# Geode: A Zero-shot Geospatial Question-Answering Agent with Explicit Reasoning and Precise Spatio-Temporal Retrieval

arXiv: https://arxiv.org/abs/2407.11014 · HTML: https://mlsanity.com/paper/2407.11014/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 2 of 2)

- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Foundation Models for Generalist Geospatial Artificial Intelligence](https://mlsanity.com/paper/2310.18660/) (2023)

## Built on by (most cited first, 5 of 5)

- [Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey](https://mlsanity.com/paper/2503.13502/) (2025)
- [ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing](https://mlsanity.com/paper/2410.12376/) (2024)
- [Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems](https://mlsanity.com/paper/2601.01891/) (2026)
- [Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment](https://mlsanity.com/paper/2511.19557/) (2025)
- [TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route](https://mlsanity.com/paper/2509.18173/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have shown promising results in learning and contextualizing information from different forms of data. Recent advancements in foundational models, particularly those employing self-attention mechanisms, have significantly enhanced our ability to comprehend the semantics of diverse data types. One such area that could highly benefit from multi-modality is in understanding geospatial data, which inherently has multiple modalities. However, current Natural Language Processing (NLP) mechanisms struggle to effectively address geospatial queries. Existing pre-trained LLMs are inadequately equipped to meet the unique demands of geospatial data, lacking the ability to retrieve precise spatio-temporal data in real-time, thus leading to significantly reduced accuracy in answering complex geospatial queries. To address these limitations, we introduce Geode--a pioneering system designed to tackle zero-shot geospatial question-answering tasks with high precision using spatio-temporal data retrieval. Our approach represents a significant improvement in addressing the limitations of current LLM models, demonstrating remarkable improvement in geospatial question-answering abilities compared to existing state-of-the-art pre-trained models.