Volume
LIPIcs, Volume 346
13th International Conference on Geographic Information Science (GIScience 2025)
Publication Details
- published at: 2025-08-15
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-95977-378-2
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Document
Complete Volume
LIPIcs, Volume 346, GIScience 2025, Complete Volume
Abstract
LIPIcs, Volume 346, GIScience 2025, Complete Volume
Cite as
13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 1-296, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Proceedings{silanowicka_et_al:LIPIcs.GIScience.2025,
title = {{LIPIcs, Volume 346, GIScience 2025, Complete Volume}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {1--296},
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},
URN = {urn:nbn:de:0030-drops-244423},
doi = {10.4230/LIPIcs.GIScience.2025},
annote = {Keywords: LIPIcs, Volume 346, GIScience 2025, Complete Volume}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization
Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as
13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 0:i-0:xvi, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{silanowicka_et_al:LIPIcs.GIScience.2025.0,
author = {Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
title = {{Front Matter, Table of Contents, Preface, Conference Organization}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {0:i--0:xvi},
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.2025.0},
URN = {urn:nbn:de:0030-drops-244351},
doi = {10.4230/LIPIcs.GIScience.2025.0},
annote = {Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Leveraging Open-Source Satellite-Derived Building Footprints for Height Inference
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
CityJSON Management Using Multi-Model Graph Database to Support 3D Urban Data Management
Abstract
The prevalence of 3D city models in urban applications is increasing due to their lightweight and flexibility, making them adaptable to various applications. However, effective data interoperability remains an issue. Managing 3D city models within a database can improve urban data management applications such as data enrichment and efficient querying. Motivated by the need for better interoperability of 3D city models, this paper proposes a novel method for storing CityJSON using the concept of a multi-model graph database as a foundation for enriching its semantics. The proposed approach involves decomposing CityJSON objects into smaller JSON components, which are then abstracted into graph elements. Parent-child and other relationship attributes are modelled to capture the hierarchical and associative structures of the CityJSON data. A specific programme is employed to preprocess CityJSON data based on several conditions before being loaded into the graph database. Our multi-model approach allows three types of queries: document, graph, and hybrid. The latter combines both document and graph query. Comparative evaluation against relational databases demonstrates that the proposed method outperforms in terms of query performance. The improved query performance is attributed to the advantage of graph database that reduces the need for joins and the ability to efficiently index and navigate JSON data. The findings of this study establish a foundation for semantic enrichment of 3D city models to improve interoperability and support advanced urban data management.
Cite as
Muhammad Syafiq, Suhaibah Azri, and Uznir Ujang. CityJSON Management Using Multi-Model Graph Database to Support 3D Urban Data Management. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{syafiq_et_al:LIPIcs.GIScience.2025.2,
author = {Syafiq, Muhammad and Azri, Suhaibah and Ujang, Uznir},
title = {{CityJSON Management Using Multi-Model Graph Database to Support 3D Urban Data Management}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {2:1--2:15},
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.2025.2},
URN = {urn:nbn:de:0030-drops-238310},
doi = {10.4230/LIPIcs.GIScience.2025.2},
annote = {Keywords: CityJSON, Graph Database, 3D City Model, 3D GIS, Interoperability}
}
Document
Enriching Location Representation with Detailed Semantic Information
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
Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs
Abstract
Geospatial Knowledge Graphs (GeoKGs) represent a significant advancement in the integration of AI-driven geographic information, facilitating interoperable and semantically rich geospatial analytics across various domains. This paper explores the use of topologically enriched GeoKGs, built on an explicit representation of S2 Geometry alongside precomputed topological relations, for constructing efficient geospatial analysis workflows within and across knowledge graphs (KGs).
Using the SAWGraph knowledge graph as a case study focused on enviromental contamination by PFAS, we demonstrate how this framework supports fundamental GIS operations - such as spatial filtering, proximity analysis, overlay operations and network analysis - in a GeoKG setting while allowing for the easy linking of these operations with one another and with semantic filters. This enables the efficient execution of complex geospatial analyses as semantically-explicit queries and enhances the usability of geospatial data across graphs. Additionally, the framework eliminates the need for explicit support for GeoSPARQL’s topological operations in the utilized graph databases and better integrates spatial knowledge into the overall semantic inference process supported by RDFS and OWL ontologies.
Cite as
Katrina Schweikert, David K. Kedrowski, Shirly Stephen, and Torsten Hahmann. Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 4:1-4:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{schweikert_et_al:LIPIcs.GIScience.2025.4,
author = {Schweikert, Katrina and Kedrowski, David K. and Stephen, Shirly and Hahmann, Torsten},
title = {{Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {4:1--4:22},
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.2025.4},
URN = {urn:nbn:de:0030-drops-238332},
doi = {10.4230/LIPIcs.GIScience.2025.4},
annote = {Keywords: knowledge graph, GeoKG, spatial analysis, ontology, SPARQL, GeoSPARQL, discrete global grid system, S2 geometry, GeoAI, PFAS}
}
Document
Analysis of Points of Interests Recommended for Leisure Walk Descriptions
Abstract
Leisure walking is a physical activity where locomotion through a natural or even urban environment is the goal in itself, e.g., in pursuit of health and wellbeing. In contrast to destination-oriented walks that are focused on navigation efficiency (i.e., shortest or simplest walk from source to destination), leisure walks emphasize experiencing the environment, engaging in activities, and discovering places that may be off route, or intermediate destinations en-route, summarily called points of interest (POIs). POIs are key for recommending leisure walks, yet a detailed analysis of POIs in the context of leisure walking is missing in the literature. This study extracts and annotates POIs of leisure walking recommendations available in WalkingMaps.com.au, creating an annotated dataset to address this research gap and provide a first analysis of leisure walking descriptions. We classify POIs using the verbal description provided in the dataset, match them with data available in OpenStreetMap (OSM), and compare the POIs with nearby alternatives in OSM. Our analysis reveals thematic and spatial patterns in POI selection, offering a machine learning approach to model POI choices for leisure walks. We further evaluate the availability of rich data in OSM for future automated leisure walking recommendation. This study contributes to automated systems for recommending leisure walks, tailoring suggestions based on available information in the spatial open data, and presents an annotated dataset to facilitate future research in this field.
Cite as
Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, and Stephan Winter. Analysis of Points of Interests Recommended for Leisure Walk Descriptions. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{hamzei_et_al:LIPIcs.GIScience.2025.5,
author = {Hamzei, Ehsan and Bui, Thi Minh Hoai and Tomko, Martin and Winter, Stephan},
title = {{Analysis of Points of Interests Recommended for Leisure Walk Descriptions}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {5:1--5:16},
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.2025.5},
URN = {urn:nbn:de:0030-drops-238341},
doi = {10.4230/LIPIcs.GIScience.2025.5},
annote = {Keywords: leisure walks, points of interest, places, platial information}
}
Document
MODAP: A Multi-City Open Data & Analytics Platform for Micromobility Research
Abstract
Over the past decade, micromobility services, particularly electric vehicles for personal short-distance trips, have experienced significant growth. Major cities around the world now host extensive fleets of vehicles available for short-term public rental. While previous research has examined usage patterns within and between a few select cities, large, open, and publicly accessible data sets for analyzing mobility across multiple cities are extremely limited. I have collected, curated, and aggregated over twenty million e-scooter and e-bicycle trips across five major cities and are openly releasing aggregated data for use by mobility and sustainable transport researchers, urban planners, and policymakers. To accompany these data, I developed MODAP (Micromobility Open Data & Analytics Platform), a geovisual analytics tool that empowers researchers to explore the temporal and regional patterns of e-mobility trips within our open data set and download the data for offline analysis. My objective is to foster further research into city-scale mobility patterns and to equip researchers, community members, and policymakers with the necessary tools to conduct this work.
Cite as
Grant McKenzie. MODAP: A Multi-City Open Data & Analytics Platform for Micromobility Research. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{mckenzie:LIPIcs.GIScience.2025.6,
author = {McKenzie, Grant},
title = {{MODAP: A Multi-City Open Data \& Analytics Platform for Micromobility Research}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {6:1--6:14},
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.2025.6},
URN = {urn:nbn:de:0030-drops-238353},
doi = {10.4230/LIPIcs.GIScience.2025.6},
annote = {Keywords: open data, mobility, geovisualization, micromobility}
}
Document
A Modularity-Driven Framework for Unraveling Congestion Centers with Enhanced Spatial-Semantic Features
Abstract
The propagation of traffic congestion is a complicated spatiotemporal phenomenon in urban networks. Extensive studies mainly relied on dynamic Bayesian network or deep learning approaches. However, they often struggle to adapt seamlessly to diverse data granularities, limiting their applicability. In this study, we propose a modularity-driven method to unravel the spatiotemporal congestion propagation centers, effectively addressing temporal granularity challenges through the use of the fast Fourier Transform (FFT). Our framework distinguishes itself due to its capacity to integrate enhanced spatial-semantic features while eliminating temporal granularity dependence, which consists of two data-driven modules. One is adaptive adjacency matrix learning module, which captures the spatiotemporal relationship from evolving congestion graphs by fusing node degree, spatial proximity, and the FFT of traffic state indices. The other one is local search module, which employs local dominance principles to unravel the congestion propagation centers. We validate our proposed methodology on the large-scale traffic networks in New York City, the United States. An ablation study on the dataset reveals that the combination of the three features achieves the highest modularity scores of 0.65. The contribution of our work is to provide a novel way to infer the propagation centers of traffic congestion, and reveals the flexibility of extending our framework at temporal scales. The network resilience and dynamic evolution of the identified congestion centers can provide implications for actional decisions.
Cite as
Weihua Huan, Xintao Liu, and Wei Huang. A Modularity-Driven Framework for Unraveling Congestion Centers with Enhanced Spatial-Semantic Features. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 7:1-7:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{huan_et_al:LIPIcs.GIScience.2025.7,
author = {Huan, Weihua and Liu, Xintao and Huang, Wei},
title = {{A Modularity-Driven Framework for Unraveling Congestion Centers with Enhanced Spatial-Semantic Features}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {7:1--7:11},
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.2025.7},
URN = {urn:nbn:de:0030-drops-238362},
doi = {10.4230/LIPIcs.GIScience.2025.7},
annote = {Keywords: Congestion center, Temporal granularity, Fast Fourier Transform, Local dominance}
}
Document
BERT4Traj: Transformer-Based Trajectory Reconstruction for Sparse Mobility Data
Abstract
Understanding human mobility is essential for applications in public health, transportation, and urban planning. However, mobility data often suffers from sparsity due to limitations in data collection methods, such as infrequent GPS sampling or call detail record (CDR) data that only capture locations during communication events. To address this challenge, we propose BERT4Traj, a transformer-based model that reconstructs complete mobility trajectories by predicting hidden visits in sparse movement sequences. Inspired by BERT’s masked language modeling objective and self-attention mechanisms, BERT4Traj leverages spatial embeddings, temporal embeddings, and contextual background features such as demographics and anchor points. We evaluate BERT4Traj on real-world CDR and GPS datasets collected in Kampala, Uganda, demonstrating that our approach significantly outperforms traditional models such as Markov Chains, KNN, RNNs, and LSTMs. Our results show that BERT4Traj effectively reconstructs detailed and continuous mobility trajectories, enhancing insights into human movement patterns.
Cite as
Hao Yang, Angela Yao, Christopher C. Whalen, and Gengchen Mai. BERT4Traj: Transformer-Based Trajectory Reconstruction for Sparse Mobility Data. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 8:1-8:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{yang_et_al:LIPIcs.GIScience.2025.8,
author = {Yang, Hao and Yao, Angela and Whalen, Christopher C. and Mai, Gengchen},
title = {{BERT4Traj: Transformer-Based Trajectory Reconstruction for Sparse Mobility Data}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {8:1--8:9},
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.2025.8},
URN = {urn:nbn:de:0030-drops-238373},
doi = {10.4230/LIPIcs.GIScience.2025.8},
annote = {Keywords: Human Mobility, Trajectory Reconstruction, Deep Learning, CDR, GPS}
}
Document
Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach
Abstract
Effective resilience analysis of road networks is fundamental to building sustainable and disaster prepared cities. Identifying which road segments share similar vulnerabilities is important for pinpointing high-risk areas within the network and implementing measures to safeguard them against future disruptions. Graph-based community detection can be applied to group together areas of the network sharing similar structural vulnerabilities. However, current graph-based community detection methods either struggle with integrating node features during partitioning or do not account for the path-based dependencies in road networks. This paper introduces the Path-based Community Embedding (PCE) model, an approach that leverages path-based embeddings to overcome these limitations. PCE combines the strengths of graph attention networks and Long Short-Term Memory models (LSTMs) to learn representations that incorporate both local neighborhood information and long-range path dependencies. Our results on the Santa Barbara road network show that PCE improves community detection performance for resilience analysis, thus offering a powerful tool for urban planners and transportation engineers to preemptively identify vulnerabilities in road networks.
Cite as
Christopher Wagner, Somayeh Dodge, and Danial Alizadeh. Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 9:1-9:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{wagner_et_al:LIPIcs.GIScience.2025.9,
author = {Wagner, Christopher and Dodge, Somayeh and Alizadeh, Danial},
title = {{Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {9:1--9:10},
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.2025.9},
URN = {urn:nbn:de:0030-drops-238380},
doi = {10.4230/LIPIcs.GIScience.2025.9},
annote = {Keywords: road networks, resilience analysis, machine learning, graph neural networks}
}
Document
Geovicla: Automated Classification of Interactive Web-Based Geovisualizations
Abstract
The exponential growth of interactive geovisualizations on the Web has underscored the need for automated techniques to enhance their findability. In this paper, we present the Geovicla dataset (2.5K instances), constructed through the harvesting and manual labelling of webpages from a broad range of domains. The webpages are categorized into three groups: "interactive visualisation", "interactive geovisualisation" and "`no interactive visualisation". Using this dataset, we compared three approaches for interactive (geo)visualization classification: (i) a heuristic-based approach (i.e. using manually derived rules), (ii) a feature-engineering approach (i.e. hand-crafted feature vectors combined with machine learning classifiers) and (iii) an embedding-based approach (i.e. automatically generated large language model (LLM) embeddings with machine learning classifiers). The results indicate that LLM embeddings, when used in conjunction with a multilayer perceptron, form a promising combination, achieving up to 74% accuracy for multiclass classification and 75% for binary classification. The dataset and the insights gained from our empirical comparison offer valuable resources for GIScience researchers aiming to enhance the discoverability of interactive geovisualizations.
Cite as
Phil Hüffer, Auriol Degbelo, and Benjamin Risse. Geovicla: Automated Classification of Interactive Web-Based Geovisualizations. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 10:1-10:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{huffer_et_al:LIPIcs.GIScience.2025.10,
author = {H\"{u}ffer, Phil and Degbelo, Auriol and Risse, Benjamin},
title = {{Geovicla: Automated Classification of Interactive Web-Based Geovisualizations}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {10:1--10:12},
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.2025.10},
URN = {urn:nbn:de:0030-drops-238397},
doi = {10.4230/LIPIcs.GIScience.2025.10},
annote = {Keywords: spatial information search, geovisualization search, findable interactive geovisualization, webpage classification}
}
Document
Georeferencing Historical Maps at Scale
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}
}
Document
Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing
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
Assessing Map Reproducibility with Visual Question-Answering: An Empirical Evaluation
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
Guiding Geospatial Analysis Processes in Dealing with Modifiable Areal Unit Problems
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
Accommodating Space-Time Scaling Issues in GAM-Based Varying Coefficient Models
Abstract
The paper describes modifications to spatial and temporal varying coefficient (STVC) modelling, using Generalized Additive Models (GAMs). Previous work developed tools using Gaussian Process (GP) thin plate splines parameterised with location and time variables, and has presented a space-time toolkit in the stgam R package, providing wrapper functions to the mgcv R package. However, whilst thin plate smooths with GP bases are acceptable for working with spatial problems they are not for working with space and time combined. A more robust approach is to use a tensor product smooth with GP basis. However, these in turn require correlation function length scale or range parameters (ρ) to be defined. These are distances (in space or time) at which the correlation function falls below some value, and can be used to indicate the scale of spatial and temporal dependencies between response and predictor variables (similar to geographically weighted bandwidths). The paper describes the problem in detail, illustrates an approach for optimising ρ and methods for determining model specification.
Cite as
Alexis Comber, Paul Harris, and Chris Brunsdon. Accommodating Space-Time Scaling Issues in GAM-Based Varying Coefficient Models. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 15:1-15:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@InProceedings{comber_et_al:LIPIcs.GIScience.2025.15,
author = {Comber, Alexis and Harris, Paul and Brunsdon, Chris},
title = {{Accommodating Space-Time Scaling Issues in GAM-Based Varying Coefficient Models}},
booktitle = {13th International Conference on Geographic Information Science (GIScience 2025)},
pages = {15:1--15:9},
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.2025.15},
URN = {urn:nbn:de:0030-drops-238440},
doi = {10.4230/LIPIcs.GIScience.2025.15},
annote = {Keywords: Spatial Analysis, Spatiotemproal Analysis}
}
Document
What, When, and Where Do You Mean? Detecting Spatio-Temporal Concept Drift in Scientific Texts
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
The Inherent Structure of Experiments as a Constraint to Spatial Analysis and Modeling
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
U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping
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
Search Space Reduction Using Species Distribution Modeling with Simulated Pollen Signatures
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}
}