The acquisition of high-resolution (HR) digital bathymetric models (DBMs) is crucial for oceanic research activities. However, obtaining HR DBM data is challenging, which has led to the use of super-resolution (SR) methods to improve the DBM’s...
The scale of digital elevation models (DEMs) is vital for terrain analysis, surface simulation, and other geographic applications. Compared to traditional super-resolution (SR) methods, deep convolutional neural networks (CNNs) have shown great succe...
The study provides a methodology for 3D model processing of historic mining landscape, and its features as mining digital cultural heritage with the possibility of using new visualization means in mining tourism. Historic mining landscapes around the...
This paper examines the multi-scale super-resolution challenge of digital elevation models in remote sensing. A dual-domain multi-scale attention fusion network is proposed, which reconstructs digital elevation image details step-by-step using cascad...
The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents...
In the design and operation scenarios driven by Digital Twins, large computer-aided design (CAD) models of production line equipment can limit the real-time performance and fidelity of the interaction between digital and physical entities. Digital CA...
In recent years, great progress has been made in the field of super-resolution (SR) reconstruction based on deep learning techniques. Although image SR techniques show strong potential in image reconstruction, the effective application of these techn...
The combination of lidar and digital photography provides a new technology for creating a high-resolution 3D digital outcrop model. The digital outcrop model can accurately and conveniently depict the surface 3D properties of an outcrop profile, maki...
Acquiring high-resolution digital elevation models (DEMs) over across extensive regions remains challenging due to high costs and insufficient detail, creating demand for super-resolution (SR) techniques. However, existing DEM SR methods still rely o...
The three-dimensional (3D) morphological structure of wheat directly reflects the interrelationship among genetics, environments, and cropping systems. However, the morphological complexity of wheat limits its rapid and accurate 3D modelling. We have...
Geomorphological surveys and terrain analysis are essential for geomorphology, hydrology, and geographic information systems (GIS). Terrain characterization parameters are fundamental for comprehending geomorphological processes, delineating landform...
Measuring surface deformation is crucial for a better understanding of spatial-temporal evolution and the mechanism of mining-induced deformation, thus effectively assessing the mining-related geohazards, such as landslides or damage to surface infra...
Toponymic entity recognition is currently a critical research hotspot in knowledge graphs. Under the guidance of the national ancient book protection policy and the promotion of the wave of digital humanities research, this paper proposes a toponymic...
Semantic segmentation of remote sensing images (RSI) plays a significant role in urban management and land cover classification. Due to the richer spatial information in the RSI, existing convolutional neural network (CNN)-based methods cannot segmen...
Digital watermarking technology is an important means to effectively protect three-dimensional (3D) model data. Among them, “blind detection” and “robustness” are key and difficult points in the current research of digital wat...
The use of digital elevation models (DEMs) that use airborne-based light detection and the ranging technique (airborne-based LiDAR) to understand large-scale geological structures has become important in geological surveying and mapping. Taking the D...
Current enteroscopy techniques present complications that are intended to be improved with the development of a new semi-automatic device called Endoworm. It consists of two different types of inflatable cavities. For its correct operation, it is ess...
An accurate forest soil organic carbon (SOC) assessment aids in the ecological restoration of forest mining areas, enabling dynamic monitoring of carbon sink accounting and informed land reclamation decisions. Digital soil mapping (DSM) has enhanced...
Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel ap...
Solar energy is the most clean renewable energy source and has good prospects for future sustainable development. Installation of solar photovoltaic (PV) systems on building rooftops has been the most widely applied method for using solar energy reso...
Deep learning has shown significant advantages over traditional spatial interpolation methods in single image super-resolution (SISR). Recently, many studies have applied super-resolution (SR) methods to generate high-resolution (HR) digital bathymet...
Near-global Digital Elevation Model (DEM) products generated through space-based radar techniques have become a basic data source for a variety range of applications. However, these DEM products often contain typical errors such as vegetation bias an...
This paper addresses the limitations and low efficiency of current methods for precise identification of continental margin break points in the delimitation of the outer continental shelf. From a three-dimensional perspective, it proposes a novel met...
Impact craters, as key geomorphic features on Mars, provide important insights into surface processes and geological evolution. However, automatic classification of crater morphologies remains challenging due to substantial variations in size, degrad...
Maize (Zea mays L.), one of the most important agricultural crops in the world, which can be devastated by lodging, which can strike maize during its growing season. Maize lodging affects not only the yield but also the quality of its kernels. The id...
Digital elevation models (DEMs) play a critical role in geospatial analysis and surface modeling. However, due to differences in data collection payload, data processing methodology, and data reference baseline, DEMs acquired from various sources oft...
The use of high resolution ground-based light detection and ranging (LiDAR) datasets provides spatial density and vertical precision for obtaining highly accurate Digital Surface Models (DSMs). As a result, the reliability of flood damage analysis ha...
In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic clas...
Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorph...
In this paper, the application is investigated of fast Fourier transform filtering (FFT-FR) to high spatial resolution digital terrain models (HR-DTM) derived from LiDAR sensors, assessing its efficacy in identifying genuine relief elements, includin...
Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted se...
Computer-aided detection (CADe) of masses in digital breast tomosynthesis (DBT) is crucial for early breast cancer diagnosis. However, the variability in the size and morphology of breast masses and their resemblance to surrounding tissues present si...
Alpine topography is formed by a complex series of geomorphological processes that result in a vast number of different landforms. The youngest and most diverse landforms are various Quaternary sedimentary bodies, each characterised by its unique lan...
Addressing the limitations of current multi-scale seabed terrain construction methods for a Digital Depth Model (DDM) and the low computational efficiency of automatic generalization algorithms, this paper draws on the concept of curvature simplifica...
Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studie...
High-resolution digital elevation models (DEMs) and its derivatives (e.g., curvature, slope, aspect) offer a great possibility of representing the details of Earth’s surface in three-dimensional space. Previous research investigations concernin...
InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM pr...
DEMs derived from Interferometric Synthetic Aperture Radar (InSAR) imagery are frequently influenced by multiple factors, resulting in systematic horizontal and elevation inaccuracies that affect their applicability in large-scale scenarios. To mitig...
The detection and counting of lunar impact craters are crucial for the selection of detector landing sites and the estimation of the age of the Moon. However, traditional crater detection methods are based on machine learning and image processing tec...
The success of deep learning and the segmentation of remote sensing images (RSIs) has improved semantic segmentation in recent years. However, existing RSI segmentation methods have two inherent problems: (1) detecting objects of various scales in RS...
The detection of edges in images is a pressing issue in the field of image processing. This technique has found widespread application in image pattern recognition, machine vision, and a variety of other areas. The feasibility and effectiveness of gr...
Bioinformatics and genomics are driving a healthcare revolution, particularly in the domain of drug discovery for anticancer peptides (ACPs). The integration of artificial intelligence (AI) has transformed healthcare, enabling personalized and immers...
The increasing interest in digital twin technology, the digitalization of worn-out social overhead capital (SOC), and disaster management services has augmented the usage of 3D spatial models and information to manage infrastructure. In this study, a...
Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote s...
Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that tr...
This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent...
The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to...
Satellite multi-view stereo (MVS) is a fundamental task in large-scale Earth surface reconstruction. Recently, learning-based multi-view stereo methods have shown promising results in this field. However, these methods are mainly developed by transfe...
This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchai...
Rapid, non-destructive estimation of leaf chlorophyll content (SPAD) is crucial for assessing plant photosynthetic health and nutrient status. However, conventional methods rely on specialized instruments (e.g., SPAD meters and hyperspectral sensors)...
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