arXiv · · Notable
This paper presents an automated ArcGIS Pro tool using ArcPy to extract Land Surface Temperature (LST) from Landsat 7, 8, and 9 satellite datasets. The tool automates multi-step workflows like radiometric calibration and emissivity correction, reducing processing time from 11-58 minutes manually to 4-11 minutes. It was successfully tested with data from Ras Al Khaimah (RAK) in the UAE, demonstrating accurate LST mapping compatible with local weather conditions, and includes an optional Large Language Model (LLM) for statistical result interpretation and reporting. Why it matters: This tool offers a practical application of AI and automation for environmental monitoring in the UAE, significantly improving efficiency and accuracy in remote sensing for climate research and urban planning.
Land Surface Temperature · Remote Sensing · Large Language Model · ArcGIS Pro · Environmental Monitoring
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arXiv ·
MBZUAI researchers introduce TerraFM, a scalable self-supervised learning model for Earth observation that uses Sentinel-1 and Sentinel-2 imagery. The model unifies radar and optical inputs through modality-specific patch embeddings and adaptive cross-attention fusion. TerraFM achieves strong generalization on classification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Bench.
arXiv ·
This paper introduces a framework that combines machine learning for multi-class attack detection in IoT/IIoT networks with large language models (LLMs) for attack behavior analysis and mitigation suggestion. The framework uses role-play prompt engineering with RAG to guide LLMs like ChatGPT-o3 and DeepSeek-R1, and introduces new evaluation metrics for quantitative assessment. Experiments using Edge-IIoTset and CICIoT2023 datasets showed Random Forest as the best detection model and ChatGPT-o3 outperforming DeepSeek-R1 in attack analysis and mitigation.