  Navigating drones through natural language commands remains challenging due
to the dearth of accessible multi-modal datasets and the stringent precision
requirements for aligning visual and textual data. To address this pressing
need, we introduce GeoText-1652, a new natural language-guided geo-localization
benchmark. This dataset is systematically constructed through an interactive
human-computer process leveraging Large Language Model (LLM) driven annotation
techniques in conjunction with pre-trained vision models. GeoText-1652 extends
the established University-1652 image dataset with spatial-aware text
annotations, thereby establishing one-to-one correspondences between image,
text, and bounding box elements. We further introduce a new optimization
objective to leverage fine-grained spatial associations, called blending
spatial matching, for region-level spatial relation matching. Extensive
experiments reveal that our approach maintains a competitive recall rate
comparing other prevailing cross-modality methods. This underscores the
promising potential of our approach in elevating drone control and navigation
through the seamless integration of natural language commands in real-world
scenarios.
