3 Search Results for "Zeng, Zichao"

Document
DOI: 10.4230/LIPIcs.GIScience.2025.3
Enriching Location Representation with Detailed Semantic Information

Authors: Junyuan Liu, Xinglei Wang, and Tao Cheng

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Spatial representations that capture both structural and semantic characteristics of urban environments are essential for urban modeling. Traditional spatial embeddings often prioritize spatial proximity while underutilizing fine-grained contextual information from places. To address this limitation, we introduce CaLLiPer+, an extension of the CaLLiPer model that systematically integrates Point-of-Interest (POI) names alongside categorical labels within a multimodal contrastive learning framework. We evaluate its effectiveness on two downstream tasks - land use classification and socioeconomic status distribution mapping - demonstrating consistent performance gains of 4% to 11% over baseline methods. Additionally, we show that incorporating POI names enhances location retrieval, enabling models to capture complex urban concepts with greater precision. Ablation studies further reveal the complementary role of POI names and the advantages of leveraging pretrained text encoders for spatial representations. Overall, our findings highlight the potential of integrating fine-grained semantic attributes and multimodal learning techniques to advance the development of urban foundation models.
Cite as

Junyuan Liu, Xinglei Wang, and Tao Cheng. Enriching Location Representation with Detailed Semantic Information. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 3:1-3:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{liu_et_al:LIPIcs.GIScience.2025.3,
  author =	{Liu, Junyuan and Wang, Xinglei and Cheng, Tao},
  title =	{{Enriching Location Representation with Detailed Semantic Information}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{3:1--3:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.3},
  URN =		{urn:nbn:de:0030-drops-238322},
  doi =		{10.4230/LIPIcs.GIScience.2025.3},
  annote =	{Keywords: Location Embedding, Contrastive Learning, Pretrained Model}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2024.46
Saturation Results Around the Erdős-Szekeres Problem

Authors: Gábor Damásdi, Zichao Dong, Manfred Scheucher, and Ji Zeng

Published in: LIPIcs, Volume 293, 40th International Symposium on Computational Geometry (SoCG 2024)

Abstract
In this paper, we consider saturation problems related to the celebrated Erdős-Szekeres convex polygon problem. For each n ≥ 7, we construct a planar point set of size (7/8) ⋅ 2^{n-2} which is saturated for convex n-gons. That is, the set contains no n points in convex position while the addition of any new point creates such a configuration. This demonstrates that the saturation number is smaller than the Ramsey number for the Erdős-Szekeres problem. The proof also shows that the original Erdős-Szekeres construction is indeed saturated. Our construction is based on a similar improvement for the saturation version of the cups-versus-caps theorem. Moreover, we consider the generalization of the cups-versus-caps theorem to monotone paths in ordered hypergraphs. In contrast to the geometric setting, we show that this abstract saturation number is always equal to the corresponding Ramsey number.
Cite as

Gábor Damásdi, Zichao Dong, Manfred Scheucher, and Ji Zeng. Saturation Results Around the Erdős-Szekeres Problem. In 40th International Symposium on Computational Geometry (SoCG 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 293, pp. 46:1-46:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{damasdi_et_al:LIPIcs.SoCG.2024.46,
  author =	{Dam\'{a}sdi, G\'{a}bor and Dong, Zichao and Scheucher, Manfred and Zeng, Ji},
  title =	{{Saturation Results Around the Erd\H{o}s-Szekeres Problem}},
  booktitle =	{40th International Symposium on Computational Geometry (SoCG 2024)},
  pages =	{46:1--46:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-316-4},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{293},
  editor =	{Mulzer, Wolfgang and Phillips, Jeff M.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2024.46},
  URN =		{urn:nbn:de:0030-drops-199919},
  doi =		{10.4230/LIPIcs.SoCG.2024.46},
  annote =	{Keywords: Convex polygon, Cups-versus-caps, Monotone path, Saturation problem}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.44
Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera (Short Paper)

Authors: Natchapon Jongwiriyanurak, Zichao Zeng, Meihui Wang, James Haworth, Garavig Tanaksaranond, and Jan Boehm

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

Abstract
Traditional safety analysis methods based on historical crash data and simulation models have limitations in capturing real-world driving scenarios. In this experiment, panoramic videos recorded from a motorcyclist’s helmet in Bangkok, Thailand, were narrated using an image-to-text model and then put into a Large Language Model (LLM) to identify potential hazards and assess crash risks. The framework can assess static and moving objects with the potential for early warning and incident analysis. However, the limitations of the existing image-to-text model cause its inability to handle panoramic images effectively.
Cite as

Natchapon Jongwiriyanurak, Zichao Zeng, Meihui Wang, James Haworth, Garavig Tanaksaranond, and Jan Boehm. Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 44:1-44:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{jongwiriyanurak_et_al:LIPIcs.GIScience.2023.44,
  author =	{Jongwiriyanurak, Natchapon and Zeng, Zichao and Wang, Meihui and Haworth, James and Tanaksaranond, Garavig and Boehm, Jan},
  title =	{{Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{44:1--44:7},
  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.2023.44},
  URN =		{urn:nbn:de:0030-drops-189394},
  doi =		{10.4230/LIPIcs.GIScience.2023.44},
  annote =	{Keywords: Traffic incident risk, Large Language Model, Vision-Language Model}
}
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