Evaluating Expert Calibration with Uncertainty Visualization Schemes in Climate Projection Maps
Keywords:
Climate Projection, Uncertainty Visualization, Graph Analysis, Expert Calibration, Computer VisionAbstract
The accurate interpretation of climate projection maps is fundamentally contingent upon the capacity of human experts to calibrate their internal confidence regarding spatial uncertainties. When dealing with complex environmental data, overconfidence or underconfidence can severely skew policy and mitigation strategies. This paper presents a comprehensive evaluation of expert calibration through the novel application of graph analysis applied to human interaction and visual attention data. By systematically assessing multiple uncertainty visualization schemes, including bivariate choropleth mapping, value-suppressing uncertainty palettes, and ensemble glyphs, this research isolates the specific graphical variables that facilitate optimal cognitive calibration. The methodology translates sequential visual attention and interactive queries into directed graph networks, analyzing structural metrics such as node centrality, modularity, and path efficiency to quantify cognitive behavior. These topological graph features provide an objective basis for comparing how well different visualization paradigms align subjective expert confidence with objective data reliability. The findings indicate that integrated visualization schemes significantly alter the graph topology of expert attention, promoting more distributed network structures that correlate with higher calibration accuracy. The implications of this study offer a robust framework for designing climate data interfaces that explicitly support calibrated decision-making processes in high-stakes environmental planning.References
1. Zheng, H.; Reaño, C.; Castillo, A.; Igual, A.; Igual, C. Cloud Computing Alternatives for Distributing a Real-world AI-based Computer Vision Application. In Proceedings of the 24th International Symposium on Parallel and Distributed Computing (ISPDC), Rennes, France, 8–11 July 2025; pp. 1–5.
2. Fu, K.; Peng, J.; Zhang, H.; Wang, X.; Jiang, F. Image super-resolution based on generative adversarial networks: A brief review. Comput. Mater. Contin. 2020, 64, 1977–1997.
3. Liu, Z.; Luo, P.; Wang, X.; Tang, X. Deep Learning Face Attributes in the Wild. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015; pp. 3730–3738.
4. Toet, A. The TNO multiband image data collection. Data Brief 2017, 15, 249.
5. Pereira, L.M.; Salazar, A.; Vergara, L. A comparative study on recent automatic data fusion methods. Computers 2024, 13, 2006.
6. Long, S.; Ruan, J.; Zhang, W.; He, X.; Wu, W.; Yao, C. Textsnake: A flexible representation for detecting text of arbitrary shapes. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 20–36.
7. Zhang, X.; Zhai, H.; Liu, J.; Wang, Z.; Sun, H. Real-time infrared and visible image fusion network using adaptive pixel weighting strategy. Inf. Fusion 2023, 99, 101863.
8. Jiang, C.; Ren, H.; Yang, H.; Huo, H.; Zhu, P.; Yao, Z.; Li, J.; Sun, M.; Yang, S. M2FNet: Multi-modal fusion network for object detection from visible and thermal infrared images. Int. J. Appl. Earth Obs. Geoinf. 2024, 130, 103918.
9. Liu, Z.; Yin, H.; Fang, B.; Chai, Y. A novel fusion scheme for visible and infrared images based on compressive sensing. Opt. Commun. 2015, 335, 168–177.
10. Altamimi, A.; Ben Youssef, B. A systematic review of hardware-accelerated compression of remotely sensed hyperspectral images. Sensors 2021, 22, 263.
11. Azam, N.; Yazid, H.; Rahim, S. Super resolution with interpolation-based method: A review. IJRAR-Int. J. Res. Anal. Rev. (IJRAR) 2022, 9, 168–174.
12. Liu, F.; Liu, Y.; Kong, Y.; Xu, K.; Zhang, L.; Yin, B.; Hancke, G.; Lau, R. Referring image segmentation using text supervision. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 2–3 October 2023; pp. 22124–22134.
13. Meng, F.; Hong, A.; Tang, H.; Tong, G. FQDNet: A fusion-enhanced quad-head network for RGB-infrared object detection. Remote Sens. 2025, 17, 1095.
14. Ma, J.; Yu, W.; Liang, P.; Li, C.; Jiang, J. FusionGAN: A generative adversarial network for infrared and visible image fusion. Inf. Fusion 2019, 48, 11–26.
15. Brooks, I.R.; Iyengar, S.S. Real-time distributed sensor fusion for time-critical sensor readings. Opt. Eng. 1997, 36, 767–779.
16. Vidhushavarshini, S.; Dhayanithi, J.; Vinayakumar, R.; Vinodhini, V.; Pradeep, R. A Comparative Study on Thyroid Nodule Classification using Transfer Learning Methods. Open Bioinform. J. 2024, 17, e18750362305982.
17. Gao, H.; Cheng, B.; Wang, J.; Li, K.; Zhao, J.; Li, D. Object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment. IEEE Trans. Ind. Inform. 2018, 14, 4224–4231.
18. Zhao, T.; Yuan, M.; Jiang, F.; Wang, N.; Wei, X. Removal and selection: Improving RGB-infrared object detection via coarse-to-fine fusion. arXiv 2024, arXiv:2401.10731.
19. Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017.
20. He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017.
21. Krishnan, B.S.; Jones, L.R.; Elmore, J.A.; Samiappan, S.; Evans, K.O.; Pfeiffer, M.B.; Blackwell, B.F.; Iglay, R.B. Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys. Sci. Rep. 2023, 13, 10385.
22. Huang, Y.; Yang, J.; Liao, P.; Pan, J. Fusion of facial expressions and EEG for multimodal emotion recognition. Comput. Intell. Neurosci. 2017, 2017, 2107451.
23. Merkel, D. Docker: Lightweight Linux Containers for Consistent Development and Deployment. Linux J. 2014, 239, 76–91.
24. Wen, W.; Liu, G.; Cheng, N.; Wei, J.; Shangguan, P.; Huang, W. Emotion recognition based on multi-variant correlation of physiological signals. IEEE Trans. Affect. Comput. 2014, 5, 126–140.
25. Ma, J.; Ma, Y.; Li, C. Infrared and visible image fusion methods and applications: A survey. Inf. Fusion 2019, 45, 153–178.
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