The field of remote sensing is experiencing significant advancements, driven by innovative approaches to target detection, representation learning, and semantic change detection. Notably, researchers are exploring the integration of structure priors and evidential learning theory to enhance domain adaptation for cross-resolution detection. Additionally, there is a growing interest in multi-modal and multi-label supervision-aware contrastive learning frameworks, which enable finer semantic disentanglement and more robust representation learning across spectrally similar and spatially complex classes. Hierarchical segmentation paradigms are also being developed to support multi-granularity predictions and efficient transfer of models to cross-domain tasks with heterogeneous hierarchies. Furthermore, text-to-image retrieval systems are being improved to exploit the unique physical information captured by various sensors, including SAR and multispectral data. Some particularly noteworthy papers include: MoSAiC, which introduces a unified framework for intra- and inter-modality contrastive learning with a multi-label supervised contrastive loss. HieraRS, which proposes a novel hierarchical interpretation paradigm that enables multi-granularity predictions and supports the efficient transfer of LCLU models to cross-domain tasks with heterogeneous tree-structured hierarchies. CrisisLandMark and CLOSP, which present a new large-scale corpus and a novel framework for text-to-image retrieval beyond RGB sources, achieving state-of-the-art performance in retrieval tasks. GAPL-SCD, which proposes a graph aggregation prototype learning framework for semantic change detection, achieving state-of-the-art performance on several datasets.