6 Search Results for "Zipf, Alexander"

Document
DOI: 10.4230/LIPIcs.ESA.2024.90
Many-To-Many Polygon Matching à La Jaccard

Authors: Alexander Naumann, Annika Bonerath, and Jan-Henrik Haunert

Published in: LIPIcs, Volume 308, 32nd Annual European Symposium on Algorithms (ESA 2024)

Abstract
Integration of spatial data is a major field of research. An important task of data integration is finding correspondences between entities. Here, we focus on combining building footprint data from cadastre and from volunteered geographic information, in particular OpenStreetMap. Previous research on this topic has led to exact 1:1 matching approaches and heuristic m:n matching approaches, most of which are lacking a mathematical problem definition. We introduce a model for many-to-many polygon matching based on the well-established Jaccard index. This is a natural extension to the existing 1:1 matching approaches. We show that the problem is NP-complete and a naive approach via integer programming fails easily. By analyzing the structure of the problem in detail, we can reduce the number of variables significantly. This approach yields an optimal m:n matching even for large real-world instances with appropriate running time. In particular, for the set of all building footprints of the city of Bonn (119,300 / 97,284 polygons) it yielded an optimal solution in approximately 1 hour.
Cite as

Alexander Naumann, Annika Bonerath, and Jan-Henrik Haunert. Many-To-Many Polygon Matching à La Jaccard. In 32nd Annual European Symposium on Algorithms (ESA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 308, pp. 90:1-90:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{naumann_et_al:LIPIcs.ESA.2024.90,
  author =	{Naumann, Alexander and Bonerath, Annika and Haunert, Jan-Henrik},
  title =	{{Many-To-Many Polygon Matching \`{a} La Jaccard}},
  booktitle =	{32nd Annual European Symposium on Algorithms (ESA 2024)},
  pages =	{90:1--90:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-338-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{308},
  editor =	{Chan, Timothy and Fischer, Johannes and Iacono, John and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2024.90},
  URN =		{urn:nbn:de:0030-drops-211614},
  doi =		{10.4230/LIPIcs.ESA.2024.90},
  annote =	{Keywords: polygon matching, exact algorithm, Jaccard index}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2024.16
Four Arguments Why Places and Information About Places Are Inextricably Interwoven (Short Paper)

Authors: Franz-Benjamin Mocnik

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

Abstract
Research on information about places can often practically not be clearly demarcated from research on the places themselves. This is not a problem itself but raises the question of how geographical information science and human geography mutually relate. This paper discusses four arguments as to why places and information about them are inextricably interwoven in many cases. The difficulty in finding a demarcation between the two lines of research is thus not due to a lack of academic engagement with these topics but rather due to the subject matter itself. Consequently, research on the role of information in the context of places is indispensable for the study of places themselves. This raises the question again as to whether the separation of geographical information science and geography, as they are currently lived by distinctly different communities of practice, is justified.
Cite as

Franz-Benjamin Mocnik. Four Arguments Why Places and Information About Places Are Inextricably Interwoven (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 16:1-16:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{mocnik:LIPIcs.COSIT.2024.16,
  author =	{Mocnik, Franz-Benjamin},
  title =	{{Four Arguments Why Places and Information About Places Are Inextricably Interwoven}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{16:1--16:8},
  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.2024.16},
  URN =		{urn:nbn:de:0030-drops-208317},
  doi =		{10.4230/LIPIcs.COSIT.2024.16},
  annote =	{Keywords: place, place-making, representation, information, communication}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2024.31
Large Language Models: Testing Their Capabilities to Understand and Explain Spatial Concepts (Short Paper)

Authors: Majid Hojati and Rob Feick

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

Abstract
Interest in applying Large Language Models (LLMs), which use natural language processing (NLP) to provide human-like responses to text-based questions, to geospatial tasks has grown rapidly. Research shows that LLMs can help generate software code and answer some types of geographic questions to varying degrees even without fine-tuning. However, further research is required to explore the types of spatial questions they answer correctly, their abilities to apply spatial reasoning, and the variability between models. In this paper we examine the ability of four LLM models (GPT3.5 and 4, LLAma2.0, Falcon40B) to answer spatial questions that range from basic calculations to more advanced geographic concepts. The intent of this comparison is twofold. First, we demonstrate an extensible method for evaluating LLM’s limitations to supporting spatial data science through correct calculations and code generation. Relatedly, we also consider how these models can aid geospatial learning by providing text-based explanations of spatial concepts and operations. Our research shows common strengths in more basic types of questions, and mixed results for questions relating to more advanced spatial concepts. These results provide insights that may be used to inform strategies for testing and fine-tuning these models to increase their understanding of key spatial concepts.
Cite as

Majid Hojati and Rob Feick. Large Language Models: Testing Their Capabilities to Understand and Explain Spatial Concepts (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 31:1-31:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{hojati_et_al:LIPIcs.COSIT.2024.31,
  author =	{Hojati, Majid and Feick, Rob},
  title =	{{Large Language Models: Testing Their Capabilities to Understand and Explain Spatial Concepts}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{31:1--31:9},
  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.2024.31},
  URN =		{urn:nbn:de:0030-drops-208460},
  doi =		{10.4230/LIPIcs.COSIT.2024.31},
  annote =	{Keywords: Geospatial concepts, Large Language Models, LLM, GPT, Llama, Falcon}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.7
Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Authors: Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan, Alexander Zipf, and Martin Werner

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

Abstract
Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability.
Cite as

Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan, Alexander Zipf, and Martin Werner. Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{li_et_al:LIPIcs.GIScience.2023.7,
  author =	{Li, Hao and Yuan, Zhendong and Dax, Gabriel and Kong, Gefei and Fan, Hongchao and Zipf, Alexander and Werner, Martin},
  title =	{{Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{7:1--7:15},
  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.2023.7},
  URN =		{urn:nbn:de:0030-drops-189028},
  doi =		{10.4230/LIPIcs.GIScience.2023.7},
  annote =	{Keywords: OpenStreetMap, Street-view Images, VGI, GeoAI, 3D city model, Facade parsing}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2021.II.3
Comparison of Simulated Fast and Green Routes for Cyclists and Pedestrians

Authors: Christina Ludwig, Sven Lautenbach, Eva-Marie Schömann, and Alexander Zipf

Published in: LIPIcs, Volume 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II

Abstract
Routes with a high share of greenery are attractive for cyclist and pedestrians. We analyze how strongly such green routes differ from the respective fast routes using the openrouteservice. Greenness of streets was estimated based on OpenStreetMap data in combination with Sentinel-II imagery, 3d laser scan data and administrative information on trees on public ground. We assess the effect both at the level of the individual route and at the urban level for two German cities: Dresden and Heidelberg. For individual routes, we study how strongly green routes differ from the respective fast routes. In addition, we identify parts of the road network which represent important green corridors as well as unattractive parts which can or cannot be avoided at the cost of reasonable detours. In both cities, our results show the importance of urban green spaces for the provision of attractive green routes and provide new insights for urban planning by identifying unvegetated bottlenecks in the street network for which no green alternatives exist at this point.
Cite as

Christina Ludwig, Sven Lautenbach, Eva-Marie Schömann, and Alexander Zipf. Comparison of Simulated Fast and Green Routes for Cyclists and Pedestrians. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 3:1-3:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{ludwig_et_al:LIPIcs.GIScience.2021.II.3,
  author =	{Ludwig, Christina and Lautenbach, Sven and Sch\"{o}mann, Eva-Marie and Zipf, Alexander},
  title =	{{Comparison of Simulated Fast and Green Routes for Cyclists and Pedestrians}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{3:1--3:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.II.3},
  URN =		{urn:nbn:de:0030-drops-147622},
  doi =		{10.4230/LIPIcs.GIScience.2021.II.3},
  annote =	{Keywords: Routing, OpenStreetMap, route choice, urban vegetation, sustainable mobility}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.63
Towards the Statistical Analysis and Visualization of Places (Short Paper)

Authors: René Westerholt, Mathias Gröbe, Alexander Zipf, and Dirk Burghardt

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
The concept of place recently gains momentum in GIScience. In some fields like human geography, spatial cognition or information theory, this topic already has a longer scholarly tradition. This is however not yet completely the case with statistical spatial analysis and cartography. Despite that, taking full advantage of the plethora of user-generated information that we have available these days requires mature place-based statistical and visualization concepts. This paper contributes to these developments: We integrate existing place definitions into an understanding of places as a system of interlinked, constituent characteristics. Based on this, challenges and first promising conceptual ideas are discussed from statistical and visualization viewpoints.
Cite as

René Westerholt, Mathias Gröbe, Alexander Zipf, and Dirk Burghardt. Towards the Statistical Analysis and Visualization of Places (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 63:1-63:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{westerholt_et_al:LIPIcs.GISCIENCE.2018.63,
  author =	{Westerholt, Ren\'{e} and Gr\"{o}be, Mathias and Zipf, Alexander and Burghardt, Dirk},
  title =	{{Towards the Statistical Analysis and Visualization of Places}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{63:1--63:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.63},
  URN =		{urn:nbn:de:0030-drops-93914},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.63},
  annote =	{Keywords: Platial Analysis, Visualization, Statistics, Geosocial Media}
}
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