mlsanityData to 14 Oct 2026

OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence

arXiv 2503.16326 ↗First posted 20 Mar 2025cs.AIcited by 7

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types such as text, images, and spatial information. In this paper, we explore the potential of multimodal LLMs (MLLM) for geospatial artificial intelligence (GeoAI), a field that leverages spatial data to address challenges in domains including Geospatial Semantics, Health Geography, Urban Geography, Urban Perception, and Remote Sensing. We propose a MLLM (OmniGeo) tailored to geospatial applications, capable of processing and analyzing heterogeneous data sources, including satellite imagery, geospatial metadata, and textual descriptions. By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems. Results demonstrate that our model outperforms task-specific models and existing LLMs on diverse geospatial tasks, effectively addressing the multimodality nature while achieving competitive results on the zero-shot geospatial tasks. Our code will be released after publication.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 22 of its 70 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

22 of 70 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis29 Mar
  2. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensing24 Nov

Other references · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  5. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,876
  6. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,565
  7. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,305
  8. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,173
  9. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,050
  10. 2016AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classificationcited by 347
  11. 2022ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answeringcited by 196
  12. 2022Advancing Plain Vision Transformer Towards Remote Sensing Foundation Modelcited by 149
8 more
  1. 2024Large Language Models are Geographically Biasedcited by 93
  2. 2023LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generationcited by 57
  3. 2023Large Language Models in Education: Vision and Opportunitiescited by 54
  4. 2020TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learningcited by 47
  5. 2022Geographic and Geopolitical Biases of Language Modelscited by 29
  6. 2023Learning to Relate to Previous Turns in Conversational Searchcited by 26
  7. 2024Leveraging Large Language Models for Learning Complex Legal Concepts through Storytellingcited by 23
  8. 2020How do people describe locations during a natural disaster: an analysis of tweets from Hurricane Harveycited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2026Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systemscited by 6
  2. 2026When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Lifecited by 3
  3. 2025The Role of Open-Source LLMs in Shaping the Future of GeoAIcited by 2
  4. 2026Agentic AI for Remote Sensing: Technical Challenges and Research Directionscited by 2
  5. 2025WXImpactBench: A Disruptive Weather Impact Understanding Benchmark for Evaluating Large Language Modelscited by 0
  6. 2025From Pixels to Urban Policy-Intelligence: Recovering Legacy Effects of Redlining with a Multimodal LLMcited by 0
  7. 2025WeatherArchive-Bench: Benchmarking Retrieval-Augmented Reasoning for Historical Weather Archivescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs
  2. 2023Are Large Language Models Geospatially Knowledgeable?
  3. 2026GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
  4. 2025UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding
  5. 2025Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses
  6. 2025SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence
  7. 2023AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Ten Modalities via Language as a Reference Framework
  8. 2025Geospatial Mechanistic Interpretability of Large Language Models