Machine learning for remote sensing (ML4RS) has emerged as a critical and exciting area of research, with the potential to address some of the most pressing global challenges, including climate change, food security, disaster management, and conservation. Remote sensing data, collected from diverse instruments capturing the Earth across various spatial, temporal, and spectral dimensions, offers unique research opportunities and challenges for the ML community. Unlike traditional data modalities, these datasets are high-dimensional, extremely multi-modal, and contain patterns at a multitude of spatial and temporal scales. These characteristics often require specialized approaches in cross-cutting ML topics like self-supervised/semi-supervised learning, domain adaptation/generalization, and multi-modal learning/data fusion to unlock their full potential. Our workshop will foster discussion and feedback on early-stage work that is critical to impactful applications and new developments in machine learning for remote sensing.