Wskaźnik ekspozycji widokowej jako narzędzie optymalizacji punktów widokowych – studium przypadku Szczebrzeszyńskiego Parku Krajobrazowego

Landscape vantage points constitute the basic visual resource of the landscape, enable observation of panoramic panoramas, and thus cultural ecosystem services flows. The panoramic view is the tourist destination itself, therefore the functioning of the vantage points is related to the network of tourist routes. The optimization of the network of vantage points is a key issue of tourist function management as well as landscape physiognomic resources protection. The GIS-based visibility analysis is the most appreciated regarding landscape visual resources; it has theoretical and practical explanations. Theoretically, despite its multi-sensory character, the landscape can be regarded as “areas, as perceived by people”. In consequence, this puts a privilege to GIS software because, since the very beginning of its development, the viewshed algorithm computes the visible area and its reverse version (reverse viewshed) computes the area of visibility. This study applies reverse viewshed in search of landscape vantage points. The study aims to propose the methodology of visual exposure indicator (VEI) calculation, which is a geo-information supporting tool of the vantage point optimization process. The VEI values describe the given place’s suitability for vantage point location in terms of view panorama exposition conditions, which, combined with information about tourist routes, enables the vantage point location optimization. The VEI spatial variability analysis also allows for locating the vantage points so far not pointed on the tourist maps. The study was carried out on the example of the Szczebrzeszyński Landscape Park, as a result, it was proposed to expand the existing network up to 45 viewpoints. The resulting visual landscape resources quantification cannot be equated with visual landscape character and quality assessment; however, the proposed GIS framework provides objective results that precisely localize and measure them. The conclusions and limitations of the method were discussed in the context of geocomputation as well as the landscape conservation plans practises.Punkty widokowe stanowią podstawowy zasób fizjonomiczny krajobrazu, umożliwiają obserwację panoram, a tym samym warunkują przepływ kulturowych usług ekosystemowych. Widok panoramiczny sam w sobie bywa celem podejmowanej aktywności turystycznej, stąd też funkcjonowanie punktów widokowych związane jest z siecią tras turystycznych. Optymalizacja sieci punktów widokowych jest więc kluczowa dla prawidłowego kształtowania funkcji turystycznej regionu oraz ochrony jego zasobów fizjonomicznych. Celem pracy jest metodyczna propozycja wskaźnika ekspozycji widokowej, będącego geoinformacyjnym narzędziem wspierającym proces wyznaczania i optymalizacji sieci punktów widokowych. Badanie przeprowadzono na przykładzie Szczebrzeszyńskiego Parku Krajobrazowego, w efekcie zaproponowano rozbudowę istniejącej sieci do 45 punktów. Wnioski wypływające z metody i jej ograniczenia omówiono w kontekście praktyki przygotowania operatów ochrony walorów krajobrazowych

High Performance Geospatial Analysis on Emerging Parallel Architectures

Geographic information systems (GIS) are performing increasingly sophisticated analyses on growing data sets. These analyses demand high performance. At the same time, modern computing platforms increasingly derive their performance from several forms of parallelism. This dissertation explores the available parallelism in several GIS-applied algorithms: viewshed calculation, image feature transform, and feature analysis. It presents implementations of these algorithms that exploit parallel processing to reduce execution time, and analyzes the effectiveness of the implementations in their use of parallel processing

Empirical Evaluation of Route-Based Landscape Experiences

This thesis explores a method of visual analysis that aims to create a more in-depth understanding of how individuals see and visually perceive their environment. Here we explore a geospatial tool, called Visual Magnitude, to assess road-based experiences. We aimed to provide evidence of a relationship between the tool and scenic rating preferences from a survey. The content of this thesis is split between two articles. The first article, contained in Chapter 2, focuses on optimizing the selection of viewpoints along route-based envrionments. In this study we ask the question is there an optimal sampling rate of viewpoints along a route that can increase efficency in running a visual magnitude analysis and still represent accurately represent the envrionment. We found that for visually sensitive areas, a 30-meter sampling distance produced optimal results. For other landscapes a 50-meter sampling distance poduced resonable results in both sampling points and retained raster area. The second article, contained in Chapter 3, is an applied visual magnitude study where we use the optimal sampling distance of 30-meters to extract visual magnitude values for 15 different envrionments. These values are then compared to scenic rating values that we collected though a survey where participants saw videos of the same 15 envrionments and rated their scenic quailty. By doing this we were able to provide emperical evidence that the visual magnitude tool can be a way to predict best visual experiences within Utah. With the results from these studies we can make suggestions to professionals on how they can better use this GIS tool. These suggestions include sampling distances for multiple envrionments and the potential for this tool to be used as a poxy when attempting to interpret how landscapes observers feel about them. This additional infromation will help planners in understanding and making decisions more informed planning decisions along roadways and surrounding areas that have the highest potential impact on observers. By using this tool planners can assess where those areas are and the amount of impact that positive or negitive planning decisions will have on observers

VRSC 2021 Conference Proceedings

The biennial conference aims to catalyze ideas and innovation between academia, practice, NGOs and government agencies who work to address analysis, planning, valuation, design and management of visual resources. The aim of the 2021 Virtual Conference is to share ideas and discuss the issues associated with the assessment and protection of visual resources in an era of major landscape change - regionally, national and globally

System management algorithms for distributed vision networks

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Novel parallel approaches to efficiently solve spatial problems on heterogeneous CPU-GPU systems

Addressing this task is difficult as (i) it requires analysing large databases in a short time, and (ii) it is commonly addressed by combining different methods with complex data dependencies, making it challenging to exploit parallelism on heterogeneous CPU-GPU systems. Moreover, most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time—the most accurate algorithm was designed to process the fingerprints using a single thread. We developed a new methodology to address the latent fingerprint identification problem called “Asynchronous processing for Latent Fingerprint Identification” (ALFI) that speeds up processing while maintaining high accuracy. ALFI exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism to analyse massive fingerprint databases. We assessed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results revealed that ALFI is on average 22x faster than the state-of-the-art identification algorithm, reaching a speed-up of 44.7x for the best-studied case. In terrain analysis, Digital Elevation Models (DEMs) are relevant datasets used as input to those algorithms that typically sweep the terrain to analyse its main topological features such as visibility, elevation, and slope. The most challenging computation related to this topic is the total viewshed problem. It involves computing the viewshed—the visible area of the terrain—for each of the points in the DEM. The algorithms intended to solve this problem require many memory accesses to 2D arrays, which, despite being regular, lead to poor data locality in memory. We proposed a methodology called “skewed Digital Elevation Model” (sDEM) that substantially improves the locality of memory accesses and exploits the inherent parallelism of rotational sweep-based algorithms. Particularly, sDEM applies a data relocation technique before accessing the memory and computing the viewshed, thus significantly reducing the execution time. Different implementations are provided for single-core, multi-core, single-GPU, and multi-GPU platforms. We carried out two experiments to compare sDEM with (i) the most used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm for solving the total viewshed problem. In the first experiment, sDEM results on average 8.8x faster than current GIS software, despite considering only a few points because of the limitations of the GIS software. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm considering the best case. The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown enormously in tasks involving terrain coverage, such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered from the flight path an essential goal, especially when the area to be monitored is irregular, large, and includes many blind spots. In this regard, state-of-the-art total viewshed algorithms can help analyse large areas and find new paths providing all-round visibility. We designed a new heuristic called “Visibility-based Path Planning” (VPP) to solve the path planning problem in large areas based on a thorough visibility analysis. VPP generates flyable paths that provide high visual coverage to monitor forest regions using the onboard camera of a single UAV. For this purpose, the hidden areas of the target territory are identified and considered when generating the path. Simulation results showed that VPP covers up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located in the province of Malaga (Spain). In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.In recent years, approaches that seek to extract valuable information from large datasets have become particularly relevant in today's society. In this category, we can highlight those problems that comprise data analysis distributed across two-dimensional scenarios called spatial problems. These usually involve processing (i) a series of features distributed across a given plane or (ii) a matrix of values where each cell corresponds to a point on the plane. Therefore, we can see the open-ended and complex nature of spatial problems, but it also leaves room for imagination to be applied in the search for new solutions. One of the main complications we encounter when dealing with spatial problems is that they are very computationally intensive, typically taking a long time to produce the desired result. This drawback is also an opportunity to use heterogeneous systems to address spatial problems more efficiently. Heterogeneous systems give the developer greater freedom to speed up suitable algorithms by increasing the parallel programming options available, making it possible for different parts of a program to run on the dedicated hardware that suits them best. Several of the spatial problems that have not been optimised for heterogeneous systems cover very diverse areas that seem vastly different at first sight. However, they are closely related due to common data processing requirements, making them suitable for using dedicated hardware. In particular, this thesis provides new parallel approaches to tackle the following three crucial spatial problems: latent fingerprint identification, total viewshed computation, and path planning based on maximising visibility in large regions. Latent fingerprint identification is one of the essential identification procedures in criminal investigations. Addressing this task is difficult as (i) it requires analysing large databases in a short time, and (ii) it is commonly addressed by combining different methods with complex data dependencies, making it challenging to exploit parallelism on heterogeneous CPU-GPU systems. Moreover, most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time—the most accurate algorithm was designed to process the fingerprints using a single thread. We developed a new methodology to address the latent fingerprint identification problem called “Asynchronous processing for Latent Fingerprint Identification” (ALFI) that speeds up processing while maintaining high accuracy. ALFI exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism to analyse massive fingerprint databases. We assessed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results revealed that ALFI is on average 22x faster than the state-of-the-art identification algorithm, reaching a speed-up of 44.7x for the best-studied case. In terrain analysis, Digital Elevation Models (DEMs) are relevant datasets used as input to those algorithms that typically sweep the terrain to analyse its main topological features such as visibility, elevation, and slope. The most challenging computation related to this topic is the total viewshed problem. It involves computing the viewshed—the visible area of the terrain—for each of the points in the DEM. The algorithms intended to solve this problem require many memory accesses to 2D arrays, which, despite being regular, lead to poor data locality in memory. We proposed a methodology called “skewed Digital Elevation Model” (sDEM) that substantially improves the locality of memory accesses and exploits the inherent parallelism of rotational sweep-based algorithms. Particularly, sDEM applies a data relocation technique before accessing the memory and computing the viewshed, thus significantly reducing the execution time. Different implementations are provided for single-core, multi-core, single-GPU, and multi-GPU platforms. We carried out two experiments to compare sDEM with (i) the most used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm for solving the total viewshed problem. In the first experiment, sDEM results on average 8.8x faster than current GIS software, despite considering only a few points because of the limitations of the GIS software. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm considering the best case. The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown enormously in tasks involving terrain coverage, such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered from the flight path an essential goal, especially when the area to be monitored is irregular, large, and includes many blind spots. In this regard, state-of-the-art total viewshed algorithms can help analyse large areas and find new paths providing all-round visibility. We designed a new heuristic called “Visibility-based Path Planning” (VPP) to solve the path planning problem in large areas based on a thorough visibility analysis. VPP generates flyable paths that provide high visual coverage to monitor forest regions using the onboard camera of a single UAV. For this purpose, the hidden areas of the target territory are identified and considered when generating the path. Simulation results showed that VPP covers up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located in the province of Malaga (Spain). In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas

Pattern to process: methodological investigations into the formation and interpretation of spatial patterns in archaeological landscapes

My research has shown that the type of regional archaeological data analysis required by landscape archaeological approaches is an area where both theory and method are still in their infancy. High-level theories about the occurrence, scope, and effects of processes such as centralization, urbanization, and Hellenization/Romanization cannot yet be supported by middle range theory, which itself cannot be developed until the basic business of generating information of sufficient quality about the archaeological record has been tackled. Currently, archaeological data can be made to fit almost any interpretation generated, ultimately, on the basis of the ancient written sources. If we are to escape from this selfreinforcing cycle, research should perhaps no longer be focused on the classical themes generated by culture-historical approaches, but should seek its own proper field of operation. In the area of methods and methodology, I have demonstrated the pervasive influence of systematic research and visibility biases on the patterns that are present in the archaeological data generated over the past 50 years or so. There are mechanisms at work, both in the traditional archaeological interpretation of limited numbers of excavated sites and historical sources, and in the landscape archaeological approach, that cause the systematic undervaluation of unobtrusive remains. The significance of systematic biases in both the coarse site-based data sets resulting from desktop and ‘topographic’ studies and the more detailed site-based or ‘continuous’ data resulting from intensive field surveys has become much clearer as a result of the studies reported here. This should have practical consequences for the ways in which we study the existing archaeological record, plan future landscape archaeological research, and conduct field surveys. Site databases, the traditional starting point for regional archaeological studies, can no longer be taken at face value; rather, they require careful source criticism before being used to support specific arguments and hypotheses about settlement and land use dynamics. My studies have also shown that future data collection, whether through field survey, excavation or other methods, has to take place in a much more methodical manner if we are to produce data that are sufficiently standardized to be successfully exchanged, compared, and interpreted by others – guidelines for which should become embodied in an international standard defining ‘best practice in landscape archaeology’.

A Voxel-Based Approach for Imaging Voids in Three-Dimensional Point Clouds

Geographically accurate scene models have enormous potential beyond that of just simple visualizations in regard to automated scene generation. In recent years, thanks to ever increasing computational efficiencies, there has been significant growth in both the computer vision and photogrammetry communities pertaining to automatic scene reconstruction from multiple-view imagery. The result of these algorithms is a three-dimensional (3D) point cloud which can be used to derive a final model using surface reconstruction techniques. However, the fidelity of these point clouds has not been well studied, and voids often exist within the point cloud. Voids exist in texturally difficult areas, as well as areas where multiple views were not obtained during collection, constant occlusion existed due to collection angles or overlapping scene geometry, or in regions that failed to triangulate accurately. It may be possible to fill in small voids in the scene using surface reconstruction or hole-filling techniques, but this is not the case with larger more complex voids, and attempting to reconstruct them using only the knowledge of the incomplete point cloud is neither accurate nor aesthetically pleasing. A method is presented for identifying voids in point clouds by using a voxel-based approach to partition the 3D space. By using collection geometry and information derived from the point cloud, it is possible to detect unsampled voxels such that voids can be identified. This analysis takes into account the location of the camera and the 3D points themselves to capitalize on the idea of free space, such that voxels that lie on the ray between the camera and point are devoid of obstruction, as a clear line of sight is a necessary requirement for reconstruction. Using this approach, voxels are classified into three categories: occupied (contains points from the point cloud), free (rays from the camera to the point passed through the voxel), and unsampled (does not contain points and no rays passed through the area). Voids in the voxel space are manifested as unsampled voxels. A similar line-of-sight analysis can then be used to pinpoint locations at aircraft altitude at which the voids in the point clouds could theoretically be imaged. This work is based on the assumption that inclusion of more images of the void areas in the 3D reconstruction process will reduce the number of voids in the point cloud that were a result of lack of coverage. Voids resulting from texturally difficult areas will not benefit from more imagery in the reconstruction process, and thus are identified and removed prior to the determination of future potential imaging locations

Pathways to spatial cognition : a multi-domain approach SpatialTrain I

“Opening a window into the future is not an easy task. Attempting to open one in a generation after the initial launching step might seemed either idealistic, naïve or with hindsight plain driven” (Formosa, 2017, p35). The drive to introduce Spatial Information integration across the Maltese Islands was an ideal, one that brought in technology, methodologies and results. However, as in the classic GIS evolution through the decades pointers on what constitutes a spatial information system were the subject of extensive debate Initially this was driven by the Push – Pull factor where entities using the primitive systems were being pushed by the availability of a mapping system and provision of base maps and hence creating data to fit the system. Initiated in the 1960s through military use, porting the processes to the physical and urban domains in the 1980s and 1990s, further takeup was made in the environmental domains in the 1990s to 2000s and eventually to the social domain in the 2000 to 2010s. Jumping through the decades, the global explosion of GIS and Spatial awareness as well as software, methods and integrative constructs morphed GIS into an availability that made it all possible, particularly through online and web-enabled GIS. This Pull – Push factor caused entities and private organisations to finally break through by creating their own data and then going for the mapping systems that fit their needs, systems that have evolved beyond recognition, both in the proprietary and open-source/open-access arenas. [Excerpt from the Introduction by Prof. Saviour Formosa]peer-reviewe