Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations | Paper Citation Record

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations @esa (Ref

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Towards Reproducible LLM Evaluation: Quantifying Uncertainty in LLM Benchmark Scores

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations and Romano, G

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations G., Liu, D., Wang, S., Ouyang, J., and Yu, Q

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Evaluating Large Language Models Trained on Code

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations and Cohn, A

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations An Evaluation of ChatGPT-4's Qualitative Spatial Reasoning Capabilities in RCC-8

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Can Large Language Models Reason about the Region Connection Calculus?

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMs

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations G., and Randell, D

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Core Building Blocks: Next Gen Geo Spatial GPT Application

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations and Goodchild, M

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations R., Hu, Y., Yang, J.-A., McKenzie, G., Ju, Y., Gong, L., Adams, B., and Yan, B

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Foundation Models for Generalist Geospatial Artificial Intelligence

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations A., and Hitzler, P

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations and Gao, S

Reference 59

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This paper cites B., Abdelmoty, A.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations B., Abdelmoty, A

Reference 60

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations u chemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., G \

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This paper cites Segment Anything.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Segment Anything

Reference 62

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations S., Reid, M., Matsuo, Y., and Iwasawa, Y

Reference 63

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Reference 64

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations u ttler, H., Lewis, M., Yih, W.-t., Rockt \

Reference 65

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This paper cites and Ning, H.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations and Ning, H

Reference 67

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations An efficient framework for learning sentence representations

Reference 68

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Reference 69

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations D., and Jones, C

Reference 70

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 77

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Reference 78

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Reference 79

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This paper cites SGPT: GPT Sentence Embeddings for Semantic Search.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations SGPT: GPT Sentence Embeddings for Semantic Search

Reference 80

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Text and Code Embeddings by Contrastive Pre-Training

Reference 81

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Introducing ChatGPT

Reference 82

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Reference 84

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Reference 85

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This paper cites A., Cui, Z., and Cohn, A.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations A., Cui, Z., and Cohn, A

Reference 86

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Reference 87

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Observation 0893caf6-b289-49ad-a2df-330899174c65 · outbound

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 88

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations M., Egenhofer, M

Reference 89

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Observation 9053d9c4-2052-4d7b-98e5-0139e264bc2b · outbound

This paper cites and Nebel, B.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations and Nebel, B

Reference 90

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Observation cf55c247-6911-4b2b-a3a0-d03ffedb4d0d · outbound

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations and Urrutia, J

Reference 91

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Observation ff7147b0-c7d2-4a99-b506-38cded96df67 · outbound

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Reference 92

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Reference 93

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Unresolved cited work

Reference 94

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Observation 5413fe58-bd9b-45f8-90ca-480bf5d2d60c · outbound

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Observation e8d59768-dd14-4cca-9fa4-44beba69acd4 · outbound

This paper cites and Xu, J.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations and Xu, J

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:20.368050Z

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Observation 156a1312-6f1a-4de2-b2fa-d11ac4dadd0b · outbound

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Unresolved cited work

Reference 97

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Observation 535a0452-68c5-46b2-9209-fdfc5e401a24 · outbound

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations LaMDA: Language Models for Dialog Applications

Reference 98

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This paper cites A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systems.

Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systems

Reference 99

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8580e36e-01db-456f-b10c-4b3a6a46faac · outbound

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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations O., Klippel, A., and Baldwin, T

Reference 100

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