Interferometry in the Large Language Models era

Large Language Models have shown immense potential in tasks like text generation [1,2] and text guided natural image generation [3,4]. Despite these advancements, the untapped potential of LLMs in Remote Sensing, particularly in the InSAR (Interferometric Synthetic Aperture Radar) domain, remains substantial. InSAR data pose significant challenes for interpretation, often requiring expert knowledge. This subject is divided into two distinct theses:

  1. InSAR detailed captioning: The primary objective is to automatically generate precise and detailed descriptions of InSAR data enabling non-experts to understand and base their decisions on the extracted insights.

  2. Text to InSAR generation: The second subject revolves around text to image generation. In this case the student will study and apply state-of-the-art text to image generation methods for the creation of synthetic InSAR based on given (textual) conditions. The resulting InSAR will be evaluated by a) training models to solve crucial tasks like earthquake and volcanic activity detection, and b) the soundness of the generated InSAR.

Both works will use the Hephaestus dataset [5] as a starting point, with the flexibility to extend and modify it to suit the specific requirements of each thesis.

Supervisor: Nikolaos Ioannis Bountos

contact: bountos@noa.gr , ipapoutsis@noa.gr

[1] Touvron, Hugo, et al. “Llama: Open and efficient foundation language models.” arXiv preprint arXiv:2302.13971 (2023).

[2] OpenAI. “GPT-4 Technical Report.” ArXiv abs/2303.08774 (2023): n. pag.

[3] Saharia, Chitwan, et al. “Photorealistic text-to-image diffusion models with deep language understanding.” Advances in Neural Information Processing Systems 35 (2022): 36479-36494.

[4] Nichol, Alex, et al. “Glide: Towards photorealistic image generation and editing with text-guided diffusion models.” arXiv preprint arXiv:2112.10741 (2021).

[5] 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.

insar nlp llm text