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Employing Ontology to Capture Expert Intelligence within GEOBIA: Automation of the Interpretation Process
Abstract
The importance of remote sensing image analysis is ever increasing due to its ability to supply meaningful geographic information that informs local and global problems, such as measuring urban sprawl, mapping vegetation communities, monitoring the impacts of global climate change, and managing natural resources and urban planning. In this process of geo-object extraction, geographic object-based image analysis (GEOBIA) provides a method to identify real-world geographic objects from remotely sensed imagery. GEOBIA uses techniques analogous to those used by humans to perceive and distinguish geo-objects in imagery, usually acquired from satellite or airborne platforms. Experts use domain knowledge and measurement data extracted from remote sensing images for object-based analysis. This signifies a need for human involvement in the form of applying expert knowledge at the time of image object identification. The need for such human intervention acts as a barrier to the automation of GEOBIA processes. In this regard, knowledge representation techniques such as the use of ontologies provide possibilities for modeling expert knowledge in a manner that contributes to the further development of GEOBIA. In this chapter, we will discuss the importance of the human factors in GEOBIA. To this end, we will draw on literature from both GEOBIA and ontology use.
... Remote Sens. 2019, 11, 503 2 of 25 with real-world features into the GEOBIA process [1,3,4] in a manner that is formal, objective and transferable. One approach to this challenge is to employ an ontology to formally capture and represent the expert knowledge [4,5], referred to here as Ontology-driven GEOBIA (O-GEOBIA). Ontology helps to reduce semantic gap between high-level knowledge and low-level information. ...
... The forest-type mapping in this work is accomplished extending an Ontology-driven GEOBIA (O-GEOBIA) framework [5,6]. For this ontological framework, the ontology is developed using the structural group classification (Figure 2). ...
Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy.
What is Geographic Object-Based Image Analysis (GEOBIA)? To answer this we provide a formal definition of GEOBIA, present a brief account of its coining, and propose a key objective for this new discipline. We then, conduct a SWOT¹analysis of its potential, and discuss its main tenets and plausible future. Much still remains to be accomplished.
Relief inversion (or terrain reversal) effect is a well-known phenomenon in cartography that occurs when shadow is the main depth cue for three-dimensional shape perception. Light direction has been suggested as the main cause of this effect. However, the prevalence of relief inversion effect with regard to the changing light direction is currently not established, and there is little empirical evidence on this subject. This article systematically assesses the influence of light direction on the accuracy of landform perception in shaded relief maps (SRM). In a controlled experiment, 27 participants were asked to identify concave and convex landforms in 128 SRMs using a 5-point Likert scale where answers varied from clearly a valley to clearly a ridge. Eight different scenes were illuminated from 16 light directions to obtain the 128 SRMs. Our findings clearly demonstrate that incident light at 337.5° north-northwest (NNW) yields the highest accuracy and confidence ratings in landform identification among the investigated light directions; and leads to higher accuracy scores than at the 315° (NW) which is conventionally used in SRMs. Thus, we propose an update to this convention and recommend the light source to be placed at 337.5° when creating SRMs.
Windows are commonly used in digital image classification studies to define the local information content around a single pixel using a per-pixel classifier. Other studies use windows for characterizing the information content of a region, or group of pixels, in an area-based classifier. Research on identifying window size and shape properties, such as minimum, maximum, or optimum size of a window, is almost exclusively based on the results from automated classifications. Under the notably different hypothesis about optimum sizes of windows in automated classifications and approaches for determining such optimum window size, this article presents a cognitive approach for evaluating the functional relationship between window size and classification accuracy. Using human subjects, a randomized experimental design, and a continuum of window sizes, portions of digital aerial photographs were classified into urban land-use classes. Unlike the findings from purely automated approaches, classification accuracy from visual analysis increased in a monotonic form with increasing window size for the urban land-use classes investigated. A minimum window size of 40 by 40 pixels (60-m by 60-m ground area) was required for classifying Level II urban land use using 1.5-m by 1.5-m resolution data (≤ 75 percent accuracy). This finding is dramatically different from the 'ideal' window size range (i.e., 3 by 3 to 9 by 9) and functional relation between window size and classification accuracy found in automated per-pixel classifications. A theoretical curve depicting the relationship between classification accuracy and window size, spatial resolution, and classification specificity is presented.
Cirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirques may help research on glacial geomorphology and climate change. Our objective was to test the transferability
of an object-based rule set for the extraction of glacial cirques, using lidar data and color-infrared orthophotos. In Vorarlberg (W-Austria), we selected one training area with well-developed cirque components to parameterize segmentation and classification criteria. The rule set was applied
to three test areas that are positioned in three altitudinal zones. Results indicate that the rule set was successful (81 percent) in the training area and a higher situated area (71 percent). Accuracy decreased in the two lower situated test areas (66 percent and 51 percent). We conclude
that rule sets are transferable to areas with a comparable geomorphological history. Yet, significant deviation from the training area requires a different extraction strategy.
Route memory is frequently assessed in virtual environments. These environments can be presented in a fully controlled manner and are easy to use. Yet they lack the physical involvement that participants have when navigating real environments. For some aspects of route memory this may result in reduced performance in virtual environments. We assessed route memory performance in four different environments: real, virtual, virtual with directional information (compass), and hybrid. In the hybrid environment, participants walked the route outside on an open field, while all route information (i.e., path, landmarks) was shown simultaneously on a handheld tablet computer. Results indicate that performance in the real life environment was better than in the virtual conditions for tasks relying on survey knowledge, like pointing to start and end point, and map drawing. Performance in the hybrid condition however, hardly differed from real life performance. Performance in the virtual environment did not benefit from directional information. Given these findings, the hybrid condition may offer the best of both worlds: the performance level is comparable to that of real life for route memory, yet it offers full control of visual input during route learning.
Scene gist, a viewer's holistic representation of a scene from a single eye fixation, has been extensively studied for terrestrial views, but not for aerial views. We compared rapid scene categorization of both views in three experiments to determine the degree to which diagnostic information is view dependent versus view independent.We found large differences in observers' ability to rapidly categorize aerial and terrestrial scene views, consistent with the idea that scene gist recognition is viewpoint dependent.In addition, computational modeling showed that training models on one view (aerial or terrestrial) led to poor performance on the other view, thereby providing further evidence of viewpoint dependence as a function of available information. Importantly, we found that rapid categorization of terrestrial views (but not aerial views) was strongly interfered with by image rotation, further suggesting that terrestrial-view scene gist recognition is viewpoint dependent, with aerial-view scene recognition being viewpoint independent. Furthermore, rotation-invariant texture images synthesized from aerial views of scenes were twice as recognizable as those synthesized from terrestrial views of scenes (which were at chance), providing further evidence that diagnostic information for rapid scene categorization of aerial views is viewpoint invariant. We discuss the results within a perceptual-expertise framework that distinguishes between configural and featural processing, where terrestrial views are more effectively processed due to their predictable view-dependent configurations whereas aerial views are processed less effectively due to reliance on view-independent features.
http://urn.fi/URN:ISBN:978-951-711-312-0
This dissertation applies the research and methods of spatial cognition in order to contribute to the development of wayfinding support in geospatial applications. The design and development of geospatial applications, such as interactive maps and mobile navigation applications, has been typically founded on the
expertise of surveying, cartography and geoinformatics. This has often led to relatively complex expert tools that many users find difficult to use. The research of spatial cognition can provide elementary understanding about human thinking in the use situations of these applications and supplement the knowledge gained using the usability research.
Perception of landmarks along routes in nature was studied in season and time-of-day studies with participants who walked nature trails in summer, winter, day and night while thinking aloud about the surroundings. The recall of the route was measured afterwards using sketch-map drawing and photo recognition tasks. The think-aloud protocols were analysed using classification of propositions and natural language processing. The importance of landmarks for the human route perception in nature was confirmed.
“Structures”, “Passages” and “Waters” were the most perceived landmark groups. Season and time-of-day significantly affected landmark perception and, based on the results, the adaptivity of geospatial applications in the studied conditions can be improved. The transfer of the empirically acquired knowledge of the landmark perception to geoinformatics was illustrated with a formal landmark ontology for hiking in nature.
The measures of landmark recall were found unexpectedly similar in all the studied conditions. The similarity was explained by the salience of landmarks in nature and the structure of route-like sketch maps but also by the participants’ conceptions on what should be drawn on maps. “Passages” and “Structures” were the most-often drawn landmark groups on the sketch maps.
Support of geospatial images for wayfinding was evaluated using a literature-based evaluation framework. Visualisation of elevation was experimented with by rendering a 3D map, a derivative of which was compared to 2D elevation visualisations in an eye-tracking study. Vertical elements and elevation were found to be central wayfinding elements in geo-images and the aerial oblique vantage point the most effective image parameter for transferring spatial knowledge. The rendered 3D map was evaluated as cognitively demanding to look at but, however, showed potential in representing the terrain relief.
The dissertation also considers challenges in the application of the methods of spatial cognition research and identifies directions for future studies.
Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects’ properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).
The images contained in the Operation Orchard public archive represent a convergence of technologies, politics, and warcraft that can only emerge across a century of air power. Their interpretation refers to older modes of camouflage and deceptive practices from the espionage and tactical manuals of an earlier era in modernity even as the virtual and digital practices of the twenty-first century appear to destabilize the powers of legitimation that have come to surround the iconic reconnaissance photograph. Sincerely or disingenuously, media and analysts alike accept the circulation of reconnaissance imagery as objective news imbued with the legitimating aura of documentary material. If we accept the image as full of information that can be deciphered definitively, a kind of ground truth, we close off the possibility of learning from other sources or even from the complex state of uncertainty itself. In this context, we might have to concede that deception is not so much the obliteration of facts, as Virilio maintained just after the first Persian Gulf War, but the profuse and uneven dissemination of fact as visual data.
The terrain reversal effect is a perceptual phenomenon which causes an illusion in various 3D geographic visualizations where landforms appear inverted, e.g. we perceive valleys as ridges and vice versa. Given that such displays are important for spatio-visual analysis, this illusion can lead to critical mistakes in interpreting the terrain. However, it is currently undocumented how commonly this effect is experienced. In this paper, we study the prevalence of the terrain reversal effect in satellite imagery through a two-stage online user experiment. The experiment was conducted with the participation of a diverse and relatively large population (n = 535). Participants were asked to identify landforms (valley or ridge?) or judge a 3D spatial relationship (is A higher than B?). When the images were rotated by 180°, the results were reversed. In a control task with ‘illusion-free’ original images, people were successful in identifying landforms, yet a very strong illusion occurred when these images were rotated 180°. Our findings demonstrate that the illusion is acutely present; thus, we need a better understanding of the problem and its solutions. Additionally, the results caution us that in an interactive environment where people can rotate the display, we might be introducing a severe perceptual problem.
This book argues that the human cognition system is the least understood, yet probably most important, component of forecasting accuracy. Minding the Weather investigates how people acquire massive and highly organized knowledge and develop the reasoning skills and strategies that enable them to achieve the highest levels of performance. The authors consider such topics as the forecasting workplace; atmospheric scientists’ descriptions of their reasoning strategies; the nature of expertise; forecaster knowledge, perceptual skills, and reasoning; and expert systems designed to imitate forecaster reasoning. Drawing on research in cognitive science, meteorology, and computer science, the authors argue that forecasting involves an interdependence of humans and technologies. Human expertise will always be necessary.
Experts, who were the sole active dispensers of certain kinds of knowledge in the days before AI, have now often assumed a rather passive role. They relay their knowledge to various novices, knowledge engineers, experimental psychologists or cognitivists - or other experts! - involved in the development and understanding of expert systems. This book achieves a perfect marriage between experimentalists and theoreticians who deal with expertise. It tries to establish the benefits to society of an advanced technology for representing and disseminating the knowledge and skills of the best corporate managers, the most seasoned pilots, and the most renowned medical diagnosticians. This book interests psychologists as well as all those out in the trenches developing expert systems, and everyone pondering the nature of expertise and the question of how it can be studied scientifically. Its scope, the pivotal concepts which it elucidates and brilliantly summarizes and appraises in the final chapter, as well as the references it includes, make this book a landmark in the field.
Broadly, this paper is about designing memorable 3D geovisualizations for spatial knowledge acquisition during (virtual) navigation. Navigation is a fundamentally important task, and even though most people navigate every day, many find it difficult in unfamiliar environments. When people get lost in an unfamiliar environment, or are unable to remember a route that they took, they might feel anxiety, disappointment and frustration; and in real world, such incidents can be costly, and at times, life-threatening. Therefore, in this paper, we study the design decisions in terms of visual realism in a city model, propose a visualization design optimized for route learning, implement and empirically evaluate this design. The evaluation features a navigational route learning task, where we measure short- and long-term recall accuracy of 42 participants with varying spatial abilities and memory capacity. Our findings provide unique empirical evidence on how design choices affect memory in route learning with geovirtual environments, contributing toward empirically verified design guidelines for digital cities.
We report on how visual realism might influence map-based route learning performance in a controlled laboratory experiment with 104 male participants in a competitive context. Using animations of a dot moving through routes of interest, we find that participants recall the routes more accurately with abstract road maps than with more realistic satellite maps. We also find that, irrespective of visual realism, participants with higher spatial abilities (high-spatial participants) are more accurate in memorizing map-based routes than participants with lower spatial abilities (low-spatial participants). On the other hand, added visual realism limits high-spatial participants in their route recall speed, while it seems not to influence the recall speed of low-spatial participants. Competition affects participants’ overall confidence positively, but does not affect their route recall performance neither in terms of accuracy nor speed. With this study, we provide further empirical evidence demonstrating that it is important to choose the appropriate map type considering task characteristics and spatial abilities. While satellite maps might be perceived as more fun to use, or visually more attractive than road maps, they also require more cognitive resources for many map-based tasks, which is true even for high-spatial users.
Spatial information acquisition happens in large part through
the visual sense. Studying visual attention and its connection to
cognitive processes has been the interest of many research efforts
in spatial cognition over the years. Recent technological
developments have led to an increasing popularity of eye-tracking
methodology for investigating research questions related to
spatial cognition, geographic information science (GIScience) and
cartography. At the same time, eye trackers can nowadays be used
as an input device for (cognitively engineered) user interfaces to
geographic information. We provide an overview of the most
recent literature advancing and utilizing eye-tracking methodology
in these fields, introduce the research articles in this Special
Issue, and discuss challenges and opportunities for future research.
People are good at rapidly extracting the "gist" of a scene at a glance, meaning with a single fixation. It is generally presumed that this performance cannot be mediated by the same encoding that underlies tasks such as visual search, for which researchers have suggested that selective attention may be necessary to bind features from multiple preattentively computed feature maps. This has led to the suggestion that scenes might be special, perhaps utilizing an unlimited capacity channel, perhaps due to brain regions dedicated to this processing. Here we test whether a single encoding might instead underlie all of these tasks. In our study, participants performed various navigation-relevant scene perception tasks while fixating photographs of outdoor scenes. Participants answered questions about scene category, spatial layout, geographic location, or the presence of objects. We then asked whether an encoding model previously shown to predict performance in crowded object recognition and visual search might also underlie the performance on those tasks. We show that this model does a reasonably good job of predicting performance on these scene tasks, suggesting that scene tasks may not be so special; they may rely on the same underlying encoding as search and crowded object recognition. We also demonstrate that a number of alternative "models" of the information available in the periphery also do a reasonable job of predicting performance at the scene tasks, suggesting that scene tasks alone may not be ideal for distinguishing between models.
Digital aerial and satellite images are easily accessible for public use and frequently employed in social media. The widespread publishing of aerial and satellite images has raised questions about how non-experts interpret these images, how they are able to interpret orthogonal and oblique images and true-colour and not-true-colour images and how they contend with an absence of text or object descriptions in these documents. Research participants (11-, 15- and 19-year-old students) were asked to solve spatial tasks in images and maps of various types. Differences in the efficiency of task solution regarding various types of source documents were analysed, and the generated scores were evaluated according to the participants’ age and gender. Schoolchildren and students were asked to provide their opinions on the difficulty of reading the various image types and their preference for either maps or images as a source for acquiring information. From our study we conclude that:1.
Age has an impact on the efficiency of image and map interpretation. Younger students, for example, 11-year-olds are better at handling tasks in images, while for 15-year-olds the difference between handling tasks in images and maps is much smaller. Lastly, 19-year-old students can better solve tasks in maps.
2.
The efficiency of interpretation varies based on gender and source material. The efficiency of solving tasks in aerial images is comparable. Eleven-year-old girls can handle tasks in maps better than boys of the same age. In the groups comprised of 15- and 19-year-olds, the boys are much more successful.
3.
The results of the evaluation of objective efficiency regarding image and map interpretation correspond with the subjective preference for maps or images: 11-year-old students prefer images; 19-year-old students prefer maps.
4.
Not-true-colour images are subjectively considered very difficult to interpret, but objective results do not confirm this. Students achieved higher scores for tasks in not-true-colour images than in true-colour images. The purpose of this research is to discover effective teaching methods in geography and to support the integration of aerial and satellite images in education.
Proposes models of the spatial knowledge people acquire from maps and navigation and the procedures required for spatial judgments using this knowledge. From a map people acquire survey knowledge encoding global spatial relations. This knowledge resides in memory in images that can be scanned and measured like a physical map. From navigation people acquire procedural knowledge of the routes connecting diverse locations. People combine mental simulation of travel through the environment and informal algebra to compute spatial judgments. An experiment in which subjects learned an environment from navigation or from a map evaluates predictions of these models. -from Selected Rand Abstracts
Perceiving the visual world around us requires the brain to represent the features of stimuli and to categorize the stimulus based on these features. Incorrect categorization can result either from errors in visual representation or from errors in processes that lead to categorical choice. To understand the temporal relationship between the neural signatures of such systematic errors, we recorded whole-scalp magnetoencephalography (MEG) data from human subjects performing a rapid scene categorization task. We built scene-category decoders based on (1) spatiotemporally-resolved neural activity, (2) spatial-envelope (SpEn) image features, and (3) behavioral responses. Using confusion matrices, we tracked how well the pattern of errors from neural decoders could be explained by SpEn decoders and behavioral errors, over time and across cortical areas. Across the visual cortex and the medial temporal lobe, we found that both SpEn and behavioral errors explained unique variance in the errors of neural decoders. Critically, these effects were nearly simultaneous, and most prominent between 100 and 250ms after stimulus onset. Thus, during rapid scene categorization, neural processes that ultimately result in behavioral categorization are simultaneous and co-localized with neural processes underlying visual information representation.
Most of the visual field is peripheral, and the periphery encodes visual input with less fidelity compared to the fovea. What information is encoded, and what is lost in the visual periphery? A systematic way to answer this question is to determine how sensitive the visual system is to different kinds of lossy image changes compared to the unmodified natural scene. If modified images are indiscriminable from the original scene, then the information discarded by the modification is not important for perception under the experimental conditions used. We measured the detectability of modifications of natural image structure using a temporal three-alternative oddity task, in which observers compared modified images to original natural scenes. We consider two lossy image transformations, Gaussian blur and Portilla and Simoncelli texture synthesis. Although our paradigm demonstrates metamerism (physically different images that appear the same) under some conditions, in general we find that humans can be capable of impressive sensitivity to deviations from natural appearance. The representations we examine here do not preserve all the information necessary to match the appearance of natural scenes in the periphery.
The computer creates opportunities for the cartographer to work on map types which are, without the use of the computer difficult or laborious to produce (Taylor, 1984). This is certainly valid when the third dimension is involved. A greater interest in map types such as prism maps and digital terrain models can now be seen. The combination of these observations instigated the research project described here: computer-assisted cartographical three-dimensional imaging techniques. Its objective is to see what characterizes three-dimensional maps and determine whether indeed three-dimensional maps produced by computer-assisted cartography give the map user a better understanding of the mapped phenomena. -from Author
This book presents the latest work in the area of naturalistic decision making (NDM) and its extension into the area of macrocognition. It contains 18 chapters relating research centered on the study of expertise in naturalistic settings, written by international experts in NDM and cognitive systems engineering. The objective of the book is to present the reader with exciting new developments in this field of research, which is characterized by its application-oriented focus. The work addresses only real-world problems and issues. For instance, how do multi-national teams collaborate effectively? How can surgeons best be supported by technology? How do detectives make sense of complex criminal cases? In all instances the studies have been carried out on experts within their respective domains. The traditional field of NDM is extended in this work by focusing on macrocognitive functions other than decision making, namely sense-making, coordination and planning. This has broadened the scope of the field. The book also contains a theoretical discussion of the macro-micro distinction. Naturalistic Decision Making and Macrocognition will be relevant to graduate students, researchers and professionals (including professionals and researchers in business, industry and government) who are interested in decision making, expertise, training methods and system design. The material may be used in two ways: theoretically, to advance understanding of the field of naturalistic decision making; and practically, to gain insight into how experts in various domains solve particular problems, understand and deal with issues and collaborate with others. © Jan Maarten Schraagen, Laura G. Militello, Tom Ormerod and Raanan Lipshitz 2008.
Non-photorealistic computer graphics offers a large potential for developing specialized visualization techniques in multimedia cartography such as for illustrative, artistic, and informal information display. Most importantly, non-photorealism provides excellent means for visual abstraction as a primary technique to effectively communicate complex spatial information and not only allows us to implement traditional cartographic visualization techniques as part of interactive visualization applications, but also offers an excellent foundation for developing new cartographic concepts for visualizing geoinformation such as illustrative city models and landscape models.
Non-photorealistic geovisualization also raises several questions with respect to its impact to map-like and cartographic presentations. What are specific non-photorealistic rendering techniques for interactive map-based depictions? How do non-photorealistic geovisualizations cooperate with photorealistic ones, how to combine them? What are the specific elements of photorealism and non-photorealism in the human visual processing, and how to take advantage of these abilities for a more effective visual communication?
Speed in acquiring the knowledge and skills to perform tasks is crucial. Yet, it still ordinarily takes many years to achieve high proficiency in countless jobs and professions, in government, business, industry, and throughout the private sector. There would be great advantages if regimens of training could be established that could accelerate the achievement of high levels of proficiency. This book discusses the construct of ‘accelerated learning.’ it includes a review of the research literature on learning acquisition and retention, focus on establishing what works, and why. This includes several demonstrations of accelerated learning, with specific ideas, plans and roadmaps for doing so. The impetus for the book was a tasking from the Defense Science and Technology Advisory Group, which is the top level Science and Technology policy-making panel in the Department of Defense. However, the book uses both military and non-military exemplar case studies.
Eye-tracking technology was not fully utilized in the cartography or geosciences yet. With its use, ways of users reading of maps can be revealed, and maps can be optimized. Before eye-tracking evaluation, it is necessary to detect eye-movements like fixations and saccades. Paper deals with the evaluation of four different settings of the dispersion-based algorithm for optimal fixation detection for evaluation of maps. Data for the case study originated from the cartographic studies, where stimuli displayed the classical orthogonal maps and their 3D (perspective) equivalents. The paper describes the basics of eye movement recordings, its history and used methods. An emphasis is placed on the remote methods, which uses digital images of the front of the eye. Image recognition methods are used for detecting the position of the eye. From these data, fixations and saccades are calculated. Number of a detection algorithm exists. For the data recorded with a sampling frequency lower than 250Hz, IDT is mostly used. It is essential to find out the best settings of this algorithm. In the case study, three existing threshold settings were compared with the new one. Calculated fixations were compared with the plot of raw data, and the most relevant settings were chosen. For eye-movement analysis, it is possible to use software from the manufacturer, but there exist also the open-source alternative. Software OGAMA is fully sufficient for data analyses, but fixation detection thresholds are entered in a different way. Paper also describes the optimal fixation detection settings in this software. As a result, the fixation detection thresholds for I-DT which represents the recorded data at the best were created. Subsequently, the most corresponding settings were chosen in the open-source software for gaze analyses. The optimal eye fixation detection settings can be used in other cartographic studies using eye-tracking as the main evaluation method.
Formalizing a verbal theory in a model forces explicit consideration about all components of the theory, allowing those theories to be rigorously evaluated and potentially falsified. The challenge in developing a process model of perceptual expertise is that the empirical phenomena span many areas of perception and cognition. For example, a successful model of expertise has to account for differences between novices and experts in terms of automatic strategies, speed of perceptual decisions, level of categorization, patterns of interference, processing costs and generalization, and the timing of neural activity. If perceptual expertise is the end point of normal learning, the development of perceptual expertise can be explored using extant models of normal visual cognition. Indeed, a number of key expertise effects can be explained using models of normal object recognition and perceptual categorization without having to invoke qualitative changes. © 2010 by Isabel Gauthier, Michael J. Tarr, and Daniel Bub. All rights reserved.
This book covers the latest research on landmarks in GIS, including practical applications. It addresses perceptual and cognitive aspects of natural and artificial cognitive systems, computational aspects with respect to identifying or selecting landmarks for various purposes, and communication aspects of human-computer interaction for spatial information provision. Concise and organized, the book equips readers to handle complex conceptual aspects of trying to define and formally model these situations. The book provides a thorough review of the cognitive, conceptual, computational and communication aspects of GIS landmarks. This review is unique for comparing concepts across a spectrum of sub-disciplines in the field. Portions of the ideas discussed led to the world’s first commercial navigation service using landmarks selected with cognitive principles. Landmarks: GI Science for Intelligent Services targets practitioners and researchers working in geographic information science, computer science, information science, cognitive science, geography and psychology. Advanced-level students in computer science, geography and psychology will also find this book valuable as a secondary textbook or reference.
This volume is the first comprehensive history of task analysis, charting its origins from the earliest applied psychology through to modern forms of task analysis that focus on the study of cognitive work. Through this detailed historical analysis, it is made apparent how task analysis has always been cognitive.
In Geographic Object-Based Image Analysis (GEOBIA) an image is partitioned into objects by a segmentation algorithm. These objects are then classified into semantic categories based on unsupervised/supervised methods, or knowledge-based methods, such as an ontology. The aim of this
paper was to develop a SPatial Ontology Reasoner (SPOR) to allow the development of GEOBIA ontologies by employing fuzzy, spatial, and multi-scale representations, with time efficiency. An enhanced version of the Web Ontology Language 2 (OWL2) with fuzzy representations was adopted and expanded
to represent fuzzy spatial relationships within the framework of GEOBIA . Segmentation results are stored within PostgreSQL. An ontology described the class/subclass hierarchy and class definitions. SPOR integrated PostgreSQL and the ontology, to classify the objects. To demonstrate the framework,
a QuickBird image was employed for building extraction. Accuracy assessment indicated that 87 percent of building rooftops were detected.
This chapter discusses cognitive processes and cartographic maps. Maps are important in daily life. The efficiency of the cognitive processes used by people interacting with maps is a key issue for geographers. Research on the cartographic map has naturally focused on their construction. The selection, classification, simplification, and symbolization decisions made by cartographers shape their maps. Cognitive maps are, in turn, shaped by these cartographic products. A good example of this relationship is the impact of map projections on the impressions of countries. The sizes and shapes of countries in a person's cognitive map undoubtedly are related to how that information was represented on frequently encountered cartographic maps. The studies discussed in the chapter shows that other variables, such as a map's complexity, orientation, and color design, also impacted people's ability to efficiently use the map. In addition, research on the map reader has measured reaction times and error patterns. They have focused on differences between cognitive maps encoded as primary or secondary information and using verbal or imagery processes.
ABSTRACT Studies in scene ,perception ,have ,shown ,that observers,recognize ,a real-world ,scene ,at a ,single glance. During this expeditious process of seeing, the visual system ,forms ,a spatial ,representation ,of the outside world,that is rich enough,to grasp the meaning of the scene, recognizing a few objects and other salient information in the image, to facilitate object detection and the deployment ,of attention. This representation refers to the gist of a scene, which includes all levels of processing, from low-level features (e.g., color, spatial frequencies) to intermediate image properties (e.g., surface, volume) and high-level information (e.g., objects, activation of semantic knowledge). Therefore, gist can be studied,at both,perceptual,and conceptual levels. I. WHAT IS THE “GIST OF A SCENE”?
The late Heinrich Berann, from Austria, was generally regarded as the most accomplished panoramist of all time. During the decade before his retirement in 1994, Berann painted four panoramas for the U.S. National Park Service (NPS) that demonstrated his genius for landscape visualization. This paper examines the widely admired, but little understood, vocation of panorama making, with emphasis on Berann’s NPS pieces, concepts, and techniques. Explanation is offered about how the panorama for Denali National Park, Alaska, was planned, compiled, sketched, and painted—starting from a blank sheet of paper. Berann’s techniques for landscape manipulation are then analyzed, including his unorthodox habit of rotating mountains and widening valleys, and his unique interpretations of vertical exaggeration. His graphical special effects used for portraying realistic environments are reviewed. The paper finishes with illustrations that compare Berann’s panoramas to digitallygenerated landscapes.
The landscape concept in geography has recently been adopted by humanistic writers because of its holistic and subjective implications. But the history of the landscape idea suggests that its origins lie in the renaissance humanists' search for certainty rather than a vehicle of individual subjectivity. Landscape was a 'way of seeing' that was bourgeois, individualist and related to the exercise of power over space. The basic theory and technique of the landscape way of seeing was linear perspective, as important for the history of the graphic image as printing was for that of the written word. Alberti's perspective was the foundation of realism in art until the nineteenth century, and is closely related by him to social class and spatial hierarchy. It employs the same geometry as merchant trading and accounting, navigation, land survey, mapping and artillery. Perspective is first applied in the city and then to a country subjugated to urban control and viewed as landscape. The evolution of landscape painting parallels that of geometry just as it does the changing social relations on the land in Tudor, Stuart and Georgian England. The visual power given by the landscape way of seeing complements the real power humans exert over land as property. Landscape as a geographical concept cannot be free of the ideological overlays of its history as a visual concept unless it subjects landscape to historical interrogation. Only as an unexamined concept in a geography which neglects its own visual foundations can landscape be appropriated for an antiscientific humanistic geography.
Interactive 3D geo-browsers, also known as globe viewers, are popular, because they are easy and fun to use. However, it is still an open question whether highly interactive, 3D geographic data browsing, and visualization displays support effective and efficient spatio-temporal decision making. Moreover, little is known about the role of time constraints for spatio-temporal decision-making in an interactive, 3D context. In this article, we present an empirical approach to assess the effect of decision-time constraints on the quality of spatio-temporal decision-making when using 3D geo-browsers, such as GoogleEarth, in 3D task contexts of varying complexity. Our experimental results suggest that while, overall, people interact more with interactive geo-browsers when not under time pressure, this does not mean that they are also more accurate or more confident in their decisions when solving typical 3D cartometric tasks. Surprisingly, we also find that 2D interaction capabilities (i.e., zooming and panning) are more frequently used for 3D tasks than 3D interaction tools (i.e., rotating and tilting), regardless of time pressure. Finally, we find that background and training of tested users do not seem to influence 3D task performance. In summary, our study does not provide any evidence for the added value of using interactive 3D globe viewers when needing to solve 3D cartometric tasks with or without time pressure.