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.
arXiv:2506.16633v3 Announce Type: replace-cross Abstract: Multimodal reasoning is a process of understanding, integrating and inferring information across different data modalities. It has recently a...
arXiv:2606.08918v2 Announce Type: replace Abstract: Worldwide image geo-localization aims to determine where on Earth a single image was captured. However, visually similar scenes may lie thousands o...
arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.
arXiv:2608.29880v1 Announce Type: new Abstract: Open-world geo-localization requires models to reason over ambiguous visual cues through multi-step reasoning and external knowledge grounding. While r...
arXiv:2608.29483v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) perform well above the human baseline in image geolocalization, a task critically important in disaster response...
arXiv:2606. 07172v1 Announce Type: cross Abstract: Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems for tasks such as image geolocation and spatial reasoning.
Multimodal large language models (MLLMs) have advanced image geolocalization mainly by improving how they reason about geographic cues. How that reasoning isdecoded into coordinates, however, has lagged behind.
arXiv:2512.02697v4 Announce Type: replace Abstract: Cross-view geo-localization infers a location by retrieving geo-tagged reference images matching a query image. However, the traditional satellite-...
arXiv:2608. 13344v1 Announce Type: new Abstract: Long-horizon Earth observation reasoning requires models to organize multi-stage geographic evolution, localize spatial changes, detect temporal anomalies, and infer future from extended image sequences.
arXiv:2608. 03826v1 Announce Type: cross Abstract: Geospatial and urban applications increasingly require models to compare heterogeneous evidence across street-view imagery, remote-sensing observations, text descriptions, region proposals, and temporal change cues.
UniGeo is a multimodal large language model designed for text-guided drone geo‑localization, enabling the identification of target regions in large image galleries from natural‑language descriptions. It integrates geo‑semantic understanding, cross‑view semantic generation, and candidate‑level verification within a shared vision‑language framework, establishing stable correspondences among local scene elements, spatial relations, and language. A multi‑stage training strategy progressively refines geo‑semantic learning, cross‑view mapping, and fine‑grained verification, yielding significant performance gains on GeoText‑1652, with R@10 and mAP improvements of 13.59 and 2.83 percentage points respectively.
Although multimodal large language models (MLLMs) have achieved remarkable progress, understanding 3D spatial relationships from 2D images remains a critical challenge. Existing methods primarily rely on symbolic text tokens, which inherently lack the fidelity to represent continuous geometric information.