Into the Unknown: Generating Geospatial Descriptions for New Environments
<|reference_start|>Into the Unknown: Generating Geospatial Descriptions for New Environments: Similar to vision-and-language navigation (VLN) tasks that focus on bridging the gap between vision and language for embodied navigation, the new Rendezvous (RVS) task requires reasoning over allocentric spatial relationships (independent of the observer's viewpoint) using non-sequential navigation instructions and maps. However, performance substantially drops in new environments with no training data. Using opensource descriptions paired with coordinates (e.g., Wikipedia) provides training data but suffers from limited spatially-oriented text resulting in low geolocation resolution. We propose a large-scale augmentation method for generating high-quality synthetic data for new environments using readily available geospatial data. Our method constructs a grounded knowledge-graph, capturing entity relationships. Sampled entities and relations (`shop north of school') generate navigation instructions via (i) generating numerous templates using context-free grammar (CFG) to embed specific entities and relations; (ii) feeding the entities and relation into a large language model (LLM) for instruction generation. A comprehensive evaluation on RVS, showed that our approach improves the 100-meter accuracy by 45.83% on unseen environments. Furthermore, we demonstrate that models trained with CFG-based augmentation achieve superior performance compared with those trained with LLM-based augmentation, both in unseen and seen environments. These findings suggest that the potential advantages of explicitly structuring spatial information for text-based geospatial reasoning in previously unknown, can unlock data-scarce scenarios.<|reference_end|>
@article{paz-argaman2024into,
    title={Into the Unknown: Generating Geospatial Descriptions for New
  Environments},
    author={Tzuf Paz-Argaman, John Palowitch, Sayali Kulkarni, Reut Tsarfaty, and
  Jason Baldridge},
    journal={ACL 2024 Findings},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19967},
    primaryClass={cs.CL cs.AI}
}