TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settings
Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, underpins applications such as disaster management, traffic planning, embodied navigation, world modeling, and geography education. Although recent vision-language models (VLMs) have advanced image geo-localization using cues like landmarks and road signs, their ability to reason about temporal signals and physically grounded spatial cues remains limited. To address this gap, we introduce TimeSpot, a benchmark for evaluating real-world geo-temporal reasoning in VLMs. TimeSpot comprises 1,455 ground-level images from 80 countries and requires structured prediction of temporal...
| Authors | Azmine Toushik Wasi, Shahriyar Zaman Ridoy, Koushik Ahamed Tonmoy et al. |
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| Primary category | cs.CV |
| Published | 4 March 2026 |
| Last updated at source | 25 May 2026 |
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