Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this...
The objective of this study was to assess the effect of stacking on the dimensional and full-arch accuracy of 3D-printed models, utilising a standardised assessment methodology. A previously validated methodology involving a standard tessellation lan...
Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series stre...
This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high pr...
To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direc...
Vision transformers represent the cutting-edge topic in computer vision and are usually employed on two-dimensional data following a transfer learning approach. In this work, we propose a trained-from-scratch stacking ensemble of 3D-vision transforme...
As the climate emergency escalates, the role of forests in carbon sequestration is paramount. This paper proposes a framework that integrates local capacities, multi-source remote sensing data, and meta-learning to enhance forest carbon assessment me...
Developers often rely on online resources, such as Stack Overflow (SO), to seek assistance for programming tasks. To facilitate effective search and resource discovery, manual tagging of questions and posts with the appropriate programming language i...
Due to the high temporal repetition rates, median/low spatial resolution remote sensing images are the main data source of change detection (CD). It is worth noting that they contain a large number of mixed pixels, which makes adequately capturing th...
Accurate irrigated area maps remain difficult to generate, as smallholder irrigation schemes often escape detection. Efforts to map smallholder irrigation have often relied on complex classification models fitted to temporal image stacks. The use of...
To expand the applications of proton exchange membrane fuel cell (PEMFC) stacks, it is essential to address the issue of their short lifetime. Various studies are being conducted to improve this limitation, and efficient methods for verifying durabil...
The emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lowe...
Environmental, social and governance (ESG) metrics increasingly inform sustainable investment yet suffer from inter-rater heterogeneity and incomplete reporting, limiting their utility for forward-looking allocation. In this study, we developed and v...
Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependen...
Recognition of invasive species and their distribution is key for managing and protecting native species within both natural and man-made ecosystems. Small woody features (SWF) represent fragmented patches or narrow linear tree features that are of h...
Digital holography has transformative potential for the measurement of stacked-chip microstructures due to its non-invasive, single-shot, full-field characteristics. However, significant light scattering and diffraction at steep edges in step microst...
The noise characteristics of the Global Navigation Satellite System (GNSS) position time series can be biased by many factors, which in turn affect the estimates of parameters in the deterministic model using a least squares method. The authors asses...
Background and objective: Cardiac magnetic resonance (CMR) is a key tool for cardiac work-up. However, arrhythmia can be responsible for arrhythmia-related artifacts (ARA) and increased scan time using segmented sequences. The aim of this study is to...
Laser powder bed fusion can fabricate porous structures through lattices, but the preparation of micropores (<50 μm) with a specific pore distribution remains a challenge. Microporous 316L was fabricated by controlling the melting and solidific...
Digital holography enables the reconstruction of both 2D and 3D object information from interference patterns captured by digital cameras. A major challenge in this field is speckle noise, which significantly degrades the quality of the reconstructed...
Air pollution forecasting remains a critical challenge for urban public health management, with traditional approaches struggling to balance accuracy and interpretability. This study introduces a novel PM10 forecasting framework combining physics-inf...
The paper proposes an approach for estimating the rectal temperature of dairy cows based on the non-invasive real-time monitoring of their respiration rates and the temperature-humidity index (THI) of the environment, combined with the analysis of in...
Earth Observation services guarantee continuous land cover mapping and are becoming of great interest worldwide. The Google Earth Engine Dynamic World represents a planetary example. This work aims to develop a land cover mapping service in geomorpho...
Structural health monitoring (SHM) plays a critical role in ensuring the safety and longevity of civil infrastructure by enabling the early detection of structural changes and supporting preventive maintenance strategies. In recent years, deep learni...
Accurate Day-Ahead Energy Price (DAEP) forecasting is essential for optimizing energy market operations. This study introduces a machine learning framework to predict the DAEP with a 24 h lead time, leveraging historical data and forecasts available...
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Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Alt...
Evaluating the long-term urban forest ecological environmental quality (EEQ) and analyzing the drivers of its spatiotemporal changes can provide a scientific basis for making long-term urban forest planning decisions. Taking into account the characte...
Mapping forest tree species is vital for the habitat assessment, ecosystem services estimation, and implementation of European environmental policies such as the Habitats Directive. This study explores how repeated satellite observations over time, k...
This study presents the first systematic investigation into the influence of spectral enhancement techniques on the segmentation accuracy of specific soil and vegetation marks associated with palaeochannels. These marks are often subtle and can be se...
Ambulatory 24–72 h Holter ECG monitoring is recommended for patients with suspected arrhythmias, which are often transitory and might remain unseen in resting standard 12-lead ECG. Holter manufacturers provide software diagnostic tools to assis...
The “natural GMS laboratory” (granulometry-morphometry-situmetry) is located within the Variscan Basement in SE Germany (Fichtelgebirge Mts.), which is uplifted relative to its Permo-Mesozoic foreland along a deep-seated lineamentary faul...
In various engineering applications, remote sensing images such as digital elevation models (DEMs) and orthomosaics provide a convenient means of generating 3D representations of physical assets, enabling the discovery of new insights and analyses. H...
Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive sample...
This study presents an operational and robust method for detecting and dating cereal harvest events using temporal stacks of Copernicus Sentinel-2 imagery and crop and fields border information from ancillary records. The proposed approach is exempt...