The primary aim of this workshop is to bring together researchers, practitioners, and industry experts to explore the integration of artificial intelligence (AI), intelligent systems, and advanced computational methods in remote sensing and computer vision applications. The workshop focuses on novel methodologies and systems that harness multimodal data, distributed computing, and machine learning to address urgent challenges related to climate change, environmental monitoring, and sustainable resource management.

Participants will have the opportunity to participate in discussions related to AI-driven techniques for large-scale modeling, real-time monitoring, and decision support systems, with a special emphasis on:

• Remote sensing and computer vision for agriculture, forestry, and biodiversity.
• Signal and time-series analysis for ecological and geospatial phenomena.
• Generative and self-supervised learning to overcome data scarcity and enhance model generalization.
• Explainable AI and edge computing for interpretable, real-time, and energy-efficient solutions.

The workshop fosters interdisciplinary collaboration and aims to bridge the gap between data-driven innovations and practical implementations in fields such as environmental forecasting, disaster management, air quality assessment, and urban mobility. By highlighting state-of-the-art research and applications, the event seeks to catalyze progress toward trustworthy, sustainable, and socially responsible AI systems that support human-environment interaction analysis and informed policy-making.