74 Search Results for "Adams, Benjamin"

Volume

LIPIcs, Volume 346

13th International Conference on Geographic Information Science (GIScience 2025)

GIScience 2025, August 26-29, 2025, Christchurch, New Zealand

Editors: Katarzyna Sila-Nowicka, Antoni Moore, David O'Sullivan, Benjamin Adams, and Mark Gahegan

Volume

LIPIcs, Volume 315

16th International Conference on Spatial Information Theory (COSIT 2024)

COSIT 2024, September 17-20, 2024, Québec City, Canada

Editors: Benjamin Adams, Amy L. Griffin, Simon Scheider, and Grant McKenzie

Document
DOI: 10.4230/LIPIcs.ESA.2025.41
Min-Max Correlation Clustering via Neighborhood Similarity

Authors: Nairen Cao, Steven Roche, and Hsin-Hao Su

Published in: LIPIcs, Volume 351, 33rd Annual European Symposium on Algorithms (ESA 2025)

Abstract
We present an efficient algorithm for the min-max correlation clustering problem. The input is a complete graph where edges are labeled as either positive (+) or negative (-), and the objective is to find a clustering that minimizes the 𝓁_∞-norm of the disagreement vector over all vertices. We address this problem with an efficient (3 + ε)-approximation algorithm that runs in nearly linear time, Õ(|E^+|), where |E^+| denotes the number of positive edges. This improves upon the previous best-known approximation guarantee of 4 by Heidrich, Irmai, and Andres [Heidrich et al., 2024], whose algorithm runs in O(|V|² + |V| D²) time, where |V| is the number of nodes and D is the maximum degree in the graph (V,E^+). Furthermore, we extend our algorithm to the massively parallel computation (MPC) model and the semi-streaming model. In the MPC model, our algorithm runs on machines with memory sublinear in the number of nodes and takes O(1) rounds. In the streaming model, our algorithm requires only Õ(|V|) space, where |V| is the number of vertices in the graph. Our algorithms are purely combinatorial. They are based on a novel structural observation about the optimal min-max instance, which enables the construction of a (3 + ε)-approximation algorithm using O(|E^+|) neighborhood similarity queries. By leveraging random projection, we further show these queries can be computed in nearly linear time.
Cite as

Nairen Cao, Steven Roche, and Hsin-Hao Su. Min-Max Correlation Clustering via Neighborhood Similarity. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 41:1-41:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{cao_et_al:LIPIcs.ESA.2025.41,
  author =	{Cao, Nairen and Roche, Steven and Su, Hsin-Hao},
  title =	{{Min-Max Correlation Clustering via Neighborhood Similarity}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{41:1--41:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-395-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{351},
  editor =	{Benoit, Anne and Kaplan, Haim and Wild, Sebastian 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.2025.41},
  URN =		{urn:nbn:de:0030-drops-245098},
  doi =		{10.4230/LIPIcs.ESA.2025.41},
  annote =	{Keywords: Min Max Correlation Clustering, Approximate algorithms}
}
Document
DOI: 10.4230/OASIcs.SpaceCHI.2025.22
Mixed-Initiative Dynamic Autonomy Through Variable Levels of Immersion and Control (MIDA-VIC): A New Paradigm for Collaborative Robotic Teleoperation in Space Exploration

Authors: Hans-Christian Jetter, Leon Raule, Jens Gerken, and Sören Pirk

Published in: OASIcs, Volume 130, Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)

Abstract
In this position paper, we propose the new control paradigm and conceptual framework MIDA-VIC for collaborative robotic teleoperation in space exploration and beyond. Such teleoperation is a complex and demanding team effort with distributed responsibilities that require both efficient human-robot and human-human collaboration. To address these challenges, we propose a new paradigm of mixed-initiative dynamic autonomy for robotic teleoperation. It exploits recent advances in human-computer interaction (HCI), human-robot interaction (HRI), augmented and virtual reality (AR/VR), and artificial intelligence (AI) research. By integrating methods from multiple fields, our paradigm allows human operators to choose their preferred level of immersion, from traditional 2D graphical user interfaces (GUIs) to fully immersive AR/VR environments. It also supports a dynamic adjustment of the level of control, ranging from direct motor commands (e.g., using a joystick) to high-level task delegation using AI (e.g., instructing the robot via natural language to select a path or explore autonomously). In addition, we propose a mixed-initiative paradigm in which a robot can also take the initiative, request human assistance, and propose the specific level of immersion and control to the human operator that it currently considers useful for effective and efficient collaboration.
Cite as

Hans-Christian Jetter, Leon Raule, Jens Gerken, and Sören Pirk. Mixed-Initiative Dynamic Autonomy Through Variable Levels of Immersion and Control (MIDA-VIC): A New Paradigm for Collaborative Robotic Teleoperation in Space Exploration. In Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025). Open Access Series in Informatics (OASIcs), Volume 130, pp. 22:1-22:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{jetter_et_al:OASIcs.SpaceCHI.2025.22,
  author =	{Jetter, Hans-Christian and Raule, Leon and Gerken, Jens and Pirk, S\"{o}ren},
  title =	{{Mixed-Initiative Dynamic Autonomy Through Variable Levels of Immersion and Control (MIDA-VIC): A New Paradigm for Collaborative Robotic Teleoperation in Space Exploration}},
  booktitle =	{Advancing Human-Computer Interaction for Space Exploration (SpaceCHI 2025)},
  pages =	{22:1--22:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-384-3},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{130},
  editor =	{Bensch, Leonie and Nilsson, Tommy and Nisser, Martin and Pataranutaporn, Pat and Schmidt, Albrecht and Sumini, Valentina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SpaceCHI.2025.22},
  URN =		{urn:nbn:de:0030-drops-240122},
  doi =		{10.4230/OASIcs.SpaceCHI.2025.22},
  annote =	{Keywords: Collaboration, Teleoperation, Robot, Space Exploration}
}
Document
DOI: 10.4230/LIPIcs.ITC.2025.3
Leakage-Resilience of Shamir’s Secret Sharing: Identifying Secure Evaluation Places

Authors: Jihun Hwang, Hemanta K. Maji, Hai H. Nguyen, and Xiuyu Ye

Published in: LIPIcs, Volume 343, 6th Conference on Information-Theoretic Cryptography (ITC 2025)

Abstract
Can Shamir’s secret-sharing protect its secret even when all shares are partially compromised? For instance, repairing Reed-Solomon codewords, when possible, recovers the entire secret in the corresponding Shamir’s secret sharing. Yet, Shamir’s secret sharing mitigates various side-channel threats, depending on where its "secret-sharing polynomial" is evaluated. Although most evaluation places yield secure schemes, none are known explicitly; even techniques to identify them are unknown. Our work initiates research into such classifier constructions and derandomization objectives. In this work, we focus on Shamir’s scheme over prime fields, where every share is required to reconstruct the secret. We investigate the security of these schemes against single-bit probes into shares stored in their native binary representation. Technical analysis is particularly challenging when dealing with Reed-Solomon codewords over prime fields, as observed recently in the code repair literature. Furthermore, ensuring the statistical independence of the leakage from the secret necessitates the elimination of any subtle correlations between them. In this context, we present: 1) An efficient algorithm to classify evaluation places as secure or vulnerable against the least-significant-bit leakage. 2) Modulus choices where the classifier above extends to any single-bit probe per share. 3) Explicit modulus choices and secure evaluation places for them. On the way, we discover new bit-probing attacks on Shamir’s scheme, revealing surprising correlations between the leakage and the secret, leading to vulnerabilities when choosing evaluation places naïvely. Our results rely on new techniques to analyze the security of secret-sharing schemes against side-channel threats. We connect their leakage resilience to the orthogonality of square wave functions, which, in turn, depends on the 2-adic valuation of rational approximations. These techniques, novel to the security analysis of secret sharings, can potentially be of broader interest.
Cite as

Jihun Hwang, Hemanta K. Maji, Hai H. Nguyen, and Xiuyu Ye. Leakage-Resilience of Shamir’s Secret Sharing: Identifying Secure Evaluation Places. In 6th Conference on Information-Theoretic Cryptography (ITC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 343, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hwang_et_al:LIPIcs.ITC.2025.3,
  author =	{Hwang, Jihun and Maji, Hemanta K. and Nguyen, Hai H. and Ye, Xiuyu},
  title =	{{Leakage-Resilience of Shamir’s Secret Sharing: Identifying Secure Evaluation Places}},
  booktitle =	{6th Conference on Information-Theoretic Cryptography (ITC 2025)},
  pages =	{3:1--3:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-385-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{343},
  editor =	{Gilboa, Niv},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITC.2025.3},
  URN =		{urn:nbn:de:0030-drops-243531},
  doi =		{10.4230/LIPIcs.ITC.2025.3},
  annote =	{Keywords: Shamir’s secret sharing, leakage resilience, physical bit probing, secure evaluation places, secure modulus choice, square wave families, LLL algorithm, Fourier analysis}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2025.88
Color Refinement for Relational Structures

Authors: Benjamin Scheidt and Nicole Schweikardt

Published in: LIPIcs, Volume 345, 50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025)

Abstract
Color Refinement, also known as Naive Vertex Classification, is a classical method to distinguish graphs by iteratively computing a coloring of their vertices. While it is traditionally used as an imperfect way to test for isomorphism, the algorithm has permeated many other, seemingly unrelated, areas of computer science. The method is algorithmically simple, and it has a well-understood distinguishing power: it has been logically characterized by Immerman and Lander (1990) and Cai, Fürer, Immerman (1992), who showed that it distinguishes precisely those graphs that can be distinguished by a sentence of first-order logic with counting quantifiers and only two variables. A combinatorial characterization was given by Dvořák (2010), who showed that it distinguishes precisely those graphs that differ in the number of homomorphisms from some tree. In this paper, we introduce Relational Color Refinement (RCR, for short), a generalization of the Color Refinement method from graphs to arbitrary relational structures, whose distinguishing power admits the equivalent combinatorial and logical characterizations as Color Refinement has on graphs: we show that RCR distinguishes precisely those structures that differ in the number of homomorphisms from an acyclic connected relational structure. Further, we show that RCR distinguishes precisely those structures that are distinguished by a sentence of the guarded fragment of first-order logic with counting quantifiers. Additionally, we show that for every fixed finite relational signature, RCR can be implemented to run on structures of that signature in time O(N⋅log N), where N denotes the number of tuples present in the structure.
Cite as

Benjamin Scheidt and Nicole Schweikardt. Color Refinement for Relational Structures. In 50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 345, pp. 88:1-88:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{scheidt_et_al:LIPIcs.MFCS.2025.88,
  author =	{Scheidt, Benjamin and Schweikardt, Nicole},
  title =	{{Color Refinement for Relational Structures}},
  booktitle =	{50th International Symposium on Mathematical Foundations of Computer Science (MFCS 2025)},
  pages =	{88:1--88:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-388-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{345},
  editor =	{Gawrychowski, Pawe{\l} and Mazowiecki, Filip and Skrzypczak, Micha{\l}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2025.88},
  URN =		{urn:nbn:de:0030-drops-241958},
  doi =		{10.4230/LIPIcs.MFCS.2025.88},
  annote =	{Keywords: color refinement, counting logics, homomorphism counts, homomorphism indistinguishability, guarded logics, pebble games, relational structures, alpha-acyclicity, join-trees}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.1
Leveraging Open-Source Satellite-Derived Building Footprints for Height Inference

Authors: Clinton Stipek, Taylor Hauser, Justin Epting, Jessica Moehl, and Daniel Adams

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

Abstract
At a global scale, cities are growing and characterizing the built environment is essential for deeper understanding of human population patterns, urban development, energy usage, climate change impacts, among others. Buildings are a key component of the built environment and significant progress has been made in recent years to scale building footprint extractions from satellite datum and other remotely sensed products. Billions of building footprints have recently been released by companies such as Microsoft and Google at a global scale. However, research has shown that depending on the methods leveraged to produce a footprint dataset, discrepancies can arise in both the number and shape of footprints produced. Therefore, each footprint dataset should be examined and used on a case-by-case study. In this work, we find through two experiments on Oak Ridge National Laboratory and Microsoft footprints within the same geographic extent that our approach of inferring height from footprint morphology features is source agnostic. Regardless of the differences associated with the methods used to produce a building footprint dataset, our approach of inferring height was able to overcome these discrepancies between the products and generalize, as evidenced by 98% of our results being within 3m of the ground-truthed height. This signifies that our approach can be applied to the billions of open-source footprints which are freely available to infer height, a key building metric. This work impacts the broader domain of urban science in which building height is a key, and limiting factor.
Cite as

Clinton Stipek, Taylor Hauser, Justin Epting, Jessica Moehl, and Daniel Adams. Leveraging Open-Source Satellite-Derived Building Footprints for Height Inference. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{stipek_et_al:LIPIcs.GIScience.2025.1,
  author =	{Stipek, Clinton and Hauser, Taylor and Epting, Justin and Moehl, Jessica and Adams, Daniel},
  title =	{{Leveraging Open-Source Satellite-Derived Building Footprints for Height Inference}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{1:1--1:20},
  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.2025.1},
  URN =		{urn:nbn:de:0030-drops-238306},
  doi =		{10.4230/LIPIcs.GIScience.2025.1},
  annote =	{Keywords: Building Height, Big Data, Machine Learning}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.12
Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing

Authors: Kalana Wijegunarathna, Kristin Stock, and Christopher B. Jones

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

Abstract
Millions of biological sample records collected in the last few centuries archived in natural history collections are un-georeferenced. Georeferencing complex locality descriptions associated with these collection samples is a highly labour-intensive task collection agencies struggle with. None of the existing automated methods exploit maps that are an essential tool for georeferencing complex relations. We present preliminary experiments and results of a novel method that exploits multi-modal capabilities of recent Large Multi-Modal Models (LMM). This method enables the model to visually contextualize spatial relations it reads in the locality description. We use a grid-based approach to adapt these auto-regressive models for this task in a zero-shot setting. Our experiments conducted on a small manually annotated dataset show impressive results for our approach (∼1 km Average distance error) compared to uni-modal georeferencing with Large Language Models and existing georeferencing tools. The paper also discusses the findings of the experiments in light of an LMM’s ability to comprehend fine-grained maps. Motivated by these results, a practical framework is proposed to integrate this method into a georeferencing workflow.
Cite as

Kalana Wijegunarathna, Kristin Stock, and Christopher B. Jones. Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 12:1-12:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{wijegunarathna_et_al:LIPIcs.GIScience.2025.12,
  author =	{Wijegunarathna, Kalana and Stock, Kristin and Jones, Christopher B.},
  title =	{{Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{12:1--12:19},
  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.2025.12},
  URN =		{urn:nbn:de:0030-drops-238412},
  doi =		{10.4230/LIPIcs.GIScience.2025.12},
  annote =	{Keywords: Large Multi-Modal Models, Large Language Models, LLM, Georeferencing, Natural History collections}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.13
Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation

Authors: Eftychia Koukouraki, Auriol Degbelo, and Christian Kray

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

Abstract
Reproducibility is a key principle of the modern scientific method. Maps, as an important means of communicating scientific results in GIScience and across disciplines, should be reproducible. Currently, map reproducibility assessment is done manually, which makes the assessment process tedious and time-consuming, ultimately limiting its efficiency. Hence, this work explores the extent to which Visual Question-Answering (VQA) can be used to automate some tasks relevant to map reproducibility assessment. We selected five state-of-the-art vision language models (VLMs) and followed a three-step approach to evaluate their ability to discriminate between maps and other images, interpret map content, and compare two map images using VQA. Our results show that current VLMs already possess map-reading capabilities and demonstrate understanding of spatial concepts, such as cardinal directions, geographic scope, and legend interpretation. Our paper demonstrates the potential of using VQA to support reproducibility assessment and highlights the outstanding issues that need to be addressed to achieve accurate, trustworthy map descriptions, thereby reducing the time and effort required by human evaluators.
Cite as

Eftychia Koukouraki, Auriol Degbelo, and Christian Kray. Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 13:1-13:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koukouraki_et_al:LIPIcs.GIScience.2025.13,
  author =	{Koukouraki, Eftychia and Degbelo, Auriol and Kray, Christian},
  title =	{{Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{13:1--13:12},
  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.2025.13},
  URN =		{urn:nbn:de:0030-drops-238426},
  doi =		{10.4230/LIPIcs.GIScience.2025.13},
  annote =	{Keywords: map comparison, computational reproducibility, visual question answering, large language models, GeoAI}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.18
U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping

Authors: Vit Kostejn, Yamil Essus, Jenna Abrahamson, and Ranga Raju Vatsavai

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

Abstract
In recent years, large pre-trained models, commonly referred to as foundation models, have become increasingly popular for various tasks leveraging transfer learning. This trend has expanded to remote sensing, where transformer-based foundation models such as Prithvi, msGFM, and SatSwinMAE have been utilized for a range of applications. While these transformer-based models, particularly the Prithvi model, exhibit strong generalization capabilities, they have limitations on capturing fine-grained details compared to convolutional neural network architectures like U-Net in segmentation tasks. In this paper, we propose a novel architecture, U-Prithvi, which combines the strengths of the Prithvi transformer with those of U-Net. We introduce a RandomHalfMaskLayer to ensure balanced learning from both models during training. Our approach is evaluated on the Sen1Floods11 dataset for flood inundation mapping, and experimental results demonstrate better performance of U-Prithvi over both individual models, achieving improved performance on out-of-sample data. While this principle is illustrated using the Prithvi model, it is easily adaptable to other foundation models.
Cite as

Vit Kostejn, Yamil Essus, Jenna Abrahamson, and Ranga Raju Vatsavai. U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 18:1-18:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{kostejn_et_al:LIPIcs.GIScience.2025.18,
  author =	{Kostejn, Vit and Essus, Yamil and Abrahamson, Jenna and Vatsavai, Ranga Raju},
  title =	{{U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{18:1--18:17},
  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.2025.18},
  URN =		{urn:nbn:de:0030-drops-238479},
  doi =		{10.4230/LIPIcs.GIScience.2025.18},
  annote =	{Keywords: GeoAI, flood mapping, foundation model, U-Net, Prithvi}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.19
Search Space Reduction Using Species Distribution Modeling with Simulated Pollen Signatures

Authors: Haoyu Wang, Jennifer A. Miller, and Shalene Jha

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

Abstract
Microscopic trace materials, such as pollen, are an important category of forensic evidence recovered during investigations. As an environmentally ubiquitous substance that can attach to various surfaces, pollen enables the linking of objects and people in space and time. In this study, we assessed the extent to which the search space could be reduced using simulated pollen signatures. These signatures were compiled by randomly selecting pairs of geographic coordinates on the Earth’s terrestrial land and querying the Global Biodiversity Information Facility (GBIF) database to identify plant taxa within 50 meters of the coordinates. These taxa were then treated as the parent taxa of the pollen, simulating the hypothetical attachment of pollen signatures to objects or individuals. For each identified pollen taxon, we modeled habitat suitability for the parent plant taxa and combined the spatial distributions to refine the geolocation search area. Since the actual coordinates for these locations of interest were known, we were able to evaluate the global performance of the search space reduction under the assumption of an extreme constraint that no other contextual information was available.
Cite as

Haoyu Wang, Jennifer A. Miller, and Shalene Jha. Search Space Reduction Using Species Distribution Modeling with Simulated Pollen Signatures. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 19:1-19:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{wang_et_al:LIPIcs.GIScience.2025.19,
  author =	{Wang, Haoyu and Miller, Jennifer A. and Jha, Shalene},
  title =	{{Search Space Reduction Using Species Distribution Modeling with Simulated Pollen Signatures}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{19:1--19:6},
  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.2025.19},
  URN =		{urn:nbn:de:0030-drops-238485},
  doi =		{10.4230/LIPIcs.GIScience.2025.19},
  annote =	{Keywords: geoforensics, species distribution modeling, search space reduction}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.16
What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts

Authors: Meilin Shi, Krzysztof Janowicz, Zilong Liu, Mina Karimi, Ivan Majic, and Alexandra Fortacz

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

Abstract
Inundated by the rapidly expanding AI research nowadays, the research community requires more effective research data management than ever. A key challenge lies in the evolving nature of concepts embedded in the growing body of research publications. As concepts evolve over time (e.g., keywords like global warming become more commonly referred to as climate change), past research may become harder to find and interpret in a modern context. This phenomenon, known as concept drift, affects how research topics and keywords are understood, categorized, and retrieved. Beyond temporal drift, such variations also occur across geographic space, reflecting differences in local policies, research priorities, and so forth. In this work, we introduce the notion of spatio-temporal concept drift to capture how concepts in scientific texts evolve across both space and time. Using a scientometric dataset in geographic information science, we detect how research keywords drifted across countries and years using word embeddings. By detecting spatio-temporal concept drift, we can better align archival research and bridge regional differences, ensuring scientific knowledge remains findable and interoperable within evolving research landscapes.
Cite as

Meilin Shi, Krzysztof Janowicz, Zilong Liu, Mina Karimi, Ivan Majic, and Alexandra Fortacz. What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 16:1-16:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{shi_et_al:LIPIcs.GIScience.2025.16,
  author =	{Shi, Meilin and Janowicz, Krzysztof and Liu, Zilong and Karimi, Mina and Majic, Ivan and Fortacz, Alexandra},
  title =	{{What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{16:1--16:18},
  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.2025.16},
  URN =		{urn:nbn:de:0030-drops-238450},
  doi =		{10.4230/LIPIcs.GIScience.2025.16},
  annote =	{Keywords: Concept Drift, Ontology, Large Language Models, Research Data Management}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.14
Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems

Authors: Guoray Cai and Yue Hao

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

Abstract
Geospatial analysis has been widely applied in different domains for critical decision making. However, the results of spatial analysis are often plagued with uncertainties due to measurement errors, choice of data representations, and unintended transformation artifacts. A well known example of such problems is the Modifiable Areal Unit Problem (MAUP) which has well documented effects on the outcome of spatial analysis on area-aggregated data. Existing methods for addressing the effects of MAUP are limited, are technically complex, and are often inaccessible to practitioners. As a result, analysts tend to ignore the effects of MAUP in practice due to lack of expertise, high cognitive loads, and resource limitations. To address these challenges, this paper proposes a machine-guidance approach to augment the analyst’s capacity in mitigating the effect of MAUP. Based on an analysis of practical challenges faced by human analysts, we identified multiple opportunities for the machine to guide the analysts by alerting to the rise of MAUP, assessing the impact of MAUP, choosing mitigation methods, and generating visual guidance messages using GIS functions and tools. For each of the opportunities, we characterize the behavior patterns and the underlying guidance strategies that generate the behavior. We illustrate the behavior of machine guidance using a hotspot analysis scenario in the context of crime policing, where MAUP has strong effects on the patterns of crime hotspots. Finally, we describe the computational framework used to build a prototype guidance system and identify a number of research questions to be addressed. We conclude by discussing how the machine guidance approach could be an answer to some of the toughest problems in geospatial analysis.
Cite as

Guoray Cai and Yue Hao. Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 14:1-14:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{cai_et_al:LIPIcs.GIScience.2025.14,
  author =	{Cai, Guoray and Hao, Yue},
  title =	{{Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{14:1--14:18},
  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.2025.14},
  URN =		{urn:nbn:de:0030-drops-238433},
  doi =		{10.4230/LIPIcs.GIScience.2025.14},
  annote =	{Keywords: Machine Guidance, Geo-Spatial Analysis, Modifiable Areal Unit Problem (MAUP)}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.17
The Inherent Structure of Experiments as a Constraint to Spatial Analysis and Modeling

Authors: Simon Scheider and Judith A. Verstegen

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

Abstract
We argue that in order to justify a modeling approach for a particular purpose, we need to better understand the experimental structure that is supposed to be represented by a given model application. For this purpose, we introduce a logic for specifying causal as well as spatio-temporal experiments, based on which we reinterpret Sinton’s structure of spatial information from a pragmatic, experimental viewpoint. We illustrate the use of this logic based on a landuse modeling example, showing to what extent remote sensing and simulation approaches can be justified by decomposing the example into experiments required for answering its main question.
Cite as

Simon Scheider and Judith A. Verstegen. The Inherent Structure of Experiments as a Constraint to Spatial Analysis and Modeling. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 17:1-17:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{scheider_et_al:LIPIcs.GIScience.2025.17,
  author =	{Scheider, Simon and Verstegen, Judith A.},
  title =	{{The Inherent Structure of Experiments as a Constraint to Spatial Analysis and Modeling}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{17:1--17:17},
  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.2025.17},
  URN =		{urn:nbn:de:0030-drops-238468},
  doi =		{10.4230/LIPIcs.GIScience.2025.17},
  annote =	{Keywords: pragmatic Logic, experimental Norms, spatio-temporal Models}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.11
Georeferencing Historical Maps at Scale

Authors: Rere-No-A-Rangi Pope and Marcus Frean

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

Abstract
This paper presents a novel approach to automatically georeferencing historical maps using an algorithm based on salient line intersections. Our algorithm addresses the challenges inherent in linking historical map images to contemporary cadastral data, particularly those due to temporal discrepancies, cartographic distortions, and map image noise. By extracting and comparing angular relationships between cadastral features, termed monads and dyads, we establish a robust method for performing record linkage by identifying corresponding spatial patterns across disparate datasets. We employ a Bayesian framework to quantify the likelihood of dyad matches corrupted by measurement noise. The algorithm’s performance was evaluated by selecting a map image and finding putative angle correspondences from the entirety of Aotearoa New Zealand. Even when restricted to a single dyad match, >99% of candidate regions can be successfully filtered out. We discuss the implications and limitations, and suggest strategies for further enhancing the algorithm’s robustness and efficiency. Our work is motivated by previous work in the areas of critical GIS, critical cartography and spatial justice and seeks to contribute to the areas of Spatial Data Science, Historical GIS and GIScience.
Cite as

Rere-No-A-Rangi Pope and Marcus Frean. Georeferencing Historical Maps at Scale. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 11:1-11:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pope_et_al:LIPIcs.GIScience.2025.11,
  author =	{Pope, Rere-No-A-Rangi and Frean, Marcus},
  title =	{{Georeferencing Historical Maps at Scale}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{11:1--11:11},
  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.2025.11},
  URN =		{urn:nbn:de:0030-drops-238400},
  doi =		{10.4230/LIPIcs.GIScience.2025.11},
  annote =	{Keywords: Historical GIS, Georeferencing, Record Linkage, Spatial Data Justice}
}
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