--- license: mit task_categories: - image-text-to-text language: - en pretty_name: TimeSpot size_categories: - 1K11B)** | | | | | | | | | | | | | | 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} } ```