Geode: A Zero-shot Geospatial Question-Answering Agent with Explicit Reasoning and Precise Spatio-Temporal Retrieval
<|reference_start|>Geode: A Zero-shot Geospatial Question-Answering Agent with Explicit Reasoning and Precise Spatio-Temporal Retrieval: 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.<|reference_end|>
@article{gupta2024geode:,
    title={Geode: A Zero-shot Geospatial Question-Answering Agent with Explicit
  Reasoning and Precise Spatio-Temporal Retrieval},
    author={Devashish Vikas Gupta, Azeez Syed Ali Ishaqui, Divya Kiran Kadiyala},
    journal={arXiv preprint arXiv:2407.11014},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11014},
    primaryClass={cs.CL cs.AI cs.MA}
}