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Seipel, Stefan, ProfessorORCID iD iconorcid.org/0000-0003-0085-5829
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Publications (10 of 78) Show all publications
Ren, Z., Seipel, S. & Jiang, B. (2024). A topology-based approach to identifying urban centers in America using multi-source geospatial big data. Computers, Environment and Urban Systems, 107, Article ID 102045.
Open this publication in new window or tab >>A topology-based approach to identifying urban centers in America using multi-source geospatial big data
2024 (English)In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 107, article id 102045Article in journal (Refereed) Published
Abstract [en]

Urban structure can be better comprehended through analyzing its cores. Geospatial big data facilitate the identification of urban centers in terms of high accuracy and accessibility. However, previous studies seldom leverage multi-source geospatial big data to identify urban centers from a topological perspective. This study attempts to identify urban centers through the spatial integration of multi-source geospatial big data, including nighttime light imagery (NTL), building footprints (BFP) and street nodes of OpenStreetMap (OSM). We use a novel topological approach to construct complex networks from intra-urban hotspots based on the theory of centers by Christopher Alexander. We compute the degree of wholeness value for each hotspot as the centric index. The overlapped hotspots with the highest centric indices are regarded as urban centers. The identified urban centers in New York, Los Angeles, and Houston are consistent with their downtown areas, with overall accuracy of 90.23%. In Chicago, a new urban center is identified considering a larger spatial extent. The proposed approach can effectively and objectively prevent counting those hotspots with high intensity values but few neighbors into the result. This study proposes a topological approach for urban center identification and a bottom-up perspective for sustainable urban design.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Urban centers, Topological representation, Wholeness, Big data, Nighttime light imagery, Complexity
National Category
Geosciences, Multidisciplinary
Identifiers
urn:nbn:se:uu:diva-516916 (URN)10.1016/j.compenvurbsys.2023.102045 (DOI)001098125800001 ()
Funder
Swedish Research Council Formas, FR-2017/0009 (2017-00824)
Available from: 2023-12-04 Created: 2023-12-04 Last updated: 2023-12-04Bibliographically approved
Ma, L., Brandt, S. A., Seipel, S. & Ma, D. (2024). Simple agents - complex emergent path systems: Agent-based modelling of pedestrian movement. Environment and planning B: Urban analytics and city science, 51(2), 479-495
Open this publication in new window or tab >>Simple agents - complex emergent path systems: Agent-based modelling of pedestrian movement
2024 (English)In: Environment and planning B: Urban analytics and city science, ISSN 2399-8083, Vol. 51, no 2, p. 479-495Article in journal (Refereed) Published
Abstract [en]

In well-planned open and semi-open urban areas, it is common to observe desire paths on the ground, which shows how pedestrians themselves enhance the walkability and affordance of road systems. To better understand how these paths are formed, we present an agent-based modelling approach that simulates real pedestrian movement to generate complex path systems. By using heterogeneous ground affordance and visit frequency of hotspots as environmental settings and by modelling pedestrians as agents, path systems emerge from collective interactions between agents and their environment. Our model employs two visual parameters, angle and depth of vision, and two guiding principles, global conception and local adaptation. To examine the model’s visual parameters and their effects on the cost-efficiency of the emergent path systems, we conducted a randomly generated simulation and validated the model using desire paths observed in real scenarios. The results show that (1) the angle (found to be limited to a narrow range of 90–120°) has a more significant impact on path patterns than the depth of vision, which aligns with Space Syntax theories that also emphasize the importance of angle for modelling pedestrian movement; (2) the depth of vision is closely related to the scale-invariance of path patterns on different map scales; and (3) the angle has a negative exponential correlation with path efficiency and a positive correlation with path costs. Our proposed model can help urban planners predict or generate cost-efficient path installations in well- and poorly designed urban areas and may inspire further approaches rooted in generative science for future cities.

Place, publisher, year, edition, pages
Sage Publications, 2024
Keywords
agent-based modelling, pedestrian movement, desire paths, spatial cognition, Space Syntax
National Category
Computer Sciences Other Civil Engineering
Identifiers
urn:nbn:se:uu:diva-530036 (URN)10.1177/23998083231184884 (DOI)001011852000001 ()
Available from: 2024-06-04 Created: 2024-06-04 Last updated: 2024-06-04Bibliographically approved
Aslani, M. & Seipel, S. (2023). Rooftop segmentation and optimization of photovoltaic panel layouts in digital surface models. Computers, Environment and Urban Systems, 105, Article ID 102026.
Open this publication in new window or tab >>Rooftop segmentation and optimization of photovoltaic panel layouts in digital surface models
2023 (English)In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 105, article id 102026Article in journal (Refereed) Published
Abstract [en]

Rooftop photovoltaic panels (RPVs) are being increasingly used in urban areas as a promising means of achieving energy sustainability. Determining proper layouts of RPVs that make the best use of rooftop areas is of importance as they have a considerable impact on the RPVs performance in efficiently producing energy. In this study, a new spatial methodology for automatically determining the proper layouts of RPVs is proposed. It aims to both extract planar rooftop segments and identify feasible layouts with the highest number of RPVs in highly irradiated areas. It leverages digital surface models (DSMs) to consider roof shapes and occlusions in placing RPVs. The innovations of the work are twofold: (a) a new method for plane segmentation, and (b) a new method for optimally placing RPVs based on metaheuristic optimization, which best utilizes the limited rooftop areas. The proposed methodology is evaluated on two test sites that differ in urban morphology, building size, and spatial resolution. The results show that the plane segmentation method can accurately extract planar segments, achieving 88.7% and 99.5% precision in the test sites. In addition, the results indicate that complex rooftops are adequately handled for placing RPVs, and overestimation of solar energy potential is avoided if detailed analysis based on panel placement is employed.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Solar energy, Rooftop photovoltaic panels, Plane segmentation, Optimization, Digital surface models
National Category
Energy Engineering
Identifiers
urn:nbn:se:uu:diva-515308 (URN)10.1016/j.compenvurbsys.2023.102026 (DOI)001080247600001 ()
Funder
European Regional Development Fund (ERDF), 20201871
Available from: 2023-11-03 Created: 2023-11-03 Last updated: 2023-11-03Bibliographically approved
Ma, L., Seipel, S., Brandt, S. A. & Ma, D. (2022). A New Graph-Based Fractality Index to Characterize Complexity of Urban Form. ISPRS International Journal of Geo-Information, 11(5), Article ID 287.
Open this publication in new window or tab >>A New Graph-Based Fractality Index to Characterize Complexity of Urban Form
2022 (English)In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 11, no 5, article id 287Article in journal (Refereed) Published
Abstract [en]

Examining the complexity of urban form may help to understand human behavior in urban spaces, thereby improving the conditions for sustainable design of future cities. Metrics, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) index have been proposed to measure this complexity. However, as these indicators are statistical rather than spatial, they result in an inability to characterize the spatial complexity of urban forms, such as building footprints. To overcome this problem, this paper proposes a graph-based fractality index (GFI), which is based on a hybrid of fractal theory and deep learning techniques. First, to quantify the spatial complexity, several fractal variants were synthesized to train a deep graph convolutional neural network. Next, building footprints in London were used to test the method, where the results showed that the proposed framework performed better than the traditional indices, i.e., the index is capable of differentiating complex patterns. Another advantage is that it seems to assure that the trained deep learning is objective and not affected by potential biases in empirically selected training datasets Furthermore, the possibility to connect fractal theory and deep learning techniques on complexity issues opens up new possibilities for data-driven GIS science.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
complexity, fractals, building groups, graph convolutional neural networks, urban form
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-476653 (URN)10.3390/ijgi11050287 (DOI)000801418000001 ()
Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2022-06-10Bibliographically approved
Aslani, M. & Seipel, S. (2022). A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data. In: Cédric Grueau, Lemonia Ragia (Ed.), GISTAM: Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management. Paper presented at 8th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM), APR 27-29, 2022, ELECTR NETWORK (pp. 56-63). Setúbal: SciTePress
Open this publication in new window or tab >>A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data
2022 (English)In: GISTAM: Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management / [ed] Cédric Grueau, Lemonia Ragia, Setúbal: SciTePress, 2022, p. 56-63Conference paper, Published paper (Refereed)
Abstract [en]

Rooftop solar energy has long been regarded as a promising solution to cities' growing energy demand and environmental problems. A reliable estimate of rooftop solar energy facilitates the deployment of photovoltaics and helps formulate renewable-related policies. This reliable estimate underpins the necessity of accurately pinpointing the areas utilizable for mounting photovoltaics. The size, shape, and superstructures of rooftops as well as shadow effects are the important factors that have a considerable impact on utilizable areas. In this study, the utilizable areas and solar energy potential of rooftops are estimated by considering the mentioned factors using a three-step methodology. The first step involves training PointNet++, a deep network for object detection in point clouds, to recognize rooftops in LiDAR data. Second, planar segments of rooftops are extracted using clustering. Finally, areas that receive sufficient solar irradiation, have an appropriate size, and fulfill photovoltaic installation requirements are identified using morphological operations and predefined thresholds. The obtained results show high accuracy for rooftop extraction (93%) and plane segmentation (99%). Moreover. the spatially detailed analysis indicates that 17% of rooftop areas are usable for photovoltaics.

Place, publisher, year, edition, pages
Setúbal: SciTePress, 2022
Keywords
Deep Learning, Clustering, Segmentation, Solar Energy, LiDAR
National Category
Energy Systems
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-477552 (URN)10.5220/0011108300003185 (DOI)000803076800005 ()978-989-758-571-5 (ISBN)
Conference
8th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM), APR 27-29, 2022, ELECTR NETWORK
Funder
European Regional Development Fund (ERDF), 20201871
Available from: 2022-06-22 Created: 2022-06-22 Last updated: 2023-01-10Bibliographically approved
Chandel, K., Åhlén, J. & Seipel, S. (2022). Augmented Reality and Indoor Positioning in Context of Smart Industry: A Review. Management and Production Engineering Review, 13(4), 72-87
Open this publication in new window or tab >>Augmented Reality and Indoor Positioning in Context of Smart Industry: A Review
2022 (English)In: Management and Production Engineering Review, ISSN 2080-8208, E-ISSN 2082-1344, Vol. 13, no 4, p. 72-87Article in journal (Refereed) Published
Abstract [en]

Presently, digitalization is causing continuous transformation of industrial processes. However,it does pose challenges like spatially contextualizing data from industrial processes. Thereare various methods for calculating and delivering real-time location data. Indoor positioningsystems (IPS) are one such method, used to locate objects and people within buildings. Theyhave the potential to improve digital industrial processes, but they are currently underutilized.In addition, augmented reality (AR) is a critical technology in today’s digital industrialtransformation. This article aims to investigate the use of IPS and AR in manufacturing,the methodologies and technologies employed, the issues and limitations encountered, andidentify future research opportunities. This study concludes that, while there have been manystudies on IPS and navigation AR, there has been a dearth of research efforts in combiningthe two. Furthermore, because controlled environments may not expose users to the practicalissues they may face, more research in a real-world manufacturing environment is required toproduce more reliable and sustainable results.

Place, publisher, year, edition, pages
Polish Academy of Sciences Chancellery, 2022
National Category
Computer Sciences Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-492817 (URN)10.24425/mper.2022.142396 (DOI)000961972800007 ()
Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-06-12Bibliographically approved
Aslani, M. & Seipel, S. (2022). Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment. Applied Energy, 306, Article ID 118033.
Open this publication in new window or tab >>Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment
2022 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 306, article id 118033Article in journal (Refereed) Published
Abstract [en]

The considerable potential of rooftop photovoltaics (RPVs) for alleviating the high energy demand of cities has made them a proven technology in local energy networks. Identification of rooftop areas suitable for installing RPVs is of importance for energy planning. Having these suitable areas referred to as utilizable areas greatly assists in a reliable estimate of RPVs energy production. Within such a context, this research aims to propose a spatially detailed methodology that involves (a) automatic extraction of buildings footprint, (b) automatic segmentation of roof faces, and (c) automatic identification of utilizable areas of roof faces for solar infrastructure installation. Specifically, the innovations of this work are a new method for roof face segmentation and a new method for the identification of utilizable rooftop areas. The proposed methodology only requires digital surface models (DSMs) as input, and it is independent of other auxiliary spatial data to become more functional. A part of downtown Gothenburg composed of vegetation and high-rise buildings with complex shapes was selected to demonstrate the methodology performance. According to the experimental results, the proposed methodology has a high success rate in building extraction (about 95% correctness and completeness) and roof face segmentation (about 85% completeness and correctness). Additionally, the results suggest that the effects of roof occlusions and roof superstructures are satisfactorily considered in the identification of utilizable rooftop areas. Thus, the methodology is practically effective and relevant for the detailed RPVs assessments in arbitrary urban regions where only DSMs are accessible.

Place, publisher, year, edition, pages
ElsevierElsevier BV, 2022
Keywords
Solar energy, Rooftop photovoltaics, Utilizable rooftop areas, Building extraction, Roof face segmentation, Digital surface models
National Category
Energy Engineering
Identifiers
urn:nbn:se:uu:diva-458703 (URN)10.1016/j.apenergy.2021.118033 (DOI)000711977900008 ()
Funder
European Regional Development Fund (ERDF), 20201871Lantmäteriet
Available from: 2021-11-29 Created: 2021-11-29 Last updated: 2024-01-15Bibliographically approved
Seffers, G., Åhlén, J., Seipel, S. & Ooms, K. (2021). Assessing Damage – Can the Crowd Interpret Colour and 3D Information?. Cartographic Journal, 58, 69-82
Open this publication in new window or tab >>Assessing Damage – Can the Crowd Interpret Colour and 3D Information?
2021 (English)In: Cartographic Journal, ISSN 0008-7041, E-ISSN 1743-2774, Vol. 58, p. 69-82Article in journal (Refereed) Published
Abstract [en]

The goal of this study is to investigate how efficiently and effectively collapsed buildings – due to the occurrence of a disaster – can be localized by a general crowd. Two types of visualization parameters are evaluated in an online user study: (1) greyscale images (indicating height information) versus true colours; (2) variation in the vertical viewing angle (0°, 30° and 60°). Additionally, the influence of map use expertise on how the visualizations are interpreted, is investigated. The results indicate that the use of the greyscale image helps to locate collapsed buildings in an efficient and effective manner. The use of the viewing angle of 60° is the least appropriate. A person with a map use expertise will prefer the greyscale image over the colour image. To confirm the benefits of the use of three-dimensional visualizations and the use of the colour image, more research is needed.

Place, publisher, year, edition, pages
Informa UK Limited, 2021
National Category
Civil Engineering Other Computer and Information Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-425979 (URN)10.1080/00087041.2020.1714277 (DOI)000576489700001 ()
Available from: 2020-11-23 Created: 2020-11-23 Last updated: 2022-11-15Bibliographically approved
Aslani, M. & Seipel, S. (2021). Efficient and decision boundary aware instance selection for support vector machines. Information Sciences, 577, 579-598
Open this publication in new window or tab >>Efficient and decision boundary aware instance selection for support vector machines
2021 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 577, p. 579-598Article in journal (Refereed) Published
Abstract [en]

Support vector machines (SVMs) are powerful classifiers that have high computational complexity in the training phase, which can limit their applicability to large datasets. An effective approach to address this limitation is to select a small subset of the most representative training samples such that desirable results can be obtained. In this study, a novel instance selection method called border point extraction based on locality-sensitive hashing (BPLSH) is designed. BPLSH preserves instances that are near the decision boundaries and eliminates nonessential ones. The performance of BPLSH is benchmarked against four approaches on different classification problems. The experimental results indicate that BPLSH outperforms the other methods in terms of classification accuracy, preservation rate, and execution time. The source code of BPLSH can be found in https://github.com/mohaslani/BPLSH. 

Place, publisher, year, edition, pages
ElsevierElsevier, 2021
Keywords
Big data, Data reduction, Instance selection, Machine learning, Support vector machines, Classification (of information), Large dataset, % reductions, Border points extraction, Decision boundary, Effective approaches, Large datasets, Locality sensitive hashing, Machine-learning, Support vectors machine, Training phasis
National Category
Computer Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-456295 (URN)10.1016/j.ins.2021.07.015 (DOI)000709264000011 ()
Funder
European Regional Development Fund (ERDF), 20201871
Available from: 2021-10-18 Created: 2021-10-18 Last updated: 2024-01-15Bibliographically approved
Milutinovic, G., Seipel, S. & Ahonen-Jonnarth, U. (2021). Geospatial Decision-Making Framework Based on the Concept of Satisficing. ISPRS International Journal of Geo-Information, 10(5), Article ID 326.
Open this publication in new window or tab >>Geospatial Decision-Making Framework Based on the Concept of Satisficing
2021 (English)In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 10, no 5, article id 326Article in journal (Refereed) Published
Abstract [en]

Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient use thereof. In this paper, we present a novel decision-making framework that emanates from the need for intuitive and easy-to-use decision support systems for geospatial multi-criteria decision making. The framework consists of two parts: the decision-making model Even Swaps on Reduced Data Sets (ESRDS), and the interactive visualization framework. The decision-making model is based on the concept of satisficing, and as such, it is intuitive and easy to understand and apply. It integrates even swaps, a prescriptive decision-making method, with the findings of behavioural decision-making theories. Providing visual feedback and interaction opportunities throughout the decision-making process, the interactive visualization part of the framework helps the decision maker gain better insight into the decision space and attribute dependencies. Furthermore, it provides the means to analyse and compare the outcomes of different scenarios and decision paths.

Place, publisher, year, edition, pages
MDPIMDPI, 2021
Keywords
decision making, GIS, interactive visualization, bounded rationality, satisficing, even swaps
National Category
Information Systems
Identifiers
urn:nbn:se:uu:diva-446843 (URN)10.3390/ijgi10050326 (DOI)000653985900001 ()
Available from: 2021-08-06 Created: 2021-08-06 Last updated: 2024-01-15Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-0085-5829

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