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Seipel, S. & Lim, N. J. (2017). Color map design for visualization in flood risk assessment. International Journal of Geographical Information Science, 31(11), 2286-2309
Open this publication in new window or tab >>Color map design for visualization in flood risk assessment
2017 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 31, no 11, p. 2286-2309Article in journal (Refereed) Published
Abstract [en]

Visualizations of flood maps from simulation models are widely used for assessing the likelihood of flood hazards in spatial planning. The choice of a suitable type of visualization as well as efficient color maps is critical to avoid errors or bias when interpreting the data. Based on a review of previous flood uncertainty visualization techniques, this paper identifies areas of improvements and suggests criteria for the design of a task-specific color scale in flood map visualization. We contribute a novel color map design for visualizing probabilities and uncertainties from flood simulation ensembles. A user study encompassing 83 participants was carried out to evaluate the effects of this new color map on user’s decisions in a spatial planning task. We found that the type of visualization makes a difference when it comes to identification of non-hazardous sites in the flood risk map and when accepting risks in more uncertain areas. In comparison with two other existing visualization techniques, we observed that the new design was superior both in terms of task compliance and efficiency. In regions with uncertain flood statuses, users were biased toward accepting less risky locations with our new color map design.

Keywords
color scales, ensemble modeling, flood maps, user study, Visualization
National Category
Physical Geography Other Computer and Information Science
Identifiers
urn:nbn:se:hig:diva-24871 (URN)10.1080/13658816.2017.1349318 (DOI)000408205100009 ()2-s2.0-85023743866 (Scopus ID)
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2018-10-10Bibliographically approved
Åhlén, J., Seipel, S. & Kautz, M.-L. (2017). Data source evaluation for shoreline deliniation applications. In: International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM: Conference proceedings. Paper presented at 17th International Multidisciplinary Scientific GeoConference SGEM 2017,27 June - 6 July, 2017, Albena, Bulgaria (pp. 849-858). , 17(2-3)
Open this publication in new window or tab >>Data source evaluation for shoreline deliniation applications
2017 (English)In: International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM: Conference proceedings, 2017, Vol. 17, no 2-3, p. 849-858Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes an evaluation of data acquired with various sensors and used in coastal water segmentation applications. Correct monitoring of coastal changes in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. Automatic shoreline delineation methods are preferable, especially in terms of time, cost, labor intensiveness and difficulties of in-situ measurements. Two main issues have been encountered within this application field, the quality of data and the segmentation algorithms. In this work, potential benefits of various data sources including optical and active sensors for extraction of shorelines have been investigated. The goal with shoreline detection from digital data sources is to obtain information as efficiently as possible and as reliably as necessary. Starting with that observation the paper discusses the effectiveness of coastal information extraction provided different data sources. This question is especially important to address since we observe a fast development of high spatial resolution data acquisition. There are many of segmentation algorithms described in the field of image processing and yet there is currently no single theory or method, no universal segmentation framework, that can be applied on all images to precisely and robustly extract shorelines. Nether there is a uniform standard for the assessment of segmentation results, and this process still largely relies on visual analysis and personal judgment. Out of myriads of image segmentation algorithms, we chose the most frequently and successfully applied within the application field and considering the data sources. In optical sensor data cases, the most frequently used methods are NDWI (Normalized Difference Water Index) and thresholding techniques. We do not aim to create yet another method to segment out the particular objects from remotely sensed data and then tailor it to work efficiently on that data set. Instead, we evaluate the data quality regarding the given application field. The case study is carried out on a 10 km coastal stretch facing the Baltic Sea (Sweden) and belonging to the Municipality of Gävle. In citu measurements were acquired to evaluate the extracted coastal lines and comparisons with reference were performed based on the average mean distance. A conclusion is done regarding the most reliable data source for this particular application of shoreline delineation.

Series
International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM : Conference proceedings, ISSN 1314-2704 ; 21
Keywords
Data source, Evaluation, Segmentation, Shoreline
National Category
Computer and Information Sciences Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:hig:diva-25424 (URN)10.5593/sgem2017/21/S08.108 (DOI)2-s2.0-85032471686 (Scopus ID)978-619-7408-01-0 (ISBN)
Conference
17th International Multidisciplinary Scientific GeoConference SGEM 2017,27 June - 6 July, 2017, Albena, Bulgaria
Available from: 2017-10-18 Created: 2017-10-18 Last updated: 2018-03-13Bibliographically approved
Seipel, S., Milutinovic, G. & Andrée, M. (2016). 3D game technology in property formation. In: 16th International Multidisciplinary Scientific GeoConference SGEM 2016: SGEM2016 Conference Proceedings : Book 2. Paper presented at 16th International Multidisciplinary Scientific GeoConference SGEM 2016, June 28 - July 6, 2016, Albena Resort, Bulgaria (pp. 539-546).
Open this publication in new window or tab >>3D game technology in property formation
2016 (English)In: 16th International Multidisciplinary Scientific GeoConference SGEM 2016: SGEM2016 Conference Proceedings : Book 2, 2016, p. 539-546Conference paper, Published paper (Refereed)
Abstract [en]

The process of real property formation involves the analysis and assessment of legal documents and cadastral information available in digital form. Quite frequently, however, it is necessary to visit the sites to establish relevant information from the real land parcels as well as communicating with involved stakeholders in the natural environment, entailing substantial cost in terms of time and travel expenses. The objective of the work presented here is to investigate alternative, IT-based processes for property formation which draw on existing data and have the potential to substitute time- and cost-intensive field visits. More specifically, the presented study explores how 3D game-based technology can be used to facilitate virtual site visits as an alternative to physical field surveys. We approach this problem by suggesting a framework that enables interoperability of existing 3D terrain models from the national land survey as well as vector data from cadastral databases with existing gaming environments for interactive exploration. Following an analysis of the quality of the existing digital terrain data, we describe an alternative data-extraction pathway that is suitable for rendering of 3D terrain models in the game engine. We present some visual results of our 3D demo system which indicate that salient structures in the terrain relevant for assessment and establishing of property boundaries are readily accessible in the virtual environment. Results of a quantitative comparison of the tested data models also support what visual inspection suggests, that existing terrain data can be refined for use of virtual site visits for property formation.

Series
SGEM2016 Conference Proceedings, ISSN 1314-2704
Keywords
3D visualization, LiDAR, game engine, cadaster, property formation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hig:diva-22784 (URN)10.5593/SGEM2016/B21/S08.068 (DOI)000395499400068 ()978-619-7105-58-2 (ISBN)
Conference
16th International Multidisciplinary Scientific GeoConference SGEM 2016, June 28 - July 6, 2016, Albena Resort, Bulgaria
Available from: 2016-11-18 Created: 2016-11-18 Last updated: 2019-01-08Bibliographically approved
Åhlén, J. & Seipel, S. (2016). Segmentation of shadows and water bodies in high resolution images using ancillary data. In: 16th International Multidisciplinary Scientific GeoConference SGEM 2016: SGEM2016 Conference Proceedings : Book 2. Paper presented at 16th International Multidisciplinary Scientific GeoConference SGEM 2016, June 28 - July 6, 2016, Albena Resort, Bulgaria (pp. 827-834). , 1
Open this publication in new window or tab >>Segmentation of shadows and water bodies in high resolution images using ancillary data
2016 (English)In: 16th International Multidisciplinary Scientific GeoConference SGEM 2016: SGEM2016 Conference Proceedings : Book 2, 2016, Vol. 1, p. 827-834Conference paper, Published paper (Refereed)
Abstract [en]

High spatial resolution imagery is often affected by shadows, both in urban environments with large variations in surface elevation and in vegetated areas. It is a common bias in classification when waters and shadows are registered as the same area. The radiometric response for the shadowed regions should be restored prior to classification. To enable that, separate classes of non-shadowed regions and shadowed areas should be created. Previous work on water extraction using low/medium resolution images, mainly faced two difficulties. Firstly, it is difficult to obtain accurate position of water boundary and secondly, shadows of elevated objects e.g. buildings, bridges, towers and trees are a typical source of noise when facing water extraction in urban regions. In high resolution images the problem of separation water and shadows becomes more prominent since the small local variation of intensity values gives rise to misclassification. This paper proposes a robust method for separation of shadowed areas and water bodies in high spatial resolution imagery using hierarchical method on different scales combined with classification of PCA (Principal Component Analysis) bands, which reduces the effects of radiometric and spatial differences that is commonly associated with the pixel-based methods for multisource data fusion. The method uses ancillary data to aid in classification of shadows and waters. The proposed method includes three steps: segmentation, classification and postprocessing. To achieve robust segmentation, we apply the merging region with three features (PCA bands, NSVDI (Normalized Saturation-value Difference Index) and height data). NSVDI discriminates shadows and some water. In the second step we use hierarchic region based classification to identify water regions. After that step candidates for water pixels are verified by the LiDAR DEM data. As a last step we consider shape parameters such as compactness and symmetry to completely remove shadows.

Series
SGEM2016 Conference Proceedings, ISSN 1314-2704
Keywords
shadows, water bodies, objects, segmentation, high-resolution
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hig:diva-22783 (URN)10.5593/SGEM2016/B21/S08.104 (DOI)000395499400104 ()978-619-7105-58-2 (ISBN)
Conference
16th International Multidisciplinary Scientific GeoConference SGEM 2016, June 28 - July 6, 2016, Albena Resort, Bulgaria
Available from: 2016-11-18 Created: 2016-11-18 Last updated: 2018-03-13Bibliographically approved
Nyström, I., Olsson, P., Nysjö, J., Nysjö, F., Malmberg, F., Seipel, S., . . . Carlbom, I. B. (2016). Virtual Cranio-Maxillofacial Surgery Planning with Stereo Graphics and Haptics (1ed.). In: Ritacco, L .E. and Milano, F. E. and Chao, E. (Ed.), Computer-Assisted Musculoskeletal Surgery: Thinking and Executing in 3D: (pp. 29-42). Springer Publishing Company
Open this publication in new window or tab >>Virtual Cranio-Maxillofacial Surgery Planning with Stereo Graphics and Haptics
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2016 (English)In: Computer-Assisted Musculoskeletal Surgery: Thinking and Executing in 3D / [ed] Ritacco, L .E. and Milano, F. E. and Chao, E., Springer Publishing Company, 2016, 1, p. 29-42Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer Publishing Company, 2016 Edition: 1
Keywords
Cranio-Maxillofacial Surgery, Virtual Surgery Planning, Haptics, 3D Visualization, Image Segmentation
National Category
Human Computer Interaction Computer Sciences
Identifiers
urn:nbn:se:hig:diva-20628 (URN)9783319129426 (ISBN)
Available from: 2015-11-23 Created: 2015-11-23 Last updated: 2018-03-13Bibliographically approved
Lim, N. J., Brandt, S. A. & Seipel, S. (2016). Visualisation and evaluation of flood uncertainties based on ensemble modelling. International Journal of Geographical Information Science, 30(2), 240-262
Open this publication in new window or tab >>Visualisation and evaluation of flood uncertainties based on ensemble modelling
2016 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 30, no 2, p. 240-262Article in journal (Refereed) Published
Abstract [en]

This study evaluates how users incorporate visualisation of flood uncertainty information in decision-making. An experiment was conducted where participants were given the task to decide building locations, taking into account homeowners’ preferences as well as dilemmas imposed by flood risks at the site. Two general types of visualisations for presenting uncertainties from ensemble modelling were evaluated: (1) uncertainty maps, which used aggregated ensemble results; and (2) performance bars showing all individual simulation outputs from the ensemble. Both were supplemented with either two-dimensional (2D) or three-dimensional (3D) contextual information, to give an overview of the area.The results showed that the type of uncertainty visualisation was highly influential on users’ decisions, whereas the representation of the contextual information (2D or 3D) was not. Visualisation with performance bars was more intuitive and effective for the task performed than the uncertainty map. It clearly affected users’ decisions in avoiding certain-to-be-flooded areas. Patterns to which the distances were decided from the homeowners’ preferred positions and the uncertainties were similar, when the 2D and 3D map models were used side by side with the uncertainty map. On the other hand, contextual information affected the time to solve the task. With the 3D map, it took the participants longer time to decide the locations, compared with the other combinations using the 2D model.Designing the visualisation so as to provide more detailed information made respondents avoid dangerous decisions. This has also led to less variation in their overall responses.

Place, publisher, year, edition, pages
Taylor & Francis, 2016
Keywords
visualisation, uncertainty, flood, ensemble modelling, decision-making
National Category
Computer and Information Sciences Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:hig:diva-20124 (URN)10.1080/13658816.2015.1085539 (DOI)000365550900006 ()2-s2.0-84948086666 (Scopus ID)
Note

Finasierat via European Union (EU) through Tillvaxtverket [GLOBES 2 project] projektnummer: 170430

Available from: 2015-08-19 Created: 2015-08-19 Last updated: 2018-12-03Bibliographically approved
Åhlén, J. & Seipel, S. (2015). Automatic water body extraction from remote sensing images using entropy. In: Proceedings of the International Multidisciplinary Scientific GeoConference SGEM: . Paper presented at 15th International Multidisciplinary Scientific GeoConference SGEM 2015, 18-24 June 2015, Albena, Bulgaria (pp. 517-524). , 4
Open this publication in new window or tab >>Automatic water body extraction from remote sensing images using entropy
2015 (English)In: Proceedings of the International Multidisciplinary Scientific GeoConference SGEM, 2015, Vol. 4, p. 517-524Conference paper, Published paper (Refereed)
Abstract [en]

This research focuses on automatic extraction of river banks and other inland waters from remote sensing images. There are no up to date accessible databases of rivers and most of other waters objects for modelling purposes. The main reason for that is that some regions are hard to access with the traditional ground through techniques and thus the boundary of river banks are uncertain in many geographical positions. The other reason is the limitations of widely applied method for extraction of water bodies called normalized-difference water index (NDWI). There is a novel approach to extract water bodies, which is based on pixel level variability or entropy, however, the methods work somewhat satisfactory on high spatial resolution images, there is no verification of the method performance on moderate or low resolution images. Problems encounter identification of mixed water pixels and e.g. roads, which are built in attachment to river banks and thus can be classified as rivers. In this work we propose an automatic extraction of river banks using image entropy, combined with NDWI identification. In this study only moderate spatial resolution Landsat TM are tested. Areas of interest include both major river banks and inland lakes. Calculating entropy on such poor spatial resolution images will lead to misinterpretation of water bodies, which all exhibits the same small variation of pixel values as e.g. some open or urban areas. Image entropy thus is calculated with the modification that involves the incorporation of local normalization index or variability coefficient. NDWI will produce an image where clear water exhibits large difference comparing to other land features. We are presenting an algorithm that uses an NDWI prior to entropy processing, so that bands used to calculate it, are chosen in clear connection to water body features that are clearly discernible.As a result we visualize a clear segmentation of the water bodies from the remote sensing images and verify the coordinates with a given geographic reference.

Series
International Multidisciplinary Scientific GeoConference SGEM, ISSN 1314-2704
Keywords
water, entropy, extraction
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hig:diva-20589 (URN)10.5593/SGEM2015/B21/S8.064 (DOI)000371599500064 ()2-s2.0-84946555831 (Scopus ID)978-619-7105-34-6 (ISBN)
Conference
15th International Multidisciplinary Scientific GeoConference SGEM 2015, 18-24 June 2015, Albena, Bulgaria
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-03-13Bibliographically approved
Liu, F. & Seipel, S. (2015). Infrared-visible image registration for augmented reality-based thermographic building diagnostics. Visualization in Engineering, 3(1), Article ID 16.
Open this publication in new window or tab >>Infrared-visible image registration for augmented reality-based thermographic building diagnostics
2015 (English)In: Visualization in Engineering, ISSN 2213-7459, Vol. 3, no 1, article id 16Article in journal (Refereed) Published
Abstract [en]

Background: In virtue of their capability to measure temperature, thermal infrared cameras have been widely used in building diagnostics for detecting heat loss, air leakage, water damage etc. However, the lack of visual details in thermal infrared images makes the complement of visible images a necessity. Therefore, it is often useful to register images of these two modalities for further inspection of architectures. Augmented reality (AR) technology, which supplements the real world with virtual objects, offers an ideal tool for presenting the combined results of thermal infrared and visible images. This paper addresses the problem of registering thermal infrared and visible façade images, which is essential towards developing an AR-based building diagnostics application. Methods: A novel quadrilateral feature is devised for this task, which models the shapes of commonly present façade elements, such as windows. The features result from grouping edge line segments with the help of image perspective information, namely, vanishing points. Our method adopts a forward selection algorithm to determine feature correspondences needed for estimating the transformation model. During the formation of the feature correspondence set, the correctness of selected feature correspondences at each step is verified by the quality of the resulting registration, which is based on the ratio of areas between the transformed features and the reference features. Results and conclusions: Quantitative evaluation of our method shows that registration errors are lower than errors reported in similar studies and registration performance is usable for most tasks in thermographic inspection of building façades.

Keywords
Multimodality image registration, Augmented reality, Thermal infrared imaging, Façade
National Category
Computer Vision and Robotics (Autonomous Systems) Human Computer Interaction Other Computer and Information Science
Identifiers
urn:nbn:se:hig:diva-20587 (URN)10.1186/s40327-015-0028-0 (DOI)2-s2.0-85044085447 (Scopus ID)
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-06-26Bibliographically approved
Liu, F. & Seipel, S. (2014). Detection of Façade Regions in Street View Images from Split-and-Merge of Perspective Patches. Journal of Image and Graphics, 2(1), 8-14
Open this publication in new window or tab >>Detection of Façade Regions in Street View Images from Split-and-Merge of Perspective Patches
2014 (English)In: Journal of Image and Graphics, ISSN ISSN 2301-3699, Vol. 2, no 1, p. 8-14Article in journal (Refereed) Published
Abstract [en]

Identification of building façades from digital images is one of the central problems in mobile augmented reality (MAR) applications in the built environment. Directly analyzing the whole image can increase the difficulty of façade identification due to the presence of image portions which are not façade. This paper presents an automatic approach to façade region detection given a single street view image as a pre-processing step to subsequent steps of façade identification. We devise a coarse façade region detection method based on the observation that façades are image regions with repetitive patterns containing a large amount of vertical and horizontal line segments. Firstly, scan lines are constructed from vanishing points and center points of image line segments. Hue profiles along these lines are then analyzed and used to decompose the image into rectilinear patches with similar repetitive patterns. Finally, patches are merged into larger coherent regions and the main building façade region is chosen based on the occurrence of horizontal and vertical line segments within each of the merged regions. A validation of our method showed that on average façade regions are detected in conformity with manually segmented images as ground truth.

Place, publisher, year, edition, pages
San Jose, CA, USA: Engineering and Technology Publishing, 2014
Keywords
façade region detection, street view image, vanishing point, mobile augmented reality
National Category
Computer Engineering
Identifiers
urn:nbn:se:hig:diva-18517 (URN)10.12720/joig.2.1.8-14 (DOI)
Available from: 2014-12-11 Created: 2014-12-11 Last updated: 2018-03-13Bibliographically approved
Åhlén, J., Seipel, S. & Liu, F. (2014). Evaluation of the Automatic methods for Building Extraction. International Journal Of Computers and Communications, 8, 171-176
Open this publication in new window or tab >>Evaluation of the Automatic methods for Building Extraction
2014 (English)In: International Journal Of Computers and Communications, ISSN 2074-1294, Vol. 8, p. 171-176Article in journal (Refereed) Published
Abstract [en]

Recognition of buildings is not a trivial task, yet highly demanded in many applications including augmented reality for mobile phones. Recognition rate can be increased significantly if building façade extraction will take place prior to the recognitionprocess. It is also a challenging task since eachbuilding can be viewed from different angles or under differentlighting conditions. Natural situation outdoor is when buildings are occluded by trees, street signs and other objects. This interferes for successful building façade recognition. In this paper we evaluate the knowledge based approach  to automatically segment out the whole buildingfaçade or major parts of thefaçade. This automatic building detection algorithm is then evaluated against other segmentation methods such as SIFT and vanishing point approach. This work contains two main steps: segmentation of building façades region using two different approaches and evaluation of the methods using database of reference features. Building recognition model (BRM) includes evaluation step that uses Chamfer metrics. BMR is then compared to vanishing points segmentation. In the evaluation mode, comparison of these two different segmentation methods is done using the data from ZuBuD.Reference matching is also done using Scale Invariant Feature Transform. Theresults show that the recognition rate is satisfactory for the BMR model and there is no need to extract the whole building façade for the successful recognition.

Keywords
Building, extraction, recognition, Chamfer metrics, SIFT, Vanishing Points
National Category
Information Systems Computer Sciences
Identifiers
urn:nbn:se:hig:diva-18200 (URN)
Available from: 2014-11-26 Created: 2014-11-26 Last updated: 2018-03-13Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-0085-5829

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