SeaScope: A Transparent and Reproducible LLM-Assisted Framework for Maritime Earth Observation Analysis
Journal
Remote Sensing
ISSN
2072-4292
Date Issued
August 22, 2026
Author(s)
Sekas, Christos
Mavrofidopoulou, Lydia
Agathangelidis, Ilias
Cartalis, Constantinos
Philippopoulos, Kostas
Mavroudis, Faidon
Yfantidis, Ioannis
Paterakis, George
Abstract
Earth Observation (EO) analysis increasingly relies on large and heterogeneous satellite datasets, yet developing EO workflows often requires specialized expertise in data selection, geospatial programming, and cloud-based processing. Recent advances in Large Language Models (LLMs) offer new opportunities for natural-language interaction with EO systems, although challenges related to transparency, reproducibility, and domain-specific reasoning remain. This study presents SeaScope, an explainable AI framework that integrates LLMs, Retrieval-Augmented Generation (RAG), scientific knowledge retrieval, and Google Earth Engine (GEE) to transform natural-language requests into transparent and executable EO workflows. The framework combines knowledge retrieval, code generation, cloud execution, provenance tracking, and interactive visualization within a unified environment. A pilot implementation is demonstrated through maritime and coastal monitoring applications, including oil spill detection, vessel monitoring, water quality assessment, floating debris detection, and air quality analysis. Multiple state-of-the-art LLMs are evaluated under both RAG and non-RAG configurations using representative EO case studies. The results indicate substantial differences among model families and show that retrieval augmentation can significantly improve workflow generation quality and reliability for capable models, while providing more limited benefits for smaller models. The proposed framework demonstrates the potential of explainable AI agents to support transparent, reproducible, and scalable EO analysis.
Affiliation
CTRL + Space Development E.E
Funding
This research was funded by the European Union, via oc1-2025-TIS-01, issued and implemented by the ENFIELD project, under the grant agreement No. 101120657.
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