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Documents authored by Majic, Ivan

Document
DOI: 10.4230/LIPIcs.COSIT.2024.9
Probing the Information Theoretical Roots of Spatial Dependence Measures

Authors: Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai, and Ivan Majic

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

Abstract
Intuitively, there is a relation between measures of spatial dependence and information theoretical measures of entropy. For instance, we can provide an intuition of why spatial data is special by stating that, on average, spatial data samples contain less than expected information. Similarly, spatial data, e.g., remotely sensed imagery, that is easy to compress is also likely to show significant spatial autocorrelation. Formulating our (highly specific) core concepts of spatial information theory in the widely used language of information theory opens new perspectives on their differences and similarities and also fosters cross-disciplinary collaboration, e.g., with the broader AI/ML communities. Interestingly, however, this intuitive relation is challenging to formalize and generalize, leading prior work to rely mostly on experimental results, e.g., for describing landscape patterns. In this work, we will explore the information theoretical roots of spatial autocorrelation, more specifically Moran’s I, through the lens of self-information (also known as surprisal) and provide both formal proofs and experiments.
Cite as

Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai, and Ivan Majic. Probing the Information Theoretical Roots of Spatial Dependence Measures. In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 9:1-9:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{wang_et_al:LIPIcs.COSIT.2024.9,
  author =	{Wang, Zhangyu and Janowicz, Krzysztof and Mai, Gengchen and Majic, Ivan},
  title =	{{Probing the Information Theoretical Roots of Spatial Dependence Measures}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{9:1--9:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.9},
  URN =		{urn:nbn:de:0030-drops-208247},
  doi =		{10.4230/LIPIcs.COSIT.2024.9},
  annote =	{Keywords: Spatial Autocorrelation, Moran’s I, Information Theory, Surprisal, Self-Information}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.63
Calculating Shadows with U-Nets for Urban Environments (Short Paper)

Authors: Dominik Rothschedl, Franz Welscher, Franziska Hübl, Ivan Majic, Daniele Giannandrea, Matthias Wastian, Johannes Scholz, and Niki Popper

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Shadow calculation is an important prerequisite for many urban and environmental analyses such as the assessment of solar energy potential. We propose a neural net approach that can be trained with 3D geographical information and predict the presence and depth of shadows. We adapt a U-Net algorithm traditionally used in biomedical image segmentation and train it on sections of Styria, Austria. Our two-step approach first predicts binary existence of shadows and then estimates the depth of shadows as well. Our results on the case study of Styria, Austria show that the proposed approach can predict in both models shadows with over 80% accuracy which is satisfactory for real-world applications, but still leaves room for improvement.
Cite as

Dominik Rothschedl, Franz Welscher, Franziska Hübl, Ivan Majic, Daniele Giannandrea, Matthias Wastian, Johannes Scholz, and Niki Popper. Calculating Shadows with U-Nets for Urban Environments (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 63:1-63:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{rothschedl_et_al:LIPIcs.GIScience.2023.63,
  author =	{Rothschedl, Dominik and Welscher, Franz and H\"{u}bl, Franziska and Majic, Ivan and Giannandrea, Daniele and Wastian, Matthias and Scholz, Johannes and Popper, Niki},
  title =	{{Calculating Shadows with U-Nets for Urban Environments}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{63:1--63:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.63},
  URN =		{urn:nbn:de:0030-drops-189581},
  doi =		{10.4230/LIPIcs.GIScience.2023.63},
  annote =	{Keywords: Neural Net, U-Net, Residual Net, Shadow Calculation}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.82
Harnessing the Sunlight on Facades - an Approach for Determining Vertical Photovoltaic Potential (Short Paper)

Authors: Franz Welscher, Ivan Majic, Franziska Hübl, Rizwan Bulbul, and Johannes Scholz

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
The paper deals with the calculation of the photovoltaic potential of vertical structures. Photovoltaic systems are a core technology for producing renewable energy. As roughly 50% of the population on planet Earth lives in urban environments, the production of renewable energy in urban contexts is of particular interest. As several papers have elaborated on the photovoltaic potential of roofs, this paper focuses on vertical structures. Hence, we present a methodology to extract facades suitable for photovoltaic installation, calculate their southness and percentage of shaded areas. The approach is successfully tested, based on a dataset located in the city of Graz, Styria (Austria). The results show the wall structures of each building, the respective shadow depth, and their score based on a multi-criteria analysis that represents the suitability for the installation of a photovoltaic system.
Cite as

Franz Welscher, Ivan Majic, Franziska Hübl, Rizwan Bulbul, and Johannes Scholz. Harnessing the Sunlight on Facades - an Approach for Determining Vertical Photovoltaic Potential (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 82:1-82:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{welscher_et_al:LIPIcs.GIScience.2023.82,
  author =	{Welscher, Franz and Majic, Ivan and H\"{u}bl, Franziska and Bulbul, Rizwan and Scholz, Johannes},
  title =	{{Harnessing the Sunlight on Facades - an Approach for Determining Vertical Photovoltaic Potential}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{82:1--82:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.82},
  URN =		{urn:nbn:de:0030-drops-189777},
  doi =		{10.4230/LIPIcs.GIScience.2023.82},
  annote =	{Keywords: Vertical Photovoltaics, Facades, Southness, Multi-Criteria-Analysis, Shadow}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.9
Perceptions of Qualitative Spatial Arrangements of Three Objects

Authors: Ningran Xu, Ivan Majic, and Martin Tomko

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)

Abstract
Cognitive grounding of formal models of qualitative spatial relations is important to bridge between spatial data and human perceptions of spatial arrangements. Here, we report on an experimental verification of the cognitive alignment of the recently proposed Ray Intersection Model (RIM) capturing qualitative relationships between three spatial objects, and human perceptions of spatial arrangements through a grouping task. Further, we explore arrangements with an object positioned "between" two other objects. We show that RIM has sufficient expressive power and aligns well with human perceptions of ternary spatial relationships.
Cite as

Ningran Xu, Ivan Majic, and Martin Tomko. Perceptions of Qualitative Spatial Arrangements of Three Objects. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{xu_et_al:LIPIcs.COSIT.2022.9,
  author =	{Xu, Ningran and Majic, Ivan and Tomko, Martin},
  title =	{{Perceptions of Qualitative Spatial Arrangements of Three Objects}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{9:1--9:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.9},
  URN =		{urn:nbn:de:0030-drops-168948},
  doi =		{10.4230/LIPIcs.COSIT.2022.9},
  annote =	{Keywords: Spatial Perception, Qualitative Spatial Relationships, Betweenness, Evaluation, Ternary Relationships}
}
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