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Seipel, Stefan, ProfessorORCID iD iconorcid.org/0000-0003-0085-5829
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Publications (10 of 104) 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
Big data; Complexity; Nighttime light imagery; Topological representation; Urban centers; Wholeness
National Category
Computer Sciences
Identifiers
urn:nbn:se:hig:diva-43193 (URN)10.1016/j.compenvurbsys.2023.102045 (DOI)001098125800001 ()2-s2.0-85174445872 (Scopus ID)
Funder
Swedish Research Council Formas, 2017-00824Swedish Research Council Formas, FR-2017/0009
Available from: 2023-10-30 Created: 2023-10-30 Last updated: 2024-01-08Bibliographically approved
Humble, N., Boustedt, J., Holmgren, H., Milutinovic, G., Seipel, S. & Östberg, A.-S. (2023). Cheaters or AI-Enhanced Learners: Consequences of ChatGPT for Programming Education. Electronic Journal of e-Learning
Open this publication in new window or tab >>Cheaters or AI-Enhanced Learners: Consequences of ChatGPT for Programming Education
Show others...
2023 (English)In: Electronic Journal of e-Learning, E-ISSN 1479-4403Article in journal (Refereed) Epub ahead of print
Abstract [en]

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT’s ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be “lying” in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast.

Place, publisher, year, edition, pages
Academic Publishing International Limited, 2023
Keywords
Artificial Intelligence in education, ChatGPT, Programming education, Computer Science Education, AI-enhanced learning
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hig:diva-43482 (URN)10.34190/ejel.21.5.3154 (DOI)
Available from: 2023-12-19 Created: 2023-12-19 Last updated: 2023-12-20Bibliographically approved
Chandel, K., Åhlén, J. & Seipel, S. (2023). Evaluating the Tracking Abilities of Microsoft HoloLens-1 for Small-Scale Industrial Processes. In: : . Paper presented at ICMAT 2023: International Conference on Computerized Manufacturing Automation Technologies, Stockholm, July 6-7, 2023.
Open this publication in new window or tab >>Evaluating the Tracking Abilities of Microsoft HoloLens-1 for Small-Scale Industrial Processes
2023 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

This study evaluates the accuracy of Microsoft HoloLens (Version 1) for small-scale industrial activities, comparingits measurements to ground truth data from a Kuka Robotics arm. Two experiments were conducted to assess its positiontracking capabilities, revealing that the HoloLens device is effective for measuring the position of dynamic objects with smalldimensions. However, its precision is affected by the velocity of the trajectory and its position within the device's field of view.While the HoloLens device may be suitable for small-scale tasks, its limitations for more complex and demanding applicationsrequiring high precision and accuracy must be considered. The findings can guide the use of HoloLens devices in industrialapplications and contribute to the development of more effective and reliable position-tracking systems.

Keywords
Augmented reality (AR), Microsoft HoloLens, object tracking, industrial processes, manufacturing processes
National Category
Geosciences, Multidisciplinary
Identifiers
urn:nbn:se:hig:diva-42841 (URN)
Conference
ICMAT 2023: International Conference on Computerized Manufacturing Automation Technologies, Stockholm, July 6-7, 2023
Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2023-08-16Bibliographically 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, 2023
Keywords
Solar energy, Rooftop photovoltaic panels, Plane segmentation, Optimization, Digital surface models
National Category
Energy Engineering
Identifiers
urn:nbn:se:hig:diva-42962 (URN)10.1016/j.compenvurbsys.2023.102026 (DOI)001080247600001 ()2-s2.0-85169504338 (Scopus ID)
Funder
European Regional Development Fund (ERDF), 20201871
Available from: 2023-09-01 Created: 2023-09-01 Last updated: 2023-10-27Bibliographically approved
Ma, L., Brandt, S. A., Seipel, S. & Ma, D. (2023). Simple agents – complex emergent path systems: Agent-based modelling of pedestrian movement. Environment and Planning B: Urban Analytics and City Science
Open this publication in new window or tab >>Simple agents – complex emergent path systems: Agent-based modelling of pedestrian movement
2023 (English)In: Environment and Planning B: Urban Analytics and City Science, ISSN 2399-8083Article in journal (Refereed) Epub ahead of print
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 Syntaxtheories 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, 2023
National Category
Geosciences, Multidisciplinary
Identifiers
urn:nbn:se:hig:diva-42413 (URN)10.1177/23998083231184884 (DOI)001011852000001 ()2-s2.0-85162881096 (Scopus ID)
Available from: 2023-06-22 Created: 2023-06-22 Last updated: 2023-07-10Bibliographically approved
Aslani, M. & Seipel, S. (2023). Solar Energy Assessment: From Rooftop Extraction to Identifying Utilizable Areas. In: Grueau, C., Laurini, R., Ragia, L. (Ed.), Geographical Information Systems Theory, Applications and Management, 7th International Conference, GISTAM 2021, Virtual Event, April 23–25, 2021, and 8th International Conference, GISTAM 2022, Virtual Event, April 27-29, 2022, Revised Selected Papers: . Paper presented at GISTAM 2021 & 2022 (pp. 102-115). Springer
Open this publication in new window or tab >>Solar Energy Assessment: From Rooftop Extraction to Identifying Utilizable Areas
2023 (English)In: Geographical Information Systems Theory, Applications and Management, 7th International Conference, GISTAM 2021, Virtual Event, April 23–25, 2021, and 8th International Conference, GISTAM 2022, Virtual Event, April 27-29, 2022, Revised Selected Papers / [ed] Grueau, C., Laurini, R., Ragia, L., Springer , 2023, p. 102-115Conference paper, Published paper (Refereed)
Abstract [en]

Rooftop photovoltaics have been acknowledged as a critical component in cities’ efforts to reduce their reliance on fossil fuels and move towards energy sustainability. Identifying rooftop areas suitable for installing rooftop photovoltaics-referred to as utilizable areas-is essential for effective energy planning and developing policies related to renewable energies. Utilizable areas are greatly affected by the size, shape, superstructures of rooftops, and shadow effects. This study estimates utilizable areas and solar energy potential of rooftops by considering the mentioned factors. First, rooftops are extracted from LiDAR data by training PointNet++, a neural network architecture for processing 3D point clouds. The second step involves extracting planar segments of rooftops using a combination of clustering and region growing. Finally, utilizable areas of planar segments are identified by removing areas that do not have a suitable size and do not receive sufficient solar irradiation. Additionally, in this step, areas reserved for accessibility to photovoltaics are removed. According to the experimental results, the methods have a high success rate in rooftop extraction, plane segmentation, and, consequently, estimating utilizable areas for photovoltaics.

Place, publisher, year, edition, pages
Springer, 2023
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1908
Keywords
Rooftop solar energy, Spatial analyses, Plane segmentation, Rooftop extraction, Deep learning
National Category
Energy Engineering
Identifiers
urn:nbn:se:hig:diva-43117 (URN)10.1007/978-3-031-44112-7_7 (DOI)2-s2.0-85174552233 (Scopus ID)978-3-031-44111-0 (ISBN)978-3-031-44112-7 (ISBN)
Conference
GISTAM 2021 & 2022
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2023-10-30Bibliographically approved
Humble, N., Boustedt, J., Holmgren, H., Milutinovic, G., Seipel, S. & Östberg, A.-S. (2023). The consequences of ChatGPT for programming education: Cheating or AI-enhanced learning?. In: Symposium on AI Opportunities and Challenges: Education will never be the same again. Paper presented at Symposium on AI Opportunities and Challenges (SAIOC), 5 December 2023 (online) (pp. 15-16). ACI Academic Conferences International, 1
Open this publication in new window or tab >>The consequences of ChatGPT for programming education: Cheating or AI-enhanced learning?
Show others...
2023 (English)In: Symposium on AI Opportunities and Challenges: Education will never be the same again, ACI Academic Conferences International, 2023, Vol. 1, p. 15-16Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where assessments have long been challenging due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT’s ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, a study was conducted where potential opportunities and threats of ChatGPT for programming education were investigated. The question to answer was: What will the consequences be for programming education? 

The study applied a methodological approach inspired by action research and analytic autoethnography to investigate, experiment and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. 

Findings of the study include several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be lying in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT will have a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast. 

Place, publisher, year, edition, pages
ACI Academic Conferences International, 2023
Keywords
Artificial Intelligence in Education, ChatGPT, Programming Education, Computer Science Education, AI-enhanced learning
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hig:diva-43425 (URN)
Conference
Symposium on AI Opportunities and Challenges (SAIOC), 5 December 2023 (online)
Available from: 2023-12-11 Created: 2023-12-11 Last updated: 2023-12-11Bibliographically 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
Environmental Sciences Geosciences, Multidisciplinary Cultural Studies
Identifiers
urn:nbn:se:hig:diva-38476 (URN)10.3390/ijgi11050287 (DOI)000801418000001 ()2-s2.0-85129726341 (Scopus ID)
Available from: 2022-04-29 Created: 2022-04-29 Last updated: 2022-12-05Bibliographically approved
Aslani, M. & Seipel, S. (2022). A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data. In: Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM: . Paper presented at Geographical Information Systems Theory, Applications and Management - GISTAM (pp. 56-63). 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: Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM, 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
ScitePress, 2022
Keywords
Deep Learning, Clustering, Segmentation, Solar Energy, LiDAR
National Category
Computer and Information Sciences Other Earth and Related Environmental Sciences
Research subject
Sustainable Urban Development
Identifiers
urn:nbn:se:hig:diva-38510 (URN)10.5220/0011108300003185 (DOI)000803076800005 ()2-s2.0-85141048924 (Scopus ID)978-989-758-571-5 (ISBN)
Conference
Geographical Information Systems Theory, Applications and Management - GISTAM
Funder
European Regional Development Fund (ERDF), 20201871
Available from: 2022-05-18 Created: 2022-05-18 Last updated: 2022-12-05Bibliographically 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, review/survey (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. There are various methods for calculating and delivering real-time location data. Indoor positioning systems (IPS) are one such method, used to locate objects and people within buildings. They have the potential to improve digital industrial processes, but they are currently under utilized. In addition, augmented reality (AR) is a critical technology in today’s digital industrial transformation. This article aims to investigate the use of IPS and AR in manufacturing, the methodologies and technologies employed, the issues and limitations encountered, and identify future research opportunities. This study concludes that, while there have been many studies on IPS and navigation AR, there has been a dearth of research efforts in combining the two. Furthermore, because controlled environments may not expose users to the practical issues they may face, more research in a real-world manufacturing environment is required to produce more reliable and sustainable results

Place, publisher, year, edition, pages
Polish Academy of Sciences, 2022
Keywords
Industrial Augmented Reality, Indoor Positioning Systems, Smart Manufacturing, Smart Factory.
National Category
Computer and Information Sciences
Research subject
Intelligent Industry
Identifiers
urn:nbn:se:hig:diva-40651 (URN)10.24425/mper.2022.142396 (DOI)000961972800007 ()2-s2.0-85168687249 (Scopus ID)
Projects
Spatial Data Innovation (SDI)
Funder
Region Gavleborg, 20201871Swedish Agency for Economic and Regional Growth
Available from: 2023-01-02 Created: 2023-01-02 Last updated: 2023-09-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0085-5829

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