International Journal of Advanced Remote Sensing and GIS
https://cloudjl.com/index.php/RemoteSensing
<p><em>International Journal of Advanced Remote Sensing and GIS </em>(IJARSG, ISSN 2320 – 0243) is an open-access peer-reviewed scholarly journal publishes original research papers, reviews, case study, case reports, and methodology articles in all aspects of Remote Sensing and GIS including associated fields. This Journal commits to working for quality and transparency in its publishing by following standard Publication Ethics and Policies. </p>Cloud Publicationsen-USInternational Journal of Advanced Remote Sensing and GIS2320-0243Evaluating Vision-Enabled LLMs a Comparative Study on Cloud Detection using Horizon Camera Imagery
https://cloudjl.com/index.php/RemoteSensing/article/view/72
<p>The rapid advancement of vision-enabled large language models (LLMs) presents transformative opportunities for specialized domains such as atmospheric science. This study evaluates the efficacy of multimodal LLMs in cloud identification tasks by leveraging a curated subset of the Clouds-1500 dataset, annotated with World Meteorological Organization (WMO) cloud classes. We introduce a novel pipeline that converts segmentation masks into text-based spatial, coverage, and class representations, enabling structured LLM analysis through custom prompts and the BAML library for response standardization. Benchmarking 18 state-of-the-art models revealed significant performance variations, with Anthropic’s Claude 3.5 Sonnet (71.67% class accuracy), OpenAI’s GPT-4o (68.89%), and xAI’s Grok Vision Beta (70.00%) emerging as top performers. However, challenges persist in low-coverage scenarios, where even leading models exhibited accuracy drops of 30–50%. The study demonstrates that while LLMs show promise in interpreting complex meteorological data, their effectiveness depends on task complexity, model architecture, and domain-specific adaptations. These findings provide a framework for integrating LLMs into remote sensing workflows, balancing automation with the precision required for operational meteorology.</p> <p> </p> <p><strong><em>Keywords </em></strong><em>Remote Sensing; Large Language Models; Sky Clouds; Multimodal LLMs</em></p>Allan CerentiniJuliana Marian Arrais ArraisBruno Juncklaus MartinsSylvio Luiz Mantelli Neto Mantelli NetoTiago Oliveira da LuzAldo von Wangenheim
Copyright (c) 2025 Allan Cerentini, Juliana Marian Arrais Arrais, Bruno Juncklaus Martins, Sylvio Luiz Mantelli Neto Mantelli Neto, Tiago Oliveira da Luz, Aldo von Wangenheim
https://creativecommons.org/licenses/by/4.0
2025-04-092025-04-0913135573569