang School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (数字)9781510689046

ISBN: (纸本)9781510689039

The Terracotta Warriors excavated from the mausoleum of Qin Shi Huang are predominantly anthropomorphic and are often found in a fragmented state. Therefore, studying the stability of the reassembled warriors is crucial for artifact preservation and restoration, including thickness measurement, center of mass analysis, and stability assessment. This paper introduces a method for reconstructing terracotta warrior fragments based on 3D laser scanning technology. First, point cloud data from the fragment surfaces is collected using a 3D laser scanner. Since scanning only captures surface information, an improved Moller-Trumbore algorithm is employed to fill the internal spaces. This involves generating bounding boxes for each fragment and randomly distributing point clouds within these spaces, combined with ray casting to achieve digital reconstruction of the internal structures. Based on this, two key analyses are performed: first, predicting the overall thickness of the assembled warriors through regional measurement and integration of thickness data;second, calculating the center of mass of the assembled warriors using both voxel and tetrahedral methods to ensure accuracy. Additionally, stability assessments are conducted for various tilting directions. Experimental results indicate that the voxel method outperforms the tetrahedral method in precision, with analysis revealing that the right leg and right side of the waist bear significant stress. Thus, during the restoration process, it is essential to enhance support for these areas to ensure the safety and stability of the artifacts. © 2025 SPIE.

关键词： Restoration

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Research on Deep Learning-Based Vehicle and Pedestrian Object Detection Algorithms 8

Research on Deep Learning-Based Vehicle and Pedestrian Objec...

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8th International Conference on Smart Data and Smart Cities, SDSC 2024

作者： Zhang, Xin Huang, He Yang, Junxing Jiang, Shan School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

As urbanization accelerates, traffic congestion and frequent accidents have become prominent issues, prompting the development of intelligent transportation systems. This paper focuses on the research of vehicle and pedestrian detection algorithms to improve detection accuracy in complex traffic environments. Considering the limitations of traditional object detection algorithms in complex situations, this study adopts the deep learning-based YOLOv8 algorithm and introduces the Coordinate Attention (CA) module to enhance the model's feature extraction and localization capabilities. Experimental results show that the improved YOLOv8 network achieves a 1.1% increase in detection accuracy while maintaining its original speed. Furthermore, this paper constructs a vehicle and pedestrian dataset suitable for Chinese traffic scenes, providing an effective solution for autonomous driving assistance systems. Overall, this study holds significant reference value for vehicle and pedestrian detection in the field of intelligent transportation. © Author(s) 2024. CC BY 4.0 License.

关键词： Traffic congestion

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Innovative Research on Small Object Detection and Recognition in Remote Sensing Images Using YOLOv5 8

Innovative Research on Small Object Detection and Recognitio...

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8th International Conference on Smart Data and Smart Cities, SDSC 2024

作者： Jiang, Shan Huang, He Yang, Junxing Zhang, Xin Wang, Siqi School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

With the increase of remote sensing image acquisition methods and the number of remote sensing image data, the traditional manual annotation and recognition methods can no longer meet the needs of the present production life. This study explores the use of deep learning techniques to improve the efficiency and accuracy of target detection in remote sensing satellite images, especially for small targets. Traditional target detection methods often face challenges in recognition accuracy and processing speed due to the specificity and complexity of satellite images, such as large size, variable lighting conditions and complex background. Therefore, this paper adopts the YOLOv5 model and introduces the CBAM (Convolutional Block Attention Module) attention mechanism, which significantly improves the detection of small and dense targets. Experimental validation, using the improved YOLOv5 model on the VisDrone2021 dataset, demonstrates that the model improves the mean average percision (mAP) by 1.9% while maintaining real-time performance. This paper provides new ideas for remote sensing image processing, especially for applications in the fields of urban planning and automatic driving, etc. Despite the progress made in this study, the detection of small targets in remote sensing images, the limited classification accuracy and the detection of dynamic targets still need further research. © Author(s) 2024. CC BY 4.0 License.

关键词： Satellite imagery

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RESEARCH ON HIGH-FREQUENCY ODOMETRY IN LASER SLAM USING POINTSUBMAP MATCHING 8

RESEARCH ON HIGH-FREQUENCY ODOMETRY IN LASER SLAM USING POIN...

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8th International Conference on Smart Data and Smart Cities, SDSC 2024

作者： Bian, Junyang Huang, He Yang, Junxing Wang, Siqi Zhang, Xin School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

The stability of front-end odometry output is the cornerstone of real-time mapping and high-precision localization. However, in autonomous driving, factors such as sensor types, driving speed, and signal occlusion can impact system performance, particularly causing trajectory drift, reduced accuracy, and decreased reliability during high-speed travel. This study employs a point-by-point update approach for odometry, achieving high-frequency odometry output and mapping updates nearly matching the point sampling rate, effectively eliminating point cloud distortions and providing solid support for tracking high-speed motion and navigation localization. This method emphasizes maintaining registration accuracy and robustness under high-frequency output conditions. Moreover, by utilizing backward propagation to optimize the estimation of posture and position, and ignoring inter-frame processing, this research implements an iterative extended Kalman filter, achieving tight coupling with IMU data. Additionally, the use of the iVox data structure accelerates point cloud searching, enhancing system processing efficiency. Testing in various scenarios has demonstrated that our method exhibits superior performance and robustness across different scene data, showcasing its potential application in the field of autonomous driving. © Author(s) 2024. CC BY 4.0 License.

关键词： Mapping

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3D Reconstruction of Buildings Based on 3D Gaussian Splatting 8

3D Reconstruction of Buildings Based on 3D Gaussian Splattin...

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8th International Conference on Smart Data and Smart Cities, SDSC 2024

作者： Cai, Zhenglong Yang, Junxing Wang, Tian jiao Huang, He Guo, Yue School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

In the current era of urban construction, smart city management, and digital twinning, three-dimensional reconstruction of urban buildings is particularly important. Traditional methods have limitations in reconstructing complex geometric scenes, while new methods such as Nerf focus on using implicit MLP to represent the geometric space of the model, but suffer from slow training and rendering speeds. To address this issue, this paper proposes the use of 3D Gaussian scatter points for three-dimensional reconstruction of urban buildings, improving training speed and reconstruction quality through optimized and accelerated rendering algorithms. This method demonstrates high efficiency and editability, providing a new solution for urban building reconstruction. © Author(s) 2024. CC BY 4.0 License.

关键词： 3D modeling

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Triangle Descriptor Loop Detection Method Based on Faster-LIO 8

Triangle Descriptor Loop Detection Method Based on Faster-LI...

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8th International Conference on Smart Data and Smart Cities, SDSC 2024

作者： Wang, Siqi Huang, He Yang, Junxing Bian, Junyang Jiang, Shan School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

This paper introduces an enhanced approach to loop closure detection in Simultaneous Localization and Mapping (SLAM) by integrating the Faster-LIO framework with Stable Triangle Descriptors (STD). SLAM, essential in autonomous driving and augmented reality, often encounters cumulative errors affecting mapping accuracy. Traditional detection methods based on sensor data like camera images and LiDAR point clouds struggle with environmental changes that alter scene appearance and geometry. Our approach utilizes LiDAR sensors and STD, exploiting the geometric stability of triangles to maintain robustness against rotational and translational changes. The process involves storing triangle descriptors from key frames in a hash table within the Faster-LIO framework, a voxel-based LiDAR-Inertial Odometry optimized for efficiency and speed. These descriptors are then matched across frames using a voting mechanism to ensure reliable loop closure detection. Validation on the KITTI dataset and a proprietary subterranean parking garage dataset demonstrates that this integration not only enhances loop closure detection but also simplifies computational demands by avoiding complex tree structures. This method shows promise for broader applications in robotics and autonomous systems, with future research focusing on refining the descriptor and expanding its applicability to other sensor modalities. © Author(s) 2024. CC BY 4.0 License.

关键词： Augmented reality

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Smartphone sensor signal based tool construction and data analysis 4

Smartphone sensor signal based tool construction and data an...

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4th International Conference on Sensors and Information Technology, ICSI 2024

作者： Zeng, Baoshan School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9781510678699

ISBN: (纸本)9781510678682

With the continuous improvement of smartphone configurations and performance, the accuracy of built-in sensors in smartphones has also been significantly enhanced. The collection, analysis, and application of smartphone sensor signals have become increasingly important. In this paper, we start with several common sensors in smartphones and design and develop a signal collection and storage tool based on the built-in sensors of smartphones. Using this tool, sensor signals are collected in different motion states and various scene positions, and then undergo data processing and analysis. Through the analysis of signal data, it can be observed that the signal characteristics of smartphone sensors exhibit significant correlations with different motion states and scenes. By perceiving the signals, it is possible to determine the pedestrian's motion state and scene location, which can be applied to various smartphone-based practical applications. © 2024 SPIE.

关键词： Signal analysis

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