The field of earth observation and object detection is rapidly evolving, with a focus on developing more accurate and efficient models for various applications. Recent research has emphasized the importance of high-quality datasets and innovative modeling approaches to address real-world challenges. Notably, the development of large-scale datasets and benchmarks has enabled significant advancements in areas such as land use land cover mapping, vehicle detection, and volcanic unrest monitoring. Furthermore, the integration of deep learning techniques with traditional methods has led to improved performance in tasks like object detection and segmentation. Overall, the field is moving towards more robust and flexible models that can handle diverse data sources and applications. Noteworthy papers include: Hephaestus Minicubes, which introduces a global, multi-modal dataset for volcanic unrest monitoring, and RemoteSAM, which presents a foundation model for earth observation that achieves state-of-the-art performance on several benchmarks. RemoteSAM establishes a new standard for earth observation perception tasks, outperforming other models with higher efficiency. Hephaestus Minicubes provides a comprehensive benchmark for volcanic unrest monitoring, supporting machine learning research in this area.