The point cloud is one of the measurement results of local measurement and is widely used because of its high measurement accuracy, high data density, and low environmental impact. However, since point cloud data from a single measurement are general...
The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although i...
This paper presents a flexible deployment of ultrasonic position sensors and a novel positioning algorithm suitable for the navigation of mobile robots (MRs) in extensive indoor environments. Our proposal uses several independently-referenced local p...
It is important to reduce the danger of collecting flame image data sets by compositing flame images by computer. In this paper, a Global-Local mask Generative Adversarial Network (FGL-GAN) is proposed to address the current status of low quality com...
The accurate estimation of the zero-height geopotential level in a local vertical datum (LVD) is critical for linking traditional height reference systems to a global height system. In this paper, we investigate the theoretical and practical challeng...
Field-road mode classification (FRMC) that identifies “in-field” and “on-road” categories for Global Navigation Satellite System (GNSS) trajectory points of agricultural machinery containing geographic information is essential...
Digital elevation models (DEMs) are the basic data of science and engineering technology research. SRTM and ASTER GDEM are currently widely used global DEMs, and TanDEM-X DEM, released in 2016, has attracted users’ attention due to its unprecedented...
K-means clustering is a fundamental tool in data mining, yet its scalability and efficacy decline when faced with massive datasets. In this work, we introduce BiModalClust, a novel clustering algorithm that leverages a bimodal optimization paradigm t...
The robotic surgery environment represents a typical scenario of human–robot cooperation. In such a scenario, individuals, robots, and medical devices move relative to each other, leading to unforeseen mutual occlusion. Traditional methods use...
Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making...
Spatiotemporal scale is a basic component of geographical problems because the size of spatiotemporal units may have a significant impact on the aggregation of spatial data and the corresponding analysis results. However, there is no clear standard f...
Wastewater treatment plants (WWTPs) play a vital role in controlling wastewater discharge and promoting recycling. Accurate WWTP identification and spatial analysis are crucial for environmental protection, urban planning, and sustainable development...
The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various tem...
The joint classification of hyperspectral imagery (HSI) and LiDAR data is an important task in the field of remote sensing image interpretation. Traditional classification methods, such as support vector machine (SVM) and random forest (RF), have dif...
Light Detection and Ranging (LiDAR) systems become more prevalent in remote sensing for modeling buildings, engineering structures, or their deformations and displacements. Processing data from such systems, usually point clouds, can be performed usi...
In this study, we investigate the diversity and spatiotemporal distribution of gelatinous zooplankton (GZ) in the central Baltic Sea (coastal waters of Gotland and adjacent Eastern and Western Gotland Basins), a region characterised by low salinity a...
Most geophysical inversions face the problem of non-uniqueness, which poses a challenge in the mapping and delineation of the subsurface anomalies. To tackle this challenge, a combined local and global optimization approach is considered for jointly...
Hyperspectral image (HSI) and light detection and ranging (LiDAR) data offer complementary spectral and structural information; however, the integration of these high-dimensional, heterogeneous modalities poses significant challenges. We propose a Gl...
This paper focuses on explaining changes over time in globally sourced annual temporal data with the specific objective of identifying features in black-box models that contribute to these temporal shifts. Leveraging local explanations, a part of exp...
A reliable Digital Elevation Model (DEM) is a key input for land use planning and risk management, particularly in floodplains where low-resolution models often fail to represent subtle topographic variations. In many regions worldwide, high-precisio...
Several studies have attempted to uncover the impact of weather parameters on the coronavirus (COVID-19) pandemic during the initial stage of its outbreak. However, they reported contradicting findings due to limited data available at an earlier stag...
In Latin American cities, violence against women (VAW) remains critical for public health, well-being, and safety. This phenomenon is influenced by social, political, and environmental drivers. VAW is not randomly distributed; built environments&mdas...
This paper presents the Variable Landscape Search (VLS), a novel metaheuristic designed for global optimization of complex problems by dynamically altering the objective function landscape. Unlike traditional methods that operate within a static sear...
With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipp...
Recent years have witnessed a remarkable growth in the way mathematics, informatics, and computer science can process data. In disciplines such as machine learning, pattern recognition, computer vision, computational neurology, molecular biology, inf...
The main geological structures in the Dammam Dome are defined by integrating geophysical measurements and applying new methodological approaches. Dammam Dome is characterized by a well-developed fracture/joints system; thus, high complexity of the su...
A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding...
The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we e...
The relationship between ionospheric research and global navigation satellite systems (GNSS) can be analysed through two approaches. The direct approach utilises ionospheric models to mitigate its influence, while the indirect approach leverages GNSS...
Several global digital elevation models (DEMs) have been developed in the last two decades. The most recent addition to the family of global DEMs is the TanDEM-X DEM. The original version of the TanDEM-X DEM is, however, a nonedited product (i.e., it...
The issue of abandoned cropland poses a significant threat to national food security and the sustainable use of land resources, highlighting the urgent need for an efficient and interpretable remote sensing identification framework. This study integr...
Ground reference data are typically required to evaluate the quality of a supervised image classification analysis used to produce a thematic map from remotely sensed data. Acquiring a suitable ground data set for a rigorous assessment of classificat...
With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time s...
This study investigates the local and global stability of the Luzzone Dam. Two finite element models were built; one with foundation rock, the other without. The purpose of this was to demonstrate a potential gulf between rigid connection modelling,...
Forest disturbances are generally estimated using globally available forest change maps or locally calibrated disturbance maps. The choice of disturbance map depends on the trade-offs among the detection accuracy, processing time, and expert knowledg...
In this paper, we propose a new approach for the measurement and reconstruction of large workpieces with freeform surfaces. The system consists of a handheld laser scanning sensor and a position sensor. The laser scanning sensor is used to acquire th...
Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole l...
Despite the agricultural sector’s consistently high injury rates, formal reporting is often limited, leading to sparse national datasets that hinder effective safety interventions. To address this, our study introduces a comprehensive framework...
During the first week of July 2018, widespread flooding caused extensive damage across several river basins in western Japan. Among the affected basins were the Mabicho district of Kurashiki city in the lower part of the Oda river basin of the Okayam...
Federated learning, as a distributed machine learning framework, aims to protect data privacy while addressing the issue of data silos by collaboratively training models across multiple clients. However, a significant challenge to federated learning...
Federated learning with data free knowledge distillation enables effective and privacy-preserving knowledge aggregation by employing generators to produce local pseudo samples during client-side model migration. However, in practical applications, da...
Global and local features are essential for visual-similarity texture perception. Therefore, understanding how people allocate their visual attention when viewing textures with global or local similarity is important. In this work, we investigate the...
Industrial system operations usually have dynamic characteristics. If these characteristics are ignored, the performance of fault detection degrades. Herein, the fault-detection algorithm of dynamic global–local preserving projection (DGLPP) is...
Convolution is a crucial component of convolution neural networks (CNNs). However, the standard static convolution has two primary defects: data independence and the weak ability to integrate global and local features. This paper proposes a novel and...
In this paper, a fault detection method based on kernel global local preserving projection is presented to address the nonlinear characteristics of industrial systems. First, data are projected into a high-dimensional feature space through nonlinear...
We propose a method for minimizing global buffer access within a deep learning accelerator for convolution operations by maximizing the data reuse through a local register file, thereby substituting the local register file access for the power-hungry...
The effective and accurate recognition of communication jamming is crucial for enhancing the anti-jamming capability of wireless communication systems. At present, a significant portion of jamming data is decentralized, stored in local nodes, and can...
Dimensionality reduction plays an important role in interpreting and visualizing high-dimensional data. Previous methods for data visualization overestimate the local structure and lack the consideration of global preservation. In this study, we deve...
Federated learning (FL) has emerged as a transformative framework for collaborative learning, offering robust model training across institutions while ensuring data privacy. In the context of making a COVID-19 diagnosis using lung imaging, FL enables...
The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data sharing among third parties. Federated learning offers a solution by enabling the trai...
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