Journal of Artificial Intelligence and Capsule Networks
IRO Journals
Volume 8 — Issue 2 — June 2026

Glacier Growth Monitoring Using YOLOv8 and Feature-Based Classification

https://doi.org/10.36548/jaicn.2026.2.001
Pages: 84-96
  • Pratyush Jha , Pratyush Jha
    Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
    Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
  • Arun A. , Arun A.
    Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
    Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
  • Srijan Srivastava Srijan Srivastava
    Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
    Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
  • &  Tanishq Gandhi Tanishq Gandhi
    Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
    Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
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How to Cite

Jha, Pratyush, Arun A., Srijan Srivastava, and Tanishq Gandhi. 2026. “Glacier Growth Monitoring Using YOLOv8 and Feature-Based Classification”. Journal of Artificial Intelligence and Capsule Networks 8 (2): 84-96. https://doi.org/10.36548/jaicn.2026.2.001.
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Keywords

— Glacier Growth Monitoring
— Satellite Imagery
— Deep Learning Segmentation
— Ensemble Machine Learning
— YOLOv8
Published: 07-04-2026

Abstract

The rising threat of climate change has raised the need for constant glacier surveillance, since their fast growth may pose an immediate danger to the environment and cause disasters. However, conventional approaches to glaciers' monitoring are dependent on challenging and irregular manual analysis of satellite images. In this paper, a new combined methodology for automated glacier growth monitoring and risk assessment based on satellite imagery is introduced. The system combines a glacier segmentation model based on deep learning with a classification algorithm that utilizes ensemble machine learning to predict a risk level. The YOLOv8 segmentation network is used to identify the glacier outlines from a set of satellite images and generate masks to be further analyzed. The masks are used to extract features representing the glacier area, shape, and growth pattern, which are later used by a random forest and an extreme gradient boosting algorithm to determine the risk level by applying the soft-voting method. A real glacier lake dataset is used to test the system and demonstrate its performance and accuracy in identifying risky and non-risky growth.

References

  1. Maffezzoli, Niccolò, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon. "A gradient-boosted tree framework to model the ice thickness of the World's glaciers (IceBoost v1)." EGUsphere 2024 (2024): 1-27.
  2. Vashistha, Anushka, Ajay Dashora, and Afroz Ahmad Shah. "Machine Learning-Based probabilistic prediction of glacial lake formation using erosional and topographic features." Scientific Reports 15, no. 1 (2025): 38673.
  3. Robbins, Edmund, Robert D. Breininger, Maxwell Jiang, Michelle Madera, Ryan T. White, and Nezamoddin N. Kachouie. "Segmentation of Glacier Area Using U-Net through Landsat Satellite Imagery for Quantification of Glacier Recession and Its Impact on Marine Systems." Journal of Marine Science and Engineering 12, no. 10 (2024): 1788.
  4. Peng, Yanfei, Jiang He, Qiangqiang Yuan, Shouxing Wang, Xinde Chu, and Liangpei Zhang. "Automated glacier extraction using a Transformer based deep learning approach from multi-sensor remote sensing imagery." ISPRS Journal of Photogrammetry and Remote Sensing 202 (2023): 303-313.
  5. Maslov, Konstantin A., Claudio Persello, Thomas Schellenberger, and Alfred Stein. "Globally scalable glacier mapping by deep learning matches expert delineation accuracy." Nature communications 16, no. 1 (2025): 43.
  6. Shankar, Siddharth, Leigh A. Stearns, and C. J. Van Der Veen. "Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM)." Journal of Glaciology 70 (2024): e4.
  7. Kaur, Sukhmeen, and Sunil Kumar. "Ai for anomaly detection in glacier movement identifying climate change effect using machine learning." In EPJ Web of Conferences, vol. 328, p. 01025. EDP Sciences, 2025.
  8. Danish, Muhammad, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Kartik Kuckreja, Fahad Shahbaz Khan, Paolo Fraccaro, Alexandre Lacoste, and Salman Khan. "Geobench-vlm: Benchmarking vision-language models for geospatial tasks." In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025, 7132-7142.
  9. Nurakynov, Serik, Nurmakhambet Sydyk, Zhaksybek Baygurin, and Larissa Balakay. "Advancements in Remote Sensing for Monitoring and Risk Assessment of Glacial Lake Outburst Floods." Geosciences 15, no. 6 (2025): 211.
  10. LiChen Liu, XiangYu Song, HuanSheng Song, ShiJie Sun, Xian-Feng Han. 2024. "Yolo-3DMM for Simultaneous Multiple Object Detection and Tracking in Traffic Scenarios." IEEE Transactions on Intelligent Transportation Systems 25 (8): 9567–9473.
  11. X. Teng, J. Xu, X. Cui, G. Shi, Z. Hu, Q. Gu, B. Yu, J. Wu, Y. Huang. 2025. "Mapping Antarctic Blue Ice Areas With Sentinel-2A/B Images and LightGBM Model." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 18 (18): 2151–1535.
  12. Choudhary, Subhash Kumar, Shivani Kumari, and Muzafar Mehraj Misgar. "Predicting Glacier Ice Melt with Machine Learning to Address Climate Change." (2025).
  13. Jiang, Di, Shiyi Li, Irena Hajnsek, Muhammad Adnan Siddique, Wen Hong, and Yirong Wu. "Glacial lake mapping using remote sensing Geo-Foundation Model." International Journal of Applied Earth Observation and Geoinformation 136 (2025): 104371.
  14. Ullah, Sajid, Muhammad Shafique, Ghazanfar Ali Khattak, Attaullah Shah, and Yaseen Ullah. "An integrated approach for GLOF hazard, vulnerability and risk assessment in the Karakoram Mountain Range of northern Pakistan." Journal of Mountain Science 22, no. 1 (2025): 142-155.
  15. Ali, Sajid, Saima Ali, Lei Wang, Syed Razi Ul Hassan, Garee Khan, Salar Ali, Taimoor Hussain, and Dostdar Ali. "Deep learning model to detect glacial lakes using high-resolution optical and radar satellite images." Remote Sensing Applications: Society and Environment 38 (2025): 101579.