Core Insights - A new method for generating satellite imagery using geographic climate prompts and Stable Diffusion 3 (SD3) has been proposed by teams from the Technical University of Munich and ETH Zurich, resulting in the creation of the largest and most comprehensive remote sensing dataset, EcoMapper [1][2][4]. Dataset Overview - EcoMapper consists of over 2.9 million RGB satellite images collected from 104,424 global locations, covering 15 land cover types and corresponding climate records [2][5]. - The dataset includes a training set with 98,930 geographic points, each observed over a 24-month period, and a test set with 5,494 geographic points observed over 96 months [5][6]. Methodology - The research developed a text-image generation model based on fine-tuned SD3, which utilizes climate and land cover details to generate realistic synthetic images [4][8]. - A multi-condition model framework using ControlNet was also developed to map climate data or generate time series, simulating landscape evolution [4][12]. Model Performance - The study evaluated the performance of SD3 and DiffusionSat models in generating climate-aware satellite images, with metrics indicating significant improvements over baseline models [14][19]. - The SD3-FT-HR model achieved the lowest Fréchet Inception Distance (FID) score of 49.48, indicating high realism in generated images [15][16]. Climate Sensitivity Analysis - The generated vegetation density was found to be significantly correlated with climate changes, with performance varying under extreme weather conditions [16][18]. Applications and Future Directions - EcoMapper provides a framework for simulating satellite images based on climate variables, offering new opportunities for visualizing climate change impacts and enhancing integration of satellite and climate data for downstream models [22][26].

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