3 Search Results for "Zhao, Bo"

Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.52
Impacts of Catchments Derived from Fine-Grained Mobility Data on Spatial Accessibility (Short Paper)

Authors: Alexander Michels, Jinwoo Park, Bo Li, Jeon-Young Kang, and Shaowen Wang

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

Abstract
Spatial accessibility is a powerful tool for understanding how access to important services and resources varies across space. While spatial accessibility methods traditionally rely on origin-destination matrices between centroids of administrative zones, recent work has examined creating polygonal catchments - areas within a travel-time threshold - from point-based fine-grained mobility data. In this paper, we investigate the difference between the convex hull and alpha shape algorithms for determining catchment areas and how this affects the results of spatial accessibility analyses. Our analysis shows that the choice of how we define a catchment produces differences in the measured accessibility which correlate with social vulnerability. These findings highlight the importance of evaluating and communicating minor methodological choices in spatial accessibility analyses.
Cite as

Alexander Michels, Jinwoo Park, Bo Li, Jeon-Young Kang, and Shaowen Wang. Impacts of Catchments Derived from Fine-Grained Mobility Data on Spatial Accessibility (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 52:1-52:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{michels_et_al:LIPIcs.GIScience.2023.52,
  author =	{Michels, Alexander and Park, Jinwoo and Li, Bo and Kang, Jeon-Young and Wang, Shaowen},
  title =	{{Impacts of Catchments Derived from Fine-Grained Mobility Data on Spatial Accessibility}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{52:1--52:6},
  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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.52},
  URN =		{urn:nbn:de:0030-drops-189470},
  doi =		{10.4230/LIPIcs.GIScience.2023.52},
  annote =	{Keywords: Spatial accessibility, alpha shape, convex hull, cyberGIS, social vulnerability}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.93
The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography (Short Paper)

Authors: Qianheng Zhang, Yuhao Kang, and Robert Roth

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

Abstract
The rapid advancement of artificial intelligence (AI) such as the emergence of large language models ChatGPT and DALL·E 2 has brought both opportunities for improving productivity and raised ethical concerns. This paper investigates the ethics of using artificial intelligence (AI) in cartography, with a particular focus on the generation of maps using DALL·E 2. To accomplish this, we first created an open-sourced dataset that includes synthetic (AI-generated) and real-world (human-designed) maps at multiple scales with a variety of settings. We subsequently examined four potential ethical concerns that may arise from the characteristics of DALL·E 2 generated maps, namely inaccuracies, misleading information, unanticipated features, and irreproducibility. We then developed a deep learning-based model to identify those AI-generated maps. Our research emphasizes the importance of ethical considerations in the development and use of AI techniques in cartography, contributing to the growing body of work on trustworthy maps. We aim to raise public awareness of the potential risks associated with AI-generated maps and support the development of ethical guidelines for their future use.
Cite as

Qianheng Zhang, Yuhao Kang, and Robert Roth. The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 93:1-93:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{zhang_et_al:LIPIcs.GIScience.2023.93,
  author =	{Zhang, Qianheng and Kang, Yuhao and Roth, Robert},
  title =	{{The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{93:1--93:6},
  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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.93},
  URN =		{urn:nbn:de:0030-drops-189886},
  doi =		{10.4230/LIPIcs.GIScience.2023.93},
  annote =	{Keywords: Ethics, GeoAI, DALL-E, Cartography}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.67
Satellite Image Spoofing: Creating Remote Sensing Dataset with Generative Adversarial Networks (Short Paper)

Authors: Chunxue Xu and Bo Zhao

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

Abstract
The rise of Artificial Intelligence (AI) has brought up both opportunities and challenges for today's evolving GIScience. Its ability in image classification, object detection and feature extraction has been frequently praised. However, it may also apply for falsifying geospatial data. To demonstrate the thrilling power of AI, this research explored the potentials of deep learning algorithms in capturing geographic features and creating fake satellite images according to the learned 'sense'. Specifically, Generative Adversarial Networks (GANs) is used to capture geographic features of a certain place from a group of web maps and satellite images, and transfer the features to another place. Corvallis is selected as the study area, and fake datasets with 'learned' style from three big cities (i.e. New York City, Seattle and Beijing) are generated through CycleGAN. The empirical results show that GANs can 'remember' a certain 'sense of place' and further apply that 'sense' to another place. With this paper, we would like to raise both public and GIScientists' awareness in the potential occurrence of fake satellite images, and its impacts on various geospatial applications, such as environmental monitoring, urban planning, and land use development.
Cite as

Chunxue Xu and Bo Zhao. Satellite Image Spoofing: Creating Remote Sensing Dataset with Generative Adversarial Networks (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 67:1-67:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

Copy BibTex To Clipboard

@InProceedings{xu_et_al:LIPIcs.GISCIENCE.2018.67,
  author =	{Xu, Chunxue and Zhao, Bo},
  title =	{{Satellite Image Spoofing: Creating Remote Sensing Dataset with Generative Adversarial Networks}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{67:1--67:6},
  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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.67},
  URN =		{urn:nbn:de:0030-drops-93952},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.67},
  annote =	{Keywords: Deep Learning and AI, GANs, Fake Satellite Image, Geographic Feature}
}
  • Refine by Author
  • 1 Kang, Jeon-Young
  • 1 Kang, Yuhao
  • 1 Li, Bo
  • 1 Michels, Alexander
  • 1 Park, Jinwoo
  • Show More...

  • Refine by Classification
  • 1 Applied computing → Earth and atmospheric sciences
  • 1 Applied computing → Health informatics
  • 1 Applied computing → Transportation
  • 1 Human-centered computing → Geographic visualization
  • 1 Human-centered computing → Human computer interaction (HCI)

  • Refine by Keyword
  • 1 Cartography
  • 1 DALL-E
  • 1 Deep Learning and AI
  • 1 Ethics
  • 1 Fake Satellite Image
  • Show More...

  • Refine by Type
  • 3 document

  • Refine by Publication Year
  • 2 2023
  • 1 2018

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact