Article
Geography, Physical
Kenneth Olofsson, Eva Lindberg, Johan Holmgren, Raul de Paula Pires, Henrik Jan Persson
Summary: This study proposes a car-mounted mobile laser scanner method for individual tree detection and stem attribute estimation, aiming to improve remote sensing-based forest inventories. The results show that this method can accurately retrieve tree information at different distances from the roadside, making it suitable for large-scale forest inventories.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Construction & Building Technology
Manohar Yadav, Parvej Khan, Ajai Kumar Singh, Bharat Lohani
Summary: A hybrid ground filtering method was proposed for processing ground points in mobile laser scanning data. After testing and validation, the method showed good performance in various challenging roadway environments. The method is straightforward, computationally efficient, and has the potential for wider application in industry.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Environmental Sciences
Xiaojuan Ning, Yishu Ma, Yuanyuan Hou, Zhiyong Lv, Haiyan Jin, Yinghui Wang
Summary: In this paper, a method for detecting trees from MLS point cloud data is proposed, which achieves accurate extraction of trees in different environments through tree top point extraction and radius expansion.
Article
Chemistry, Analytical
Marius M. Kostelic, Ciara K. Zak, Yang Liu, Victor Shugui Chen, Zhuchun Wu, Jared Sivinski, Eli Chapman, Michael T. Marty
Summary: This study developed UniDecCD software for computational deconvolution of CD-MS data to overcome charge state uncertainty and significantly improve the resolution, which is crucial for analyzing proteins, viral capsids, and nanodiscs using CD-MS technology.
ANALYTICAL CHEMISTRY
(2021)
Article
Construction & Building Technology
Chenli Wang, Jun Jiang, Thomas Roth, Cuong Nguyen, Yuhong Liu, Hohyun Lee
Summary: This paper introduces a cost-effective approach to occupancy detection using a two-layer detection scheme based on data obtained from multiple non-intrusive sensors. Machine learning is utilized for data fusion, enhancing the validity and reliability of occupancy detection. The proposed system shows significant improvements in accuracy and F1-score compared to the current state-of-the-art approach.
ENERGY AND BUILDINGS
(2021)
Article
Computer Science, Artificial Intelligence
Prarthi Jain, Seemandhar Jain, Osmar R. Zaiane, Abhishek Srivastava
Summary: PiForest is an effective anomaly detection method designed for resource-constrained environments and streaming data. By employing a pre-processing stage and a sliding window mechanism, PiForest can identify anomalies efficiently while reducing storage and prediction complexity.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
Article
Engineering, Electrical & Electronic
Haipeng Wang, Yibin Liu
Summary: A multisensor data fusion algorithm is proposed for life detection in cluttered environments. The algorithm combines data from different sensors and utilizes preprocessing and fusion techniques to improve the success rate of life detection. The results show high accuracies under various conditions.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Software Engineering
Shao-Kui Zhang, Hou Tam, Yi-Xiao Li, Tai-Jiang Mu, Song-Hai Zhang
Summary: This article presents SceneViewer, an integrated system for automatic view selections in 3D scenes. By applying rules of interior photography, the system guides the selection of views and improves the visual experience.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2023)
Article
Thermodynamics
Zhenxin Zhou, Huanxin Chen, Guannan Li, Hanlu Zhong, Menghua Zhang, Junfeng Wu
Summary: The data-driven approach is important for fault detection and diagnosis in variable refrigerant flow systems, but imbalanced class distributions in datasets can impact classification accuracy. A study was conducted to address this issue, concluding that fault detection accuracy in VRF systems is significantly reduced in imbalanced data environments. The proposed PCA-SMOTE method demonstrated improvement and versatility in overcoming this challenge.
INTERNATIONAL JOURNAL OF REFRIGERATION
(2021)
Article
Computer Science, Information Systems
Lazaro Bustio-Martinez, Miguel A. Alvarez-Carmona, Vitali Herrera-Semenets, Claudia Feregrino-Uribe, Rene Cumplido
Summary: Phishing is a cyber-attack that preys on victims' technical ignorance or naivety and is now increasingly targeting the Internet of Things (IoT) field. While there have been approaches for detecting phishing attacks, research on selecting the most suitable feature set for IoT environments is limited.
INFORMATION SCIENCES
(2022)
Article
Development Studies
L. Marek, S. Hills, J. Wiki, M. Campbell, M. Hobbs
Summary: This nationwide geospatial study from Aotearoa New Zealand examines the frequency and spatial distribution of residential mobility and its relationship with the environment. Using data from the Integrated Data Infrastructure, levels of residential mobility were defined from 2016 to 2020. The study identifies spatial clusters and five groups based on residential mobility characteristics, and explores their relation to the Healthy Location Index, urbanicity, and ethnicity. Areas with high residential mobility are associated with better access to health-promoting environments.
HABITAT INTERNATIONAL
(2023)
Article
Computer Science, Information Systems
Taehun Yang, Sang-Hoon Lee, Soochang Park
Summary: This paper proposes a novel individual monitoring system based on edge intelligence, which utilizes a user state detection mechanism and fine-grained localization scheme to monitor the coexistence of users with smart mobile devices for user activity recognition and localization accuracy.
Article
Environmental Sciences
Chun Liu, Yuanfan Qi, Hangbin Wu, Youyuan Li, Zhanyong Fan
Summary: This article develops a pipeline-based method to obtain street tree maintenance information by analyzing the spatial relationship between street trees and clearance pipelines using MLS data. The results show that this method can effectively detect obstructive objects and improve the visibility of traffic signs by pruning the street trees. This study highlights the potential of MLS data in urban facilities management and aims to provide a safer road environment.
GEOCARTO INTERNATIONAL
(2022)
Article
Remote Sensing
Michele Dalponte, Yady Tatiana Solano-Correa, Hans Ole Orka, Terje Gobakken, Erik Naesset
Summary: This study explored the possibility of using multi-temporal and multispectral satellite data to detect rot presence in Norway spruce trees. The results showed an underestimation of the rot presence, but the method can be used to provide a tentative map of the rot presence.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Engineering, Multidisciplinary
Jintao Li, Xiaojun Cheng, Zhihua Xiao
Summary: The study proposes a branch-trunk-constrained hierarchical clustering method to individually extract street trees from mobile laser scanning point clouds, achieving high extraction precision and recall rates.
Article
Environmental Sciences
Shaobo Xia, Dong Chen, Jiju Peethambaran, Pu Wang, Sheng Xu
Summary: This study aims to make top-based methods applicable to TLS forest scenes by proposing a novel point cloud transformation. The method is tested on an international benchmark, demonstrating its necessity and effectiveness without requiring additional preprocessing steps. It has the potential to benefit other object localization tasks in different scenes based on detailed analysis and tests.
Article
Geochemistry & Geophysics
Shaobo Xia, Sheng Nie, Pu Wang, Dong Chen, Sheng Xu, Cheng Wang
Summary: The proposed gap-based data dividing method aims to minimize the intersections between cutting lines and objects. Experimental results demonstrate that this method outperforms the baseline method in terms of visual inspection and cutting line quality.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Sheng Xu, Xuan Zhou, Weidu Ye, Qiaolin Ye
Summary: This study introduces a new augmented convolutional neural network (ACNN) for point cloud classification, which enhances local structure information. Adaptive learning of parameters and adjustment of smoothness play a significant role during the learning process, showcasing high robustness in point cloud processing.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Sheng Xu, Wen Han, Weidu Ye, Qiaolin Ye
Summary: In this letter, a semi-automatic method is proposed to extract desired segmentation contours by initializing, calculating internal and external forces, and solving deformation equations. Experimental results demonstrate the effectiveness of the method in terms of accuracy and consistency, outperforming selected primitive- and object-based methods.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Shaobo Xia, Sheng Xu, Ruisheng Wang, Jonathan Li, Guanghui Wang
Summary: This study presents a method to extract individual buildings from ALS point clouds using widely accessible polygonal footprints. The method can achieve high instance-level building mapping accuracy around 90% and future work will focus on improving classification errors in preprocessing, shape inconsistencies between point clouds and polygons, as well as building footprint delineation and updating in postprocessing.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Sheng Xu, Xin Li, Jiayan Yun, Shanshan Xu
Summary: This paper proposes a four-step framework for tree skeleton extraction, achieving complete skeletons through optimizing paths and interpolating points, and providing an efficient solution for tree skeleton and structure study.
Article
Computer Science, Artificial Intelligence
Fa Zhu, Junbin Gao, Jian Yang, Ning Ye
Summary: Linear Discriminant Analysis (LDA) assumes samples from the same class are independently and identically distributed, which may lead to failure when there are multiple clusters within a class. This paper proposes a neighborhood linear discriminant analysis (nLDA) that defines scatter matrices based on a neighborhood of reverse nearest neighbors, eliminating the need for the i.i.d. assumption. Experimental results show that nLDA outperforms previous discriminators in terms of performance.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Wei Zheng, Shuo Chen, Zhenyong Fu, Fa Zhu, Hui Yan, Jian Yang
Summary: FSBUF is a novel embedded framework for feature selection that improves the generalization ability of traditional embedded methods by introducing an additional classifier for unselected features. Experimental results demonstrate its comprehensibility and superior performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Geochemistry & Geophysics
Xin Li, Xuan Zhou, Sheng Xu
Summary: In this study, we propose an innovative method for obtaining the complete skeletons and 3-D structures of individual trees using point clouds. Our method extracts individual trees from input street scenes and segments them into small successive pieces, with the centers of each piece serving as skeleton candidate points. Through interpolation based on Euclidean distance and orientation, the entire skeleton is obtained. Finally, a high-precision 3-D model of trees is constructed by cylindrically fitting the skeleton using optimized circular truncated cones. Experimental results show that our method is highly efficient and effective, achieving 98% accuracy and requiring less than 1 minute for reconstruction. Compared to other methods, our approach reduces the time by more than 95%.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Remote Sensing
Sheng Xu, Xin Li, Hongxin Yang, Shanshan Xu
Summary: This work aims to develop a deep learning framework for segmenting woods from tree point clouds. The authors propose a novel preprocessing layer called the projection layer, which transforms 3D point clouds into 2D points for the subsequent convolution process. The projection map is updated in the learning process to capture geometric structure information.
REMOTE SENSING LETTERS
(2023)
Article
Environmental Sciences
Zhouyang Hua, Sheng Xu, Yingan Liu
Summary: This paper proposes an individual tree segmentation method called Shadow-cut to extract the contours of street tree point clouds. The method includes using support vector machine for tree region separation, calculating the optimal projection, and using image segmentation algorithm to extract edges. Experiments demonstrate that this method achieves high accuracy and completeness on LiDAR data.
Article
Computer Science, Artificial Intelligence
Fa Zhu, Xingchi Chen, Shuo Chen, Wei Zheng, Weidu Ye
Summary: As a classical ordinal regression model, support vector ordinal regression (SVOR) finds parallel discriminant hyperplanes to maximize the minimal margins between different ranks. However, SVOR only considers minor patterns near the margin hyperplanes and ignores the contributions of other patterns. To address this issue, this paper proposes relative margin induced support vector ordinal regression (RMSVOR) models, which depict the margin between a pattern and a discriminant hyperplane based on relative margin information. Experimental results on various datasets show that RMSVOR outperforms previous ordinal regression models and canonical multi-class classification models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Sciences
Tianyi Xie, Wen Han, Sheng Xu
Summary: This paper proposes YOLO-RS, an optimized object detection algorithm based on YOLOv4, which improves the detection accuracy and speed by introducing the ASFF structure, optimizing the SPP structure in YOLOv4, and introducing Lightnet.
Article
Geochemistry & Geophysics
Ling Xing, Hongyu Qu, Sheng Xu, Yao Tian
Summary: This article proposes a novel and effective method, named CLEGAN, for unpaired low-light image enhancement using self-similarity contrastive learning within a single GAN framework. The proposed method maximizes the mutual information between low-light and restored images.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Fa Zhu, Wenjie Zhang, Xingchi Chen, Xizhan Gao, Ning Ye
Summary: As a state-of-the-art supervised novelty detection model, SVM-SND can recognize novelty or the class of a test instance through a single model. However, maximizing minimum margin is not sufficient for ensuring classification generalization. This paper introduces margin distribution and proposes an LMD-SND model to enhance the performance of multi-class supervised novelty detection. Experimental results show that LMD-SND outperforms SVM-SND and achieves comparative performance with shallow and deep novelty detection models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)