README.md

---
license: mit
task_categories:
- image-text-to-text
language:
- en
pretty_name: TimeSpot
size_categories:
- 1K<n<10K
---



# TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settings

**Azmine Toushik Wasi\*, Shahriyar Zaman Ridoy\*, Koushik Ahamed Tonmoy, Kinga Tshering, S. M. Muhtasimul Hasan, Wahid Faisal, Tasnim Mohiuddin, Md Rizwan Parvez**

Computational Intelligence and Operations Laboratory (CIOL) | Shahjalal University of Science and Technology (SUST) | North South University (NSU) | Qatar Computing Research Institute (QCRI)

\*Equal Contribution

**Correspondence:** shahriyar.zaman01@gmail.com, mparvez@hbku.edu.qa

Accepted to **The Forty-Third International Conference on Machine Learning (ICML 2026)**

[OpenReview](https://openreview.net/forum?id=XQlUqVCHJd) | [arXiv](https://arxiv.org/abs/2603.06687)

---

**Tags:** 1,455 VQA pairs | Geographic Reasoning | Temporal Reasoning | Rubric-based Open-ended Evaluation

---

## Overview

Geo-temporal understanding -- the ability to infer location, time, and contextual properties from visual input -- is crucial for applications such as disaster management, traffic planning, navigation, world modeling, and geography education, yet current vision-language models (VLMs) struggle with temporal reasoning and physically grounded spatial cues. To address this, we introduce **TimeSpot**, a benchmark of 1,455 ground-level images from 80 countries that evaluates structured prediction of temporal (season, month, time of day, daylight phase) and geographic attributes (continent, country, climate zone, environment type, latitude-longitude) under real-world uncertainty, revealing that existing VLMs perform poorly and highlighting the need for new approaches to robust, physically grounded geo-temporal reasoning.

---

## Benchmark Structure and Task Categories

| Axis | Field | Categories (top shown) |
|---|---|---|
| Temporal | Season | Summer (400), Fall (399), Spring (335), Winter (321) |
| Temporal | Daylight phase | Afternoon (584), Night (287), Sunset (210), Morning (203), Midday (124), Sunrise (47) |
| Temporal | Month | 12 months represented; top: August (163), September (146), July (145), March (131) |
| Temporal | Hemispheric tag | Northern Hemisphere Summer (703), Northern Hemisphere Winter (615), Southern Hemisphere Winter (81), Southern Hemisphere Summer (56) |
| Temporal | Time coverage | Day (1182), Night (273) |
| Temporal | Hour range | Full 0-23; densest 08-18 |
| Geography | Continents | Asia (529), Europe (430), North America (326), South America (170) |
| Geography | Countries | 80 unique; top: USA (196), Russia (97), Japan (67), Italy (65), China (58) |
| Geography | Climate | Temperate (C) (582), Continental (D) (396), Tropical (A) (274), Arid (B) (180), Polar (E) (23) |
| Geography | Environment type | Urban (648), Rural (202), Mountain (193), Coastal (181), Suburban (118), Desert (113) |
| Geography | Lat/Lon span | lat -54.80 to 71.96, lon -173.24 to 170.31 |
| Cues | Primary temporal cues | Sun/Shadows (573), Vegetation (325), Other (289), Snow/Ice (122), Human Clothing (95), Agricultural Activity (51) |
| Cues | Primary geolocation cues | Architecture (355), Natural Biome (354), Topography (Mountains/Coast) (295), Road Signage/Language (236), Vehicles (156), Other (58) |

---

## Results

Evaluation results for TimeSpot benchmark across multiple vision-language models, including both proprietary and open-source variants.

### Multiple Choice Evaluation Accuracy (%) on TimeSpot-MCQ

| Model | Cnt. | Cou. | Clim. | Env. | Lat.° | Long.° | Dist.(km) | Season | Month | Time (Ac.) | Time (MAE) | DLP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| **Proprietary Models** | | | | | | | | | | | | |
| GPT-4o-mini | 82.68 | 49.14 | 50.93 | 57.87 | 12.40 | 24.70 | 2827.07 | 47.08 | 22.34 | 30.32 | 3:54 | 31.55 |
| GPT-5-mini | 83.62 | 68.27 | 72.47 | 60.01 | 4.72 | 15.64 | 1389.79 | 58.43 | 34.27 | 21.55 | 4:10 | 44.60 |
| Gemini-2.0-Flash | 89.07 | 76.91 | 68.52 | 60.96 | 3.32 | 11.23 | 994.30 | 49.76 | 22.89 | 27.35 | 4:22 | 30.24 |
| Gemini-2.5-Flash | 90.51 | 77.25 | 71.34 | 64.32 | 3.05 | 10.38 | 917.61 | 50.92 | 23.91 | 25.15 | 3:56 | 41.92 |
| Claude 3.5 Haiku | 77.25 | 55.53 | 61.86 | 55.74 | 6.85 | 27.51 | 2269.86 | 44.12 | 19.04 | 23.09 | 4:14 | 30.93 |
| Mistral Medium 3.1 | 75.88 | 52.85 | 66.67 | 61.72 | 6.37 | 22.62 | 2045.61 | 36.84 | 15.26 | 30.73 | 3:36 | 36.01 |
| **Open-Source Models (<=11B)** | | | | | | | | | | | | |
| InternVL3.5-1B | 43.02 | 14.15 | 32.50 | 53.54 | 44.68 | 4378.92 | 7700.42 | 30.65 | 3.78 | 7.77 | 11:45 | 35.80 |
| InternVL3.5-2B | 60.00 | 29.41 | 51.82 | 57.80 | 13.11 | 43.71 | 3959.29 | 36.29 | 5.70 | 27.80 | 4:30 | 24.05 |
| Qwen-VL2.5-3B-Instruct | 22.40 | 13.47 | 18.83 | 44.53 | 16.18 | 130.98 | 8231.18 | 27.49 | 9.96 | 22.06 | 4:34 | 8.52 |
| InternVL3.5-4B | 60.79 | 30.12 | 57.77 | 56.74 | 15.34 | 44.15 | 4236.77 | 37.55 | 12.03 | 29.33 | 4:10 | 41.61 |
| Qwen-VL2.5-7B-Instruct | 85.70 | 73.96 | 70.86 | 75.21 | 32.94 | 21.46 | 4719.95 | 61.46 | 44.96 | 25.68 | 3:47 | 64.09 |
| Llama-3.2-11B-Vision-Instruct | 74.22 | 55.73 | 57.12 | 57.61 | 5.85 | 26.57 | 2072.35 | 43.50 | 16.68 | 25.74 | 4:18 | 43.57 |
| **Open-Source Models (>11B)** | | | | | | | | | | | | |
| Gemma-3-27B-it | 79.59 | 54.02 | 60.41 | 53.12 | 6.83 | 23.58 | 2063.93 | 44.81 | 17.11 | 26.34 | 4:28 | 30.86 |
| Qwen-VL2.5-32B-Instruct | 78.56 | 57.11 | 62.95 | 60.82 | 6.27 | 24.02 | 2010.12 | 44.81 | 17.86 | 31.10 | 3:44 | 44.54 |
| Internvl3-78b | 77.46 | 53.26 | 71.61 | 61.37 | 7.42 | 23.63 | 2180.29 | 45.91 | 16.43 | 29.64 | 4:07 | 34.91 |
| Qwen-VL2.5-72B-Instruct | 77.94 | 58.28 | 65.15 | 58.14 | 5.11 | 19.33 | 1711.42 | 44.47 | 18.28 | 28.71 | 4:00 | 36.84 |
| Llama-3.2-90B-Vision-Instruct | 78.08 | 53.54 | 63.85 | 59.04 | 7.05 | 26.79 | 2284.85 | 45.15 | 19.72 | 23.33 | 4:29 | 33.88 |
| GLM-4.5V-106B-MoE | 85.32 | 69.68 | 62.09 | 62.51 | 4.23 | 14.09 | 1280.87 | 57.55 | 36.04 | 30.51 | 4:09 | 42.45 |
| **Reasoning Models** | | | | | | | | | | | | |
| o4-mini | 82.39 | 71.82 | 73.06 | 66.64 | 4.85 | 15.39 | 1359.96 | 65.81 | 48.20 | 23.91 | 4:04 | 51.79 |
| Gemini-2-Flash-Thinking | 88.66 | 76.22 | 66.73 | 59.93 | 3.44 | 11.70 | 1024.14 | 49.28 | 22.68 | 27.49 | 4:22 | 29.76 |
| Gemini-2.5-Flash-Thinking | 90.31 | 77.59 | 70.86 | 64.47 | 3.04 | 9.85 | 892.54 | 51.13 | 24.26 | 22.19 | 4:03 | 36.56 |
| Kimi-VL-A3B-Thinking-2506 | 58.90 | 40.69 | 54.84 | 59.31 | 16.00 | 39.83 | 4034.15 | 39.72 | 12.65 | 32.23 | 4:18 | 25.70 |
| GLM-4.1V-9B-Thinking | 84.44 | 68.34 | 70.19 | 68.54 | 4.34 | 23.01 | 1788.77 | 58.02 | 38.88 | 33.74 | 3:58 | 47.76 |

**Abbreviations:** Cnt. = Continent, Cou. = Country, Clim. = Climate Zone, Env. = Environment Type, Lat.° = Latitude in degrees, Long.° = Longitude in degrees, Dist.(km) (MD) = Mean distance from actual location in kilometers, DLP = Daylight phase. Time (Ac.) = accuracy within a 1-hour window; Time (MAE) = mean error in HH:MM format.

---

## Key Takeaways

### Overall Findings

- **Temporal Inference is a Major Bottleneck:** Time-of-day accuracy is extremely low across all models (22-34%), peaking at 33.74% (GLM-4.1V-9B-Thinking), with MAE approximately 4 hours.
- **Geodesic Disconnect:** High coarse-grained localization often coexists with large metric and temporal errors. Top models (Gemini-2.5-Flash-Thinking) reach ~77.59% country accuracy but median geodesic error = 892.54 km.
- **Proprietary Models Lead:** Closed-source models outperform open-source in spatial reasoning, metric localization, and calibration.
- **Open-Source Variance:** GLM-4.5V-106B-MoE attains competitive country accuracy (69.68%) but weaker metric grounding; Qwen-VL2.5-7B fails in coordinate estimation (MD 4719 km).
- **Reasoning-Augmented Models Excel:** Gemini-2.5-Flash-Thinking and o4-mini outperform base counterparts across geolocation and temporal tasks, demonstrating multi-step inference benefits.

### Geo-Temporal Consistency and Confidence

- Even strong models exhibit physical consistency violations (phase-time mismatches, season-month inconsistencies, spatial tail errors).
- Calibration failures: all models show overconfidence on fine-grained temporal tasks (high ECE), signaling low reliability under ambiguous cues.

### Error Analysis

- **Seasonal Collapse:** Summer is reliably predicted; Autumn collapses to 0% accuracy, showing models rely on static color cues rather than true phenology.
- **Daylight Phase Blindspots:** Midday/Afternoon are easier; Night and Sunrise/Sunset accuracy remains <35%, often confusing dawn and dusk.
- **Time Anchoring:** Predictions collapse to round-hour anchors (09:00, 12:00, 18:00), indicating failure to compute continuous solar geometry.
- **Spatial Drift and Biases:** Near-miss country errors (e.g., Bangladesh vs India); default to Temperate/Urban, poor performance in Polar, Arid, and Continental climates.

### Qualitative Error Analysis

- **Low-Light Breakdowns:** Night/twilight scenes cause drastic drift; absence of solar cues leads to round-hour guesses.
- **Urban Occlusion:** Street canyons compress apparent sun elevation, producing late time predictions.
- **Missing Shorelines:** Coastal/elevation cues systematically ignored; coordinates shift inland.
- **Shortcut Exploitation:** Models rely on human-centric cues (architecture/signage), failing to use environmental cues (biome, topography) for precise geolocation or temporal estimation.

### Benchmark Design and Evaluation Methods

**Dataset Construction and Protocol:**
- 1,455 ground-level images from 80 countries; landmarks and heavy text removed to force reliance on physical cues (illumination, shadows, sky, vegetation, materials).
- Structured 9-field prediction: 4 temporal (season, month, local time, daylight phase) + 5 geographic (continent, country, climate, environment type, lat/lon).
- Annotations verified with metadata (timestamps, GPS, solar ephemerides) and cross-checked manually for physical validity.

**Evaluation Metrics:**
- Geographic: Top-1 categorical accuracy for region/climate; MAE for lat/lon; mean/median great-circle distance (MD) in km.
- Temporal: Top-1 accuracy, +-1-hour window accuracy, HH:MM MAE for local time.
- LLM-as-a-Judge: Semantic alignment for synonyms (USA vs United States, Fall vs Autumn) handled by Gemini-2.5-Flash judge model.

**Performance Improvement Approaches:**
- **Supervised Fine-Tuning (SFT):** Fine-tuning Qwen-VL2.5-3B-Instruct on 40% of TimeSpot improved categorical geo-semantic accuracy (Country: 14.2% -> 19.2%), but time predictions remain unstable.
- **Recommendations:** Future models should include physical inductive biases (latitude/solar conditioning), constraint-aware reasoning (phase/time/longitude matching), and augmentations for extreme lighting and polar environments.

---

## Citation

```bibtex
@inproceedings{
wasi2026timespot,
title={TimeSpot: Benchmarking Geo-Temporal Understanding in Vision{\textendash}Language Models in Real-World Settings},
author={Azmine Toushik Wasi and Shahriyar Zaman Ridoy and Koushik Ahamed Tonmoy and Kinga Tshering and S. M. Muhtasimul Hasan and Wahid Faisal and Tasnim Mohiuddin and Md Rizwan Parvez},
booktitle={Forty-third International Conference on Machine Learning (ICML 2026)},
year={2026},
url={https://openreview.net/forum?id=XQlUqVCHJd}
}
```