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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.05879 |
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| _version_ | 1866909965889830912 |
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| author | Moska, Julia Furman, Oleksii Kozaczko, Kacper Leszkiewicz, Szymon Polczyk, Jakub Gramacki, Piotr Szymański, Piotr |
| author_facet | Moska, Julia Furman, Oleksii Kozaczko, Kacper Leszkiewicz, Szymon Polczyk, Jakub Gramacki, Piotr Szymański, Piotr |
| contents | GeoAI is evolving rapidly, fueled by diverse geospatial datasets like traffic patterns, environmental data, and crowdsourced OpenStreetMap (OSM) information. While sophisticated AI models are being developed, existing benchmarks are often concentrated on single tasks and restricted to a single modality. As such, progress in GeoAI is limited by the lack of a standardized, multi-task, modality-agnostic benchmark for their systematic evaluation. This paper introduces a novel benchmark designed to assess the performance, accuracy, and efficiency of geospatial embedders. Our benchmark is modality-agnostic and comprises 7 distinct datasets from diverse cities across three continents, ensuring generalizability and mitigating demographic biases. It allows for the evaluation of GeoAI embedders on various phenomena that exhibit underlying geographic processes. Furthermore, we establish a simple and intuitive task-oriented model baselines, providing a crucial reference point for comparing more complex solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05879 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OBSR: Open Benchmark for Spatial Representations Moska, Julia Furman, Oleksii Kozaczko, Kacper Leszkiewicz, Szymon Polczyk, Jakub Gramacki, Piotr Szymański, Piotr Machine Learning GeoAI is evolving rapidly, fueled by diverse geospatial datasets like traffic patterns, environmental data, and crowdsourced OpenStreetMap (OSM) information. While sophisticated AI models are being developed, existing benchmarks are often concentrated on single tasks and restricted to a single modality. As such, progress in GeoAI is limited by the lack of a standardized, multi-task, modality-agnostic benchmark for their systematic evaluation. This paper introduces a novel benchmark designed to assess the performance, accuracy, and efficiency of geospatial embedders. Our benchmark is modality-agnostic and comprises 7 distinct datasets from diverse cities across three continents, ensuring generalizability and mitigating demographic biases. It allows for the evaluation of GeoAI embedders on various phenomena that exhibit underlying geographic processes. Furthermore, we establish a simple and intuitive task-oriented model baselines, providing a crucial reference point for comparing more complex solutions. |
| title | OBSR: Open Benchmark for Spatial Representations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.05879 |