Elon Musk says that the biggest weakness of Grok currently is its image generation and understanding https://t.co/BBp83zAQwk ...

How do you compete with the giants, all of whom are here when it comes to AI talent. Well, we've picked some really interesting places to invest when it comes to AI. One of the things that we've really focused on is image, video, and 3D generation where Snapchat has really differentiated as a camera with people making billions of snaps every day.And so, building our own image and video models, especially models that are small enough to run on device, is a real competitive advantage for us because it allows ...

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].