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In cartography, generalization is a key process used to simplify complex geographic information, making it suitable for display at different scales while maintaining its essential meaning. When representing high-density road networks on a fixed screen area, overcrowding and loss of clarity often occur. To solve these problems, a road selection operation can be applied. However, traditional methods have primarily focused on structured vector road networks, leaving unstructured raster road networks largely unaddressed. This study introduces a novel technique, Adaptive Road Width Selection (ARWS), designed to improve the multiscale visualization of compact road systems using unstructured raster datasets. The ARWS method begins by segmenting the original raster road network into multilevel superpixels of varying sizes, reflecting the road widths, through neighborhood analysis. Next, road superpixel matching and selection are performed based on the minimum angle and maximum distance rules, alongside shortest-path calculations. Finally, redundant intersection pixels are eliminated to generate the selection results. The proposed ARWS method was evaluated using road network data from Shenzhen, China, producing effective multiscale visualization outcomes. Unlike conventional techniques relying on structured vector data, ARWS excels in preserving the semantic attributes, overall structure, local connectivity, and integrity of road networks. It addresses the challenges of multiscale visualization in dense road networks, offering a robust solution for unstructured raster data. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for the conversion of raster data to hexagonal DGGSs in the GPU by redevising the encoding and decoding mechanisms. The researchers first designed a data structure based on rhombic tiles to convert the hexagonal DGGS to a texture format acceptable for GPUs, thus avoiding the irregularity of the hexagonal DGGS. Then, the encoding and decoding methods of the tile data based on space-filling curves were designed, respectively, so as to reduce the amount of data transmission from the CPU to the GPU. Finally, the researchers improved the algorithmic efficiency through thread design. To validate the above design, raster integration experiments were conducted based on the global Aster 30 m digital elevation dataDEM, and the experimental results showed that the raster integration accuracy of this algorithms was around 1 m, while its efficiency could be improved to more than 600 times that of the algorithm for integrating the raster data to the hexagonal DGGS data, executed in the CPU. Therefore, the researchers believe that this study will provide a feasible method for the efficient and stable integration of massive raster data based on a hexagonal grid, which may well support the organization of massive raster data in the field of GIS. [ABSTRACT FROM AUTHOR]

In cartography, generalization is a key process used to simplify complex geographic information, making it suitable for display at different scales while maintaining its essential meaning. When representing high-density road networks on a fixed screen area, overcrowding and loss of clarity often occur. To solve these problems, a road selection operation can be applied. However, traditional methods have primarily focused on structured vector road networks, leaving unstructured raster road networks largely unaddressed. This study introduces a novel technique, Adaptive Road Width Selection (ARWS), designed to improve the multiscale visualization of compact road systems using unstructured raster datasets. The ARWS method begins by segmenting the original raster road network into multilevel superpixels of varying sizes, reflecting the road widths, through neighborhood analysis. Next, road superpixel matching and selection are performed based on the minimum angle and maximum distance rules, alongside shortest-path calculations. Finally, redundant intersection pixels are eliminated to generate the selection results. The proposed ARWS method was evaluated using road network data from Shenzhen, China, producing effective multiscale visualization outcomes. Unlike conventional techniques relying on structured vector data, ARWS excels in preserving the semantic attributes, overall structure, local connectivity, and integrity of road networks. It addresses the challenges of multiscale visualization in dense road networks, offering a robust solution for unstructured raster data.

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Computer screens often constrain the level of detail and clarity of displays. High-density data require a predefined strategy to select significant features hierarchically to allow interactive data zooming. Although many methods are available for hierarchically selecting rivers from vector data, some approaches for raster data are better than others for maintaining accuracy when the original river data are in a raster format during generalization. In this study, a raster-based approach is proposed to allow hierarchical superpixel selection in river networks. Linear spectral clustering segmentation was applied to divide the original raster river networks into superpixels at multiple levels. A graph was constructed to organize the generated river network superpixels based on the distances between adjacent superpixels by considering the weights determined by the four types of rules. Finally, the total weight values were ranked, the river-network superpixels were selected according to their weights, and the redundant pixels at the river-network intersections were removed. Compared with the traditional vector selection method, the proposed superpixel river network selection method can effectively consider the characteristics of river width without artificial river grading and preserve the main structure and connectivity features during hierarchical mapping. Notably, the average geometry and density changes decreased by 15.8% and 5.1%, respectively. [ABSTRACT FROM AUTHOR]

High-resolution satellite data are an excellent way to monitor the growth of an urban area in terms of vertical and horizontal growth. Time-series data over two different zones from the same satellite sensor or contemporary sensors act as a good test bed for change detection. In most of the cases, 2D images of different time frames are spatially registered, and pixel difference is calculated which enables the detection of change in horizontal growths. Three-dimensional change detection to mark a change in the vertical direction can also be computed by comparing high-resolution digital surface models (DSM) of two different times and detect changes in topography. Using accurate DSM and derived digital terrain models (DTM) information from DSM, exact and accurate heights of the building footprints can be extracted. Using 3D city models, information about horizontal growth and vertical growth of the city can be assessed using change detection over the temporal data. Three-dimensional change detection can also enable district and state administration to discern the planned growth of the city, illegal constructions and future planning of the city, especially in the projects like smart cities. In this study, we are comparing 2D raster images of different time frames to assess change in horizontal direction, very high-resolution DSMs and DTMs datasets of two different time zones to assess change in vertical directions and visualizing 3D change detection of Ahmedabad city, in terms of its horizontal and vertical changes in urban growth area. We are also making the assessment of the growth of the city (5% change in building structures) and population in the studied area. It is inferred that the city population in the year 2018 is more than 35% as compared to the population in the year 2011. Further, we are calculating geophysical parameters of land surface temperature (LST) and normalized difference vegetation index (NDVI) over a time using satellite datasets, which provides a proxy observation for the changes in the urban growth. Using satellite data, it is concluded that NDVI is reduced over the study area whereas there is an increase in LST temperature at night time during the winter season. We concluded that increased urbanization and population (> 35%) are also contributing for rise in the LST temperature at nights in the city apart from the other big environmental parameters such as global warming, etc. [ABSTRACT FROM AUTHOR]

Wind flow is one of the elements that influence the dispersion of pollutants in air pollution events. The wind flow can be observed using the Computational Fluid Dynamics (CFD) technique. Attachment of the building model is required to monitor the wind flow of the urban area as the building's existence manipulates the movement of the wind throughout the urban area. In the meantime, prior research used building models with no specific modelling standard incorporated into the simulation environment. However, this research employs the building standard of City Geographic Markup Language (CityGML) to model the building involved. As referred to in the earlier research, the present level of detail 3.1 (LoD3.1) model is the least detail LoD suitable to visualize the wind simulation; this model is used as the original model to be compared with the newly generated model in 3D raster type. The vector data type is the model in the specified LoD of the CityGML standard. This study investigates the amalgamation of the raster model in the simulation environment by comparing the edge length, the simulation result, and the computational time of the rasterized model with the vectorized model. A slight edge length difference of approximately 14.5 cm is shown by the raster model. However, its simulation environment yields acceptable error values for wind velocity and pressure difference of 0.089 and 0.096, respectively, and outperforms the vector environment with a 50.4% shorter computational time. The investigation concludes that the 3D rasterized model is compatible with the wind simulation environment. [ABSTRACT FROM AUTHOR]

Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborhoods. This study proposed a hexagon-based method for polygon generalization using morphological operators. First, we defined three generalization operators: aggregation, elimination, and line simplification, based on hexagonal morphological operations. We then used corrective operations with selection, skeleton, and exaggeration to detect, classify, and correct the unreasonably reduced narrow parts of the polygons. To assess the effectiveness of the proposed method, we conducted experiments comparing the hexagonal raster to square raster and vector data. Unlike vector-based methods in which various algorithms simplified either areal objects or exterior boundaries, the hexagon-based method performed both simplifications simultaneously. Compared to the square-based method, the results of the hexagon-based method were more balanced in all neighborhood directions, matched better with the original polygons, and had smoother simplified boundaries. Moreover, it performed with shorter running time than the square-based method, where the minimal time difference was less than 1 min, and the maximal time difference reached more than 50 mins. [ABSTRACT FROM AUTHOR]

The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, collect and publish several land-use and land-cover data sets. The aim of this study is to analyze the consistency of forestland estimates based on two widely used, publicly available products: the National Land-Cover Database (NLCD) and Cropland Data Layer (CDL). Both remote-sensing-based products provide raster-formatted land-cover categorization at a spatial resolution of 30 m. Although the processing of the yearly published CDL non-agricultural land-cover data is based on less frequently updated NLCD, the consistency of large-area forestland mapping between these two datasets has not been assessed. To assess the similarities and the differences between CDL- and NLCD-based forestland mappings for the state of North Carolina, we overlay the two data products for the years 2011 and 2016 in ArcMap 10.5.1 and analyze the location and attributes of the matched and mismatched forestland. We find that the mismatch is relatively smaller for the areas of the state where forests occupy larger shares of the total land, and that the relative mismatch is smaller in 2011 when compared to 2016. We also find that a large portion of the forestland mismatch is attributable to the dynamics of re-growth of periodically harvested and otherwise disturbed forests. Our results underscore the need for a holistic approach to data preparation, data attribution, and data accuracy when performing high-scale map-based analyses using each of these products. [ABSTRACT FROM AUTHOR]

To solve the problems of small area, slow calculation speed and low precision in the projection transformation algorithm of raster data, the idea of using Kriging interpolation approximate grid algorithm for raster data projection transformation is proposed. Through the experimental results of point-to-point transformation and Kriging interpolation approximate grid algorithm transformation under the same conditions, it can be seen that for different projection types under the same limit conditions, Kriging interpolation approximate grid algorithm can ensure that the raster data projection error is always within the given projection limit. With the same number of points, the Kriging interpolation approximate grid algorithm is faster and more efficient than the point-to-point projection algorithm. Under the same pixel condition, the Kriging interpolation approximate grid algorithm is faster, more accurate and more effective than the point-to-point projection algorithm.

Geospatial data is very sensitive data. The GIS data model should provide make sensitive dataset available to authorized users only and preserve the access for insensitive data from same database to general users. To secure sensitive data from any unauthorized modifications and maintain its confidentiality, a strong encryption method with limited resources should be developed. Available encryption techniques for GIS data security are dealing with encryption technologies for GIS data based on watermarking, symmetric key cryptography techniques, and chaotic maps etc. which are useful for copyright protections. Geospatial data is used widely for many data sensitive applications like defence management, power grids, business decision making, tracking of events and activities using IoT devices etc. These systems are all vulnerable to various cyber-attacks, intrusions. It leads to incorrect information and affects business decisions. [ABSTRACT FROM AUTHOR]

We present efficient algorithms to compute simple and complex map algebra operations over raster data stored in main memory, using the k2-acc compact data structure. Raster data correspond to numerical data that represent attributes of spatial objects, such as temperature or elevation measures. Compact data structures allow efficient data storage in main memory and query them in their compressed form. A k2-acc is a set of k2-trees, one for every distinct numeric value in the raster matrix. We demonstrate that map algebra operations can be computed efficiently using this compact data structure. In fact, some map algebra operations perform over five orders of magnitude faster compared with algorithms working over uncompressed datasets. [ABSTRACT FROM AUTHOR]

Marine spatial planning (MSP) has recently attracted more attention as an efficient decision support tool. MSP is a strategic and long-term process gathering multiple competing users of the ocean with the objective to simplify decisions regarding the sustainable use of marine resources. One of the challenges in MSP is to determine an optimal zone to locate a new activity while taking into account the locations of the other existing activities. Most approaches to spatial zoning are formulated as non-linear optimization models involving multiple objectives, which are usually solved using stochastic search algorithms, leading to sub-optimal solutions. In this paper, we propose to model the problem as a Multi-Objective Integer Linear Program. The model is developed for raster data and it aims at maximizing the interest of the area of the zone dedicated to the new activity while maximizing its spatial compactness. We study two resolution methods: first, a weighted-sum of the two objectives, and second, an interactive approach based on an improved augmented version of the ϵ-constraint method, AUGMECON2. To validate and study the model, we perform experiments on artificially generated data. Our experimental study shows that AUGMECON2 represents the most promising approach in terms of relevance and diversity of the solutions, compactness, and computation time.

To solve the problems of small area, slow calculation speed and low precision in the projection transformation algorithm of raster data, the idea of using Kriging interpolation approximate grid algorithm for raster data projection transformation is proposed. Through the experimental results of point-to-point transformation and Kriging interpolation approximate grid algorithm transformation under the same conditions, it can be seen that for different projection types under the same limit conditions, Kriging interpolation approximate grid algorithm can ensure that the raster data projection error is always within the given projection limit. With the same number of points, the Kriging interpolation approximate grid algorithm is faster and more efficient than the point-to-point projection algorithm. Under the same pixel condition, the Kriging interpolation approximate grid algorithm is faster, more accurate and more effective than the point-to-point projection algorithm. [ABSTRACT FROM AUTHOR]

Accounting for variation in infiltration over space and time is essential in planning for land management. One such example is the consideration of sites for mitigation wetlands, where knowledge of the water cycle is essential to a determination of whether a site will have sufficient water to support wetland functioning. A methodology and an implementing script (GAINS) were developed using the Green–Ampt equation to calculate instantaneous and cumulative infiltration, moisture content, and excess water on the land surface. Results are available in tabular form, as well in GIS maps displaying the individual parameters at a site at any given point in time. GAINS, written in Python, was demonstrated for a site within Pershing State Park in Missouri, a location that traditionally has supported wetlands. Results showed that the Green–Ampt equation was correctly modeled. The successful demonstration is an important step toward providing a single tool to support decision-making regarding potential wetland mitigation sites. [ABSTRACT FROM AUTHOR]

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO 2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO 2 data. The models were used to predict actual near-surface NO 2 concentrations. Model-predicted NO 2 concentrations were validated with NO 2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO 2 was assessed. It was found that the weather component had substantial impact on NO 2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO 2. Both models accurately estimated near-surface NO 2 concentrations. Modeled NO 2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO 2 concentrations were found to highly correlate with monitoring station NO 2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases. [Display omitted] • PCA is reliable for identifying and predicting air pollution variation. • WPCA and RPCA models well fitted observed NO 2. • Both models accurately estimated near-surface NO 2 concentrations. • Total lockdown had prolongated effect on NO 2 concentrations. • Weather conditions propitiated air pollutant decrease during lockdown and increase after. [ABSTRACT FROM AUTHOR]

The size of spatial data is growing intensively due to the emergence of and the tremendous advances in technology such as sensors and the internet of things. Supporting high-performance queries on this large volume of data becomes essential in several data- and compute-intensive applications. Unfortunately, most of the existing methods and approaches are based on a traditional computing framework (uniprocessors) which makes them not scalable and not adequate to deal with large-scale data. In this work, we present a high-performance query for massive spatio–temporal data. The query consists of selecting fixed size raster subsequences, based on the average of their region of interest, from a spatio–temporal raster sequence satisfying a user threshold condition. In our paper, for the purpose of simplification, we consider that the region of interest is the entire raster and not only a subregion. Our aim is to speed up the execution using parallel primitives and pure CUDA. Furthermore, we propose a new method based on a sorting step to save computations and boost the speed of the query execution. The test results show that the proposed methods are faster and good performance is achieved even with large-scale rasters and data.

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO 2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO 2 data. The models were used to predict actual near-surface NO 2 concentrations. Model-predicted NO 2 concentrations were validated with NO 2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO 2 was assessed. It was found that the weather component had substantial impact on NO 2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO 2 . Both models accurately estimated near-surface NO 2 concentrations. Modeled NO 2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO 2 concentrations were found to highly correlate with monitoring station NO 2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This article presents an evaluation of the ERDAS IMAGINE Spatial Model Editor from the perspective of effective cognition. Workflow models designed in Spatial Model Editor are used for the automatic processing of remote sensing data. The process steps are designed as a chain of operations in the workflow model. The functionalities of the Spatial Model Editor and the visual vocabulary are both important for users. The cognitive quality of the visual vocabulary increases the comprehension of workflows during creation and utilization. The visual vocabulary influences the user’s exploitation of workflow models. The complex Physics of Notations theory was applied to the visual vocabulary on ERDAS IMAGINE Spatial Model Editor. The results were supplemented and verified using the eye-tracking method. The evaluation of user gaze and the movement of the eyes above workflow models brought real insight into the user’s cognition of the model. The main findings are that ERDAS Spatial Model Editor mostly fulfils the requirements for effective cognition of visual vocabulary. Namely, the semantic transparency and dual coding of symbols are very high, according to the Physics of Notations theory. The semantic transparency and perceptual discriminability of the symbols are verified through eye-tracking. The eye-tracking results show that the curved connector lines adversely affect the velocity of reading and produce errors. The application of the Physics of Notations theory and the eye-tracking method provides a useful evaluation of graphical notation as well as recommendations for the user design of workflow models in their practice.

Context: Lacunarity as a scale-dependent measure of spatial heterogeneity has received great attention in landscape ecology. Most lacunarity measures have been obtained from greyscale or binary (0 and 1) data for an entire study area or fixed rectangular windows, and a zonal lacunarity tool for discrete raster data is still lacking in current geographic information systems. Objectives: This short communication presents the development of a free zonal lacunarity analysis tool for ArcGIS to support applications involving scale-dependent analysis of spatial heterogeneity, including landscape ecology. The application of the tool is also demonstrated using 2001 and 2011 land cover data from the National Land Cover Database (NLCD). Methods: Based on the gliding-box algorithm for lacunarity estimation, a tool for zonal lacunarity analysis of discrete raster data is developed using ArcPy and the Python programming language. The tool uses discrete raster data as input, an optional zone feature class as zone data to partition the input raster data into different zones, and a spreadsheet with zonal lacunarity values as output. Results: As a demonstration, lacunarity measurements of grasslands in Corinth and Lake Dallas, Texas were calculated from the 2001 and 2011 NLCD data using box sizes (scales) of 2, 3, 4, 5, 6, 7, 8, 9, and 10. The results show that measures of grassland lacunarity in Lake Dallas were higher than Corinth at all scales, and the measures of grassland lacunarity in 2011 were higher than 2001 for both cities because of the increasing gap sizes in grasslands. The increasing gap sizes in grasslands were caused by converting the grasslands into developed areas. Conclusions: The results suggest that the zonal lacunarity analysis tool can provide important information on the spatial distribution of gaps in the input discrete raster data at different scales. It is hoped that the zonal lacunarity analysis tool can be further evaluated using different datasets in landscape ecology. [ABSTRACT FROM AUTHOR]

Raster type of forest inventory data with site and growing stock variables interpreted for small square-shaped grid cells are increasingly available for forest planning. In Finland, there are two sources of this type of lattice data: the multisource national forest inventory and the inventory that is based on airborne laser scanning (ALS). In both cases, stand variables are interpreted for 16 m × 16 m cells. Both data sources cover all private forests of Finland and are freely available for forest planning. This study analyzed different ways to use the ALS raster data in forest planning. The analyses were conducted for a grid of 375 × 375 cells (140,625 cells, of which 97,893 were productive forest). The basic alternatives were to use the cells as calculation units throughout the planning process, or aggregate the cells into segments before planning calculations. The use of cells made it necessary to use spatial optimization to aggregate cuttings and other treatments into blocks that were large enough for the practical implementation of the plan. In addition, allowing premature cuttings in a part of the cells was a prerequisite for compact treatment areas. The use of segments led to 5–9% higher growth predictions than calculations based on cells. In addition, the areas of the most common fertility classes were overestimated and the areas of rare site classes were underestimated when segments were used. The shape of the treatment blocks was more irregular in cell-based planning. Using cells as calculation units instead of segments led to 20 times longer computing time of the whole planning process than the use of segments when the number of grid cells was approximately 100,000. [ABSTRACT FROM AUTHOR]

Advancements in satellite imaging and sensor technologies result in capturing of large amount of spatial data. Many parallel processing techniques based on data or control parallelism have been attempted during the past 2 decades to provide performance improvement in image processing applications such as urban sprawl, weather prediction and crop estimation. These techniques have used block-based distributed file processing or the more modern MapReduce-based programming for implementation which still have gaps between optimal and best processing in terms of resource scheduling, data distribution and ease of programming. In this paper, we present a layered framework for parallel data processing to improve storage, retrieval and processing performance of spatial data on an underlying distributed file system. The paper presents a data placement strategy across a distributed HDFS cluster in a way to optimize spatial data retrieval and processing. The presence of neighborhood pixels local to the processing node in a distributed environment reduces network latencies and improves the efficiency of applications such as object recognition, change detection and site selection. We evaluate the data placement strategy on a four-node HDFS cluster and show that it can deliver good performance benefits by way of reading blocks of data at almost 10–12 times the default, which contributes to the improvement in efficiency of the various applications that use region growing methods. [ABSTRACT FROM AUTHOR]

Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments. This is the case for instance in the Lidar-based and multi-source forest inventories of Finland where the interpretation units are 16 m × 16 m grid cells. Using these cells as simulation units in forest planning would lead to very large planning problems. This difficulty could be alleviated by aggregating the grid cells into larger homogeneous segments before planning calculations. This study developed a cellular automaton (CA) for aggregating grid cells into larger calculation units, which in this study were called stands. The criteria used in stand delineation were the shape and size of the stands, and homogeneity of stand attributes within the stand. The stand attributes were: main site type (upland or peatland forest), site fertility, mean tree diameter, mean tree height and stand basal area. In the CA, each cell was joined to one of its adjacent stands for several iterations, until the cells formed a compact layout of homogeneous stands. The CA had several parameters. Due to high number possible parameter combinations, particle swarm optimization was used to find the optimal set of parameter values. Parameter optimization aimed at minimizing within-stand variation and maximizing between-stand variation in stand attributes. When the CA was optimized without any restrictions for its parameters, the resulting stand delineation consisted of small and irregular stands. A clean layout of larger and compact stands was obtained when the CA parameters were optimized with constrained parameter values and so that the layout was penalized as a function of the number of small stands (< 0.1 ha). However, there was within-stand variation in fertility class due to small-scale variation in the data. The stands delineated by the CA explained 66-87% of variation in stand basal area, mean tree height and mean diameter, and 41-92% of variation in the fertility class of the site. It was concluded that the CA developed in this study is a flexible new tool, which could be immediately used in forest planning. [ABSTRACT FROM AUTHOR]