Raster Image Research Articles

Palette‐Based Recolouring of Gradient Meshes

AbstractGradient meshes are a vector graphics primitive formed by a regular grid of bicubic quad patches. They allow for the creation of complex geometries and colour gradients, with recent extensions supporting features such as local refinement and sharp colour transitions. While many methods exist for recolouring raster images, often achieved by modifying an automatically detected palette of the image, gradient meshes have not received the same amount of attention when it comes to global colour editing. We present a novel method that allows for real‐time palette‐based recolouring of gradient meshes, including gradient meshes constructed using local refinement and containing sharp colour transitions. We demonstrate the utility of our method on synthetic illustrative examples as well as on complex gradient meshes.

A novel global multi-level terrain data tile production method

Abstract. Terrain services have increasingly wide applications in urban planning, 3D gaming animation, and other fields. Map websites typically provide terrain services to users in the form of terrain shaded relief tiles. Map tile technology offers a new solution for large-scale concurrent user access. Although terrain shaded relief tiles can effectively represent changes in terrain relief, users cannot adjust the visualized raster images for visualization schemes, making data analysis and applications more challenging. The emergence of Terrain-RGB technology has effectively addressed this limitation. However, currently, internet maps mostly provide multi-level map services to users in the form of web map tile service (WMTS). Terrain-RGB images produced by simple encoding tools may exhibit grid lines when rendered on tiles with a small grid spacing, affecting the usability of Terrain-RGB products. Therefore, this paper proposes a technical approach for producing global multi-level Terrain-RGB products using digital elevation model (DEM), focusing on addressing the issues of gridlines and splicing traces from different scenes DEM data, offering a new solution for map websites to release analyzable terrain services.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Mapping cellular coverage suitability: a GIS-Based approach to identifying poor service areas

Cellular coverage is a critical component of modern telecommunications, providing essential services for communication, commerce, and emergency response. Despite its importance, many regions, particularly those with challenging terrain or low population density, experience inadequate cellular service. This study focuses on the commune of Laghouat, Algeria, employing a GIS-based Multi-Criteria Analysis (MCA) to assess cellular coverage suitability. Using ArcGIS Pro, four key factors were integrated: cell tower locations, terrain elevation, population distribution, and restricted areas. The methodology includes performing a Viewshed analysis to identify dead zones, converting population data into raster format to map demand, and digitizing restricted areas where telecommunication infrastructure development is prohibited. All factors were standardized to a common scale to ensure comparability and a weighted overlay analysis was conducted to generate a comprehensive suitability map. The map highlights areas of poor cellular coverage, guiding the placement of new infrastructure. Additionally, the research considers regulatory restrictions on land use, ensuring compliance with governmental policies. The resulting suitability map provides valuable insights for telecommunications providers and policymakers, enabling them to target underserved regions for infrastructure improvements while adhering to legal constraints. This study offers a robust, scalable framework for identifying and addressing cellular coverage gaps, applicable to other regions with similar geographic and demographic challenges.

3D Data Integration in the Voxel Domain

Abstract. The interest in 3D data integration is growing as the concept of Spatial Digital Twins gains traction among local governments and stakeholders. Spatial data is typically organized into standardised themes such as buildings, transport, and vegetation, or as ad hoc data products in either 2D or 3D dimensions. However, integrating these data sets into a cohesive 3D model presents several challenges. The foundational data sets may vary in terms of accuracy, resolution, representation, and actuality due to differences in creation standards and procedures. Consequently, inconsistencies often arise within the 3D models, making them difficult to identify and rectify. Many of these data integration issues can be traced back to the 3D vector representation. Vector 3D models exhibit significant geometric diversity, which complicates the computational operations required for examining and validating the models. In this paper, we propose a novel voxel-based approach for integrating 3D data. Voxels, analogous to pixels in 2D raster images, inherit several key properties. We employ a dedicated voxel data structure and a set of operations tailored for this purpose. First, we voxelise vector datasets, converting continuous geometric information into discrete voxel representations. Next, we merge voxel layers using a specialized voxel overlay operation. The resulting 3D model adheres to most validity and integrity requirements. To highlight the flexibility of our voxel data structure, we transformed it into point clouds for visualisation in ArcGIS Pro, enabling the addition of 3D base maps and GIS layers. We then published these datasets to Scene Services, granting public access. This allows users to easily explore the 3D scenes through a web browser, making complex geographic data more accessible. By leveraging voxels, our approach facilitates efficient and accurate 3D data integration, making it a valuable contribution to the field.

The optimal way to use geoinformation systems with graphical elements of symbols of veterinary significant objects in vector format

A significant problem in the field of standardization in the monitoring of infectious animal diseases is the lack of a system of conventional symbols of veterinary significant objects used in geoinformation technologies, in particular in digital and electronic cartography. The object of the study is the conventional signs of veterinary significant objects, which in the context of display on digital maps can be defined as "non–scalable", marked with corresponding pictograms. A systematic approach has been applied as a methodological basis for the epizootological research method. The basic graphic format of the proposed SVG symbols is used – (Scalable Vector Graphics), which is a modern generally accepted vector format. This article presents the results of cartographic display of pictograms of one of the three sets of symbols for the database – veterinary facilities for various purposes – for which the method of selecting basic signs is used. An algorithm for fixing elements of symbols of veterinary significant objects in the vector format of the SVG geoinformation system with maximum use of free software and secure information technologies is presented. Images in SVG format can be directly represented in current GIS and converted to almost any vector and raster formats without loss of image quality. The algorithm for developing a system of symbols for veterinary significant objects will be used in the Draft of the preliminary national standard in the future.

Convolutional neural networks and seismogram fingerprints as a tool for recognizing the nature of seismic events

In the presented article, two facts are convincingly demonstrated based on the results of numerous experiments. Firstly, fingerprints are informative enough representations of signal seismograms, despite their compression, to carry information about the nature of a seismic event. Secondly, the study showed that it is practically possible to design and train an artificial neural network capable of classifying events by origin based on their fingerprints with high accuracy. Fingerprints are a tenthousandfold compressed representation of the original seismogram obtained using the onedimensional wavelet transform and the twodimensional Haar wavelet transform. They carry information about all significant frequencytime phenomena contained in the primary seismogram. Convolutional neural networks were chosen as a class of neural network classifier based on the conducted review of publications on this topic. They have proven themselves to be excellent in recognizing objects and persons in raster images. And the fingerprints used in this study are binary images measuring 64×64 pixels. The convolutional neural network prepared to work with them has one of the simplest architectures for this type of network and a very small number of adjustable parameters. By means of it, the classification accuracy of 95% was easily achieved. To prove that this result is not accidental, a strategy for modeling the architecture of convolutional neural networks using specially developed software, the Trova system, is demonstrated. This software allows the researcher to easily and conveniently operate seismograms, obtain binary fingerprints from them, correctly augment data, create, train and test neural networks. An important feature of the Trova system is the advanced functionality of the graphical representation of multidimensional modeling results.

Archaeologies and Organologies: Towards an alternative history of early synthetic video and image processing practices 1939–1969

Abstract This article focuses on the early history of two related techniques deployed within artistic contexts; the distortion of pre-existing television raster images and the generation of synthetic images through the application of external signals to cathode-ray tube deflection systems. Organology and media archaeological techniques are used to begin to trace an additional technical history of these complex visual instruments.

A New Framework for Generating Indoor 3D Digital Models from Point Clouds

Three-dimensional indoor models have wide applications in fields such as indoor navigation, civil engineering, virtual reality, and so on. With the development of LiDAR technology, automatic reconstruction of indoor models from point clouds has gained significant attention. We propose a new framework for generating indoor 3D digital models from point clouds. The proposed method first generates a room instance map of an indoor scene. Walls are detected and projected onto a horizontal plane to form line segments. These segments are extended, intersected, and, by solving an integer programming problem, line segments are selected to create room polygons. The polygons are converted into a raster image, and image connectivity detection is used to generate a room instance map. Then the roofs of the point cloud are extracted and used to perform an overlap analysis with the generated room instance map to segment the entire roof point cloud, obtaining the roof for each room. Room boundaries are defined by extracting and regularizing the roof point cloud boundaries. Finally, by detecting doors and windows in the scene in two steps, we generate the floor plans and 3D models separately. Experiments with the Giblayout dataset show that our method is robust to clutter and furniture point clouds, achieving high-accuracy models that match real scenes. The mean precision and recall for the floorplans are both 0.93, and the Point–Surface Distance (PSD) and standard deviation of the PSD for the 3D models are 0.044 m and 0.066 m, respectively.

Efficient compressed storage and fast reconstruction of large binary images using chain codes

AbstractLarge binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between $$12\%$$ 12 % and $$33\%$$ 33 % faster reconstruction on test data.

The Potential of Hybrid Data Integration Approaches Based UAV-SfM Photogrammetry in Identifying New Archaeological Features in The Archaeological UR Site

Structure-from-Motion (SfM) Photogrammetry-based unmanned aerial vehicles (UAV) are considered one of the most effective non-destructive techniques to detect features in archaeology. However, relying on only standalone-based photogrammetric approaches is not always valid in revealing new archaeological features and monuments. This study aims to verify the efficiency of the integration-based methodologies of SfM photogrammetry products to improve the detection of new archaeological features in the non-excavated regions of the archaeological site of Ur, south of Iraq. The methodology proposes an automatic workflow to extract 3D topographic models such as Digital Terrain Model (DTM) by Agisoft Metashape (MS) software and orthomosaic images as standard products for UAV-photogrammetry. These have delivered the following Structure-from-Motion and Multi-View Stereo (SfM-MVS) algorithms using an optimized flight planning, data capturing, and data processing pipeline. Stand-alone raster images derived from photogrammetry, such as (slope images, hill shade rasters, etc.), have limitations in archaeology compared to hybrid raster images in detecting covered and unrevealed features. The methodology focuses on fusing multiple rasters extracted from DTM using the QGIS software package analysis tools to detect features that may never be detected from single standalone image rasters. This is because combining multiple rasters of different parameters may highlight new potential features such as paths, temples, etc. The results findings revealed that the fusion methodology of raster images obtained from UAV-SfM Photogrammetry through its standard products and using high precision settings could successfully help to identify many new archaeological features in the undiscovered site in Ur, such as walls and houses, which may date back to the late periods of Ur. The results concluded that this enhances the role of photogrammetry and remote sensing techniques as non-destructive excavation tools to provide a rich database for archaeologists for further analysis and interpretation of undiscovered archaeological sites without destruction. Many new archaeological features were also revealed, and a general digital map of the site was prepared. It also prompts a review of the settlement theories in previous studies in the ancient city of UR and the documentation of excavated archaeological ruins.

An optimal B-spline approach for vectorizing raster image outlines.

Capturing outlines is crucial in vectorization of the digital images. An effective and automatic algorithm is presented for outline vectorizing of planar objects within the digital images using trigonometric B-spline. A soft computing optimization technique Genetic Algorithm (GA) is employed to determine the suitable measures of parameter in the description of proposed B-spline. The anticipated scheme is executed on a few raster (bitmap), digital images to validate the robustness of the algorithm. The procedure of vectorizing outlines encompasses a series of stages like boundary detection, corner recognition, break point identification and curve fitting using the proposed B-spline.

Harmonized Landsat and Sentinel-2 Data with Google Earth Engine

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R2 = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data.

Deep Sketch Vectorization via Implicit Surface Extraction

We introduce an algorithm for sketch vectorization with state-of-the-art accuracy and capable of handling complex sketches. We approach sketch vectorization as a surface extraction task from an unsigned distance field, which is implemented using a two-stage neural network and a dual contouring domain post processing algorithm. The first stage consists of extracting unsigned distance fields from an input raster image. The second stage consists of an improved neural dual contouring network more robust to noisy input and more sensitive to line geometry. To address the issue of under-sampling inherent in grid-based surface extraction approaches, we explicitly predict undersampling and keypoint maps. These are used in our post-processing algorithm to resolve sharp features and multi-way junctions. The keypoint and undersampling maps are naturally controllable, which we demonstrate in an interactive topology refinement interface. Our proposed approach produces far more accurate vectorizations on complex input than previous approaches with efficient running time.

Four-Dimensional Digital Monitoring and Registering of Historical Architecture for the Preservation of Cultural Heritage

Preserving cultural heritage through monitoring, registering, and analyzing damage in historical architectural structures presents significant financial and logistical burdens. Developed approaches for monitoring and registering 4D (4-dimensional)-scanned range and raster images of damaged objects were investigated in a case study of historical Baron Palace in Egypt. In the methodology, we first prepared and observed the damaged historical models. The damaged historical models were scanned using a laser scanner at a predetermined date and time. Simultaneously, digital images of the models were captured (by a calibrated digital camera) and stored on a researcher’s tablet device. By observing and comparing the scanned models with the digital images, geometric defects and their extent are identified. Then, the observed data components were detected on the map. Then, damaged statue materials were investigated using system of energy dispersive (SEM; scanning electron microscope, Gemini Zeiss-Ultra 55) and XRF (X-ray fluorescence) spectroscopic analysis to identify the statue’s marble elements, and the results indicate that SEM-EDX and XRF analyses accurately identify major and minor compositions of the damaged statue. Then, the damaged models were registered in two stages. In the registration stages, the corresponding points were determined automatically by detecting the closest points in the clouds and ICP (iterative closest point) algorithm in RiSCAN. The point clouds (of the Palace and damaged statues) gave very detailed resolutions and more realistic images in RiSCAN, but it is a costly program. Finally, the accuracies of the registration tasks were assessed; the standard deviations are within acceptable limits and tend to increase irregularly as the number of polydata observations used in the registration calculations increase.

Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches

Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among children younger than 5 years in sub-Saharan African (sSA) countries. We used the most recent (2012–2022) nationally representative Demographic and Health Surveys data of 33 sSA countries. The air pollution covariates such as global annual surface particulate matter (PM 2.5) and the nitrogen dioxide available in the form of raster images were obtained from the National Aeronautics and Space Administration (NASA). The MLA was used for predicting the symptoms of ARIs among under-five children. We randomly split the dataset into two, 80% was used to train the model, and the remaining 20% was used to test the trained model. Model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. A total of 327,507 under-five children were included in the study. About 7.10, 4.19, 20.61, and 21.02% of children reported symptoms of ARI, Severe ARI, cough, and fever in the 2 weeks preceding the survey years respectively. The prevalence of ARI was highest in Mozambique (15.3%), Uganda (15.05%), Togo (14.27%), and Namibia (13.65%,), whereas Uganda (40.10%), Burundi (38.18%), Zimbabwe (36.95%), and Namibia (31.2%) had the highest prevalence of cough. The results of the random forest plot revealed that spatial locations (longitude, latitude), particulate matter, land surface temperature, nitrogen dioxide, and the number of cattle in the houses are the most important features in predicting the diagnosis of symptoms of ARIs among under-five children in sSA. The RF algorithm was selected as the best ML model (AUC = 0.77, Accuracy = 0.72) to predict the symptoms of ARIs among children under five. The MLA performed well in predicting the symptoms of ARIs and associated predictors among under-five children across the sSA countries. Random forest MLA was identified as the best classifier to be employed for the prediction of the symptoms of ARI among under-five children.

GraphDecoder: Recovering Diverse Network Graphs From Visualization Images via Attention-Aware Learning.

DNGs are diverse network graphs with texts and different styles of nodes and edges, including mind maps, modeling graphs, and flowcharts. They are high-level visualizations that are easy for humans to understand but difficult for machines. Inspired by the process of human perception of graphs, we propose a method called GraphDecoder to extract data from raster images. Given a raster image, we extract the content based on a neural network. We built a semantic segmentation network based on U-Net. We increase the attention mechanism module, simplify the network model, and design a specific loss function to improve the model's ability to extract graph data. After this semantic segmentation network, we can extract the data of all nodes and edges. We then combine these data to obtain the topological relationship of the entire DNG. We also provide an interactive interface for users to redesign the DNGs. We verify the effectiveness of our method by evaluations and user studies on datasets collected on the internet and generated datasets.

Accessing global soil raster images and equal-area splines to estimate soil organic carbon stocks on the regional scale

Accessing global soil raster images and equal-area splines to estimate soil organic carbon stocks on the regional scale

Observations of fan-spine topology by Solar Orbiter/EUI: Rotational motions and indications of Alfvén waves

Context. Torsional Alfvén waves do not produce any intensity variation and are therefore challenging to observe with imaging instruments. Previously, Alfvén wave observations were reported throughout all the layers of the solar atmosphere using spectral imaging. Aims. We present a torsional Alfvén wave detected in an inverted Y-shaped structure observed with the HRIEUV telescope of the EUI instrument on board Solar Orbiter in its 174 Å channel. The feature consists of two footpoints connected through short loops and a spine with a length of 30 mm originating from one of the footpoints. Methods. We made use of the simultaneous observations from two other instruments on board Solar Orbiter. The first one is the Polarimetric and Helioseismic Imager, which is used to derive the magnetic configuration of the observed feature. The second is the Spectral Imaging of the Coronal Environment (SPICE) instrument, which provided observations of intensity maps in different lines, including Ne VIII and C III lines. We also address the issues of the SPICE point spread function and its influence on the Doppler maps via performed forward-modeling analysis. Results. The difference movie constructed from the HRIEUV images shows clear signatures of propagating rotational motions in the spine. We measured propagation speeds of 136 km s−1–160 km s−1, which are consistent with the expected Alfvén speeds. Evidence of rotational motions in the transverse direction with velocities of 26 km s−1–60 km s−1 serves as an additional indication of torsional waves being present. Doppler maps obtained with SPICE show a strong signal in the spine region. Under the assumption that the recovered point spread function is mostly correct, synthesized raster images confirm that this signal is predominantly physical. Conclusions. We conclude that the presented observations are compatible with an interpretation of either propagating torsional Alfvén waves in a low coronal structure or the untwisting of a flux rope. This is the first time we have seen signatures of propagating torsional motion in the corona as observed by the three instruments on board Solar Orbiter.

Rectilinear Building Footprint Regularization Using Deep Learning

Abstract. Nowadays, deep learning allows to automatically learn features from data. Buildings are one of the most important objects in urban environments. They are used in applications such as inputs to building reconstruction, disaster monitoring, city planing and environment modelling for autonomous driving. However, it is not enough to represent them in raster format, since applications require buildings as polygons. We use an existing, learning based approach to extract building footprints from ortho imagery and digital surface model (DSM) and propose a pipeline for building polygon extraction, which we call primary orientation learning (POL). The first step is to extract initial polygons, that contain a vertex for each pixel in the boundary of the footprint. Afterwards, the two primary orientation angles are regressed continuously. Using these orientation, we insert vertices such that all consecutive edges are perpendicular. To the best of our knowledge, our approach is the first to predict a continuous orientation angle for building boundary regularization. Furthermore, the proposed method is highly efficient with an average processing time of 2.879 ms for a single building.