arXiv Computer Vision
Aug 27

GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction

GTPred is a new benchmark for geo‑temporal prediction that evaluates multi‑modal large language models (MLLMs) on 370 images taken across 120 years worldwide. It assesses predictions by matching both the year and a hierarchical location sequence, and includes annotated reasoning chains to test intermediate reasoning. Experiments on 15 MLLMs show that while visual perception is strong, models still lack world knowledge and geo‑temporal reasoning, and that adding temporal data improves location inference.

By Jinnao Li, Tingzhu Chen, Changbo Wang
arXiv AI
Sep 7

IPGeoAI: Transformer-Based Geolocation with LLM Semantic Fusion

IPGeoAI is a transformer-based deep learning model that transforms IP geolocation from a static lookup into a sequential modeling task. It captures hierarchical dependencies in IP subnet structures and resolves geographic ambiguity by fusing unstructured semantic context extracted from Autonomous System descriptions via a zero‑shot LLM feature extraction pipeline. Extensive offline evaluation on a proprietary dataset of 200,000 cities shows IPGeoAI outperforms a leading vendor, achieving a 6% improvement in city‑level accuracy and 100% traffic coverage, while online production tests demonstrate a statistically significant +0.35% improvement in first‑tier downstream use‑case metrics.

By Avinash Kadimisetty, Andy Jinqing Yu, Philip Favaloro, Wenlong Liu, Xiaolu Xiong