Add thesis for eo foundation models

content/education/theses/2023-06-11-foundation-models/index.md

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-title: Leveraging foundation models for earth observation applications
+title: Leveraging Foundation Models for Earth Observation Applications
 date: 2023-11-06
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-tags: ['wildfires']
+tags: ['foundation models', 'earth observation', 'EO', 'climate']
+draft: false
 ---
+Supervisors: [Ioannis Prapas](/author/ioannis-prapas/), [Ioannis Papoutsis](/author/ioannis-papoutsis)
 
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+Recently we've witnessed the rise of foundation models [^1] for text (e.g. [chatGPT](https://openai.com/blog/chatgpt), [Llama](https://ai.meta.com/llama/)) and images (e.g. [Dalle](https://openai.com/dall-e-3), Stable Diffusion [^2]), even video (e.g. [Stable Video Diffusion](https://stability.ai/news/stable-video-diffusion-open-ai-video-model)).
 
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+Foundation models are also being developed for other fields (e.g. Medical science [^3]), and particularly for Earth Observation[^4] [^4.5] and Climate Science[^5]. The goal of this thesis is to investigate the maturity of these models and evaluating them in real-world downstream applications, like burned area mapping[^6], flood detection[^7] [^8], wildfire forecasting[^9] [^10], volcanic unrest detection[^11] [^12] or similar applications with impact for social good. Beyond evaluation, this thesis is about devising ways to fine-tune these large models to work better for downstream tasks. 
 
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+[^1]: Bommasani, Rishi, et al. "On the opportunities and risks of foundation models." arXiv preprint arXiv:2108.07258 (2021).
+[^2]: Rombach, Robin, et al. "High-resolution image synthesis with latent diffusion models." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.
+[^3]: Moor, Michael, et al. "Foundation models for generalist medical artificial intelligence." Nature 616.7956 (2023): 259-265.
+[^4]: https://www.earthdata.nasa.gov/news/impact-ibm-hls-foundation-model
+[^4.5]: Liu, Fan, et al. "RemoteCLIP: A Vision Language Foundation Model for Remote Sensing." arXiv preprint arXiv:2306.11029 (2023).
+[^5]: Nguyen, Tung, et al. "ClimaX: A foundation model for weather and climate." arXiv preprint arXiv:2301.10343 (2023).
+[^6]: Sdraka, Maria, et al. "FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2." arXiv preprint arXiv:2311.03339 (2023).
+[^7]: Bountos, Nikolaos Ioannis, et al. "Kuro Siwo: 12.1 billion $ m^ 2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping." arXiv preprint arXiv:2311.12056 (2023).
+[^8]: Li, Wenwen, et al. "Assessment of a new GeoAI foundation model for flood inundation mapping." Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. 2023.
+[^9]: Kondylatos, Spyros, et al. "Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean." arXiv preprint arXiv:2306.05144 (2023).
+[^10]: Prapas, Ioannis, et al. "TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.
+[^11]: Bountos, Nikolaos Ioannis, et al. "Self-supervised contrastive learning for volcanic unrest detection." IEEE Geoscience and Remote Sensing Letters 19 (2021): 1-5.
+[^12]: Bountos, Nikolaos Ioannis, et al. "Hephaestus: A large scale multitask dataset towards InSAR understanding." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.