Aug 2026· Remote Sensing· Vol 18, pp. 2849· 0 citations· 30 references
TL;DR
SeaScope is presented, 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.
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.
It is argued that future progress depends less on incremental accuracy gains than on reproducible multi-source workflows, explicit uncertainty, trustworthy and explainable geospatial artificial intelligence, privacy-preserving governance, interoperable standards and evaluation in the institutions that ultimately use the evidence.
Yashvardhan Singh, Divya Singh, Sakshi Shukla et al.· Advances in Research· 0 citations
Earth and environmental research (E&E) involves managing complex, large-scale data streams that require robust processing, thorough documentation, and secure long-term preservation. The O2A (Observations to Analysis and Archives) Data Flow Framework presents a modular solution linking raw observations to standardized workflows and archival systems, facilitating the transformation of diverse sensor outputs into curated and accessible datasets. Its integrated modules enable efficient instrument metadata management, automated data ingestion, near real-time monitoring, collaborative analysis, and FAIR-compliant data publication. The framework has been successfully implemented in prominent initiatives such as MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) and the German Marine Research Alliance (DAM), providing a scalable and interoperable foundation for contemporary data management and promoting reproducibility as well as cross-institutional collaboration.
R. Koppe, S. Hänzelmann, Daniel Damaske et al.· Earth Science Informatics· 0 citations
Many existing time-series Interferometric Synthetic Aperture Radar (TS-InSAR) software tools have limitations, including restricted geographic applicability, commercial licensing, and incomplete end-to-end processing support. Although GMTSAR avoids some of these constraints, it still requires substantial manual intervention and C-shell commands, lacks a user-friendly graphical interface, and omits important steps such as interferogram network pruning and anchoring of unwrapped interferograms. This paper introduces DefoEye (v1), an open-source Python-based software package that wraps GMTSAR and provides a unified, user-friendly TS-InSAR workflow for Sentinel-1 data. DefoEye supports parallel job execution, interferogram network pruning, and multiple anchoring options. Its performance was evaluated from 2020 to 2024 in four regions with different geological settings, deformation mechanisms, and atmospheric and climatic conditions. In Bologna, Italy; Gotland, Sweden; and Houston, USA, DefoEye results were compared with observations from 10 GNSS stations and showed strong agreement, with RMSE values of 4.3-11.9 mm and Pearson correlation coefficients of 0.63-0.95. In Karaj, Iran, where GNSS observations were unavailable, DefoEye was compared with other widely used processing tools and achieved similarly close agreement, with an RMSE of 4.8 mm/yr and a Pearson correlation coefficient of 0.98. These results demonstrate that DefoEye provides reliable TS-InSAR products for geological, hydrological, and environmental applications.
Alireza Taheri Dehkordi, Hossein Hashemi, Amir Naghibi· 0 citations
Abstract. The increasing availability of kilometer-scale climate simulations presents major challenges for data access, processing, and analysis due to the unprecedented volume and heterogeneity of the outputs. Different data formats, structures, and metadata conventions, require dedicated solutions to ensure interoperability and usability. We introduce AQUA (Application for QUality Assessment), a Python-based framework developed within the Climate Change Adaptation Digital Twin of the Destination Earth (DestinE) initiative, designed to support the automated evaluation of high-resolution global climate simulations. Although several diagnostic suites for the analysis of global climate model data are already available, AQUA provides a flexible and modular infrastructure for accessing and processing climate model output across various formats. By building on widely adopted Python libraries, it enables scalable, out-of-core computations. Its design supports integration into automated workflows and user-defined pipelines, facilitating both operational and research-oriented applications. This paper focuses on the architecture and core functionalities of the AQUA core, which handles data ingestion, standardization, and pre-processing. AQUA is open source and actively maintained, and aims to serve as a community tool for robust, reproducible, and efficient climate data analysis across projects and institutions.
Matteo Nurisso, J. Hardenberg, Marco Cadau et al.· Geoscientific Model Developm...· 0 citations
Foundation models (FMs) and their task-specific variants are increasingly forming large and heterogeneous collections that resemble
model lakes.
Managing such lakes requires more than hosting models: users must be able to discover relevant models, compare them using reproducible evidence, and operationalize selected models for downstream use. We present EarthLake, a model lake system for Earth Observation (EO) FMs. The system integrates a schema-guided model registry, task-driven model discovery, and an experiment management framework for benchmarking and comparing candidate FMs. In the demo, participants explore the model registry, discover FMs using natural language task descriptions, benchmark selected FMs on EO datasets, compare experiment results, and execute chosen FMs on new imagery. Our interactive demonstration illustrates how EarthLake supports end-to-end workflows for discovering, evaluating, and operating EO FMs.
Bing-Er Chen, Haralampos Gavriilidis, Luca Gaedicke et al.· Proceedings of the VLDB Endo...· 0 citations
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