Michael Vaccaro

Antoine Labatie, Michael Vaccaro, Nina Lardiere et al.

Self-supervised learning holds great promise for remote sensing, but standard self-supervised methods must be adapted to the unique characteristics of Earth observation data. We take a step in this direction by conducting a comprehensive benchmark of fusion strategies and normalization schemes of reconstruction targets for multimodal, multitemporal, and multispectral Earth observation data. Based on our findings, we introduce MAESTRO, a novel adaptation of the Masked Autoencoder with optimized fusion mechanisms and a normalization scheme that incorporates a spectral prior as a self-supervisory signal. Evaluated on four Earth observation datasets in both intra- and cross-dataset settings, MAESTRO achieves state-of-the-art performance on tasks that strongly rely on multitemporal dynamics, while also remaining competitive on others. Code to reproduce all our experiments is available at https://github.com/ignf/maestro.

Michael Vaccaro, Mikayla Friday, Arash Zaghi

Large language models (LLMs), including OpenAI's GPT-series, have made significant advancements in recent years. Known for their expertise across diverse subject areas and quick adaptability to user-provided prompts, LLMs hold unique potential as Personalized Learning (PL) tools. Despite this potential, their application in K-12 education remains largely unexplored. This paper presents one of the first randomized controlled trials (n = 23) to evaluate the effectiveness of GPT-4 in personalizing educational science texts for middle school students. In this study, GPT-4 was used to profile student learning preferences based on choices made during a training session. For the experimental group, GPT-4 was used to rewrite science texts to align with the student's predicted profile while, for students in the control group, texts were rewritten to contradict their learning preferences. The results of a Mann-Whitney U test showed that students significantly preferred (at the .10 level) the rewritten texts when they were aligned with their profile (p = .059). These findings suggest that GPT-4 can effectively interpret and tailor educational content to diverse learner preferences, marking a significant advancement in PL technology. The limitations of this study and ethical considerations for using artificial intelligence in education are also discussed.