Artificial intelligence-assisted remote sensing observation,  understanding,  and decision

Remote sensing underpins environmental monitoring and Earth science. The expansion of satellites and observation platforms drives a substantial increase in multi-source remote sensing data. The land-air-space multi-sensor stereoscopic observation heralds a new era of intelligent photogrammetry and digital infrastructure. However, the inherent complexity of multi-source data (spanning spatial, spectral, and temporal domains) poses challenges for observation, interpretation, and decision. The rapid advancement of artificial intelligence (AI) injects new vitality into the intelligent remote sensing by reshaping systems: from overcoming imaging limitations through enhanced visual observation to elevating knowledge dimensions via semantic understanding and ultimately enabling intelligent decision-making. This commentary examines how AI enhances visual observation, facilitates semantic transition, and empowers intelligent decision-making. These advancements provide support for the paradigm shift from data acquisition to cognitive services.

AI-enhanced visual observation in remote sensing imagery

Remote sensing imagery fundamentally differs from natural imagery due to disparities in sensor-specific geometric configurations, physical radiation mechanisms, and imaging platforms. These discrepancies manifest as variations in spatial/spectral resolution, radiometry, and viewing geometry, necessitating integrating multi-source data for comprehensive observation. Then, AI can harmonize multi-sensor data by leveraging underlying physical priors to guide registration and fusion. However, these multifaceted variances complicate model training, necessitating models that structurally adapt to highly heterogeneous data. Deep models enable cross-modal invariant representation and complex geometric transformations for robust registration and derive comprehensive feature extraction and fusion. They jointly underpin visual perception for downstream semantic understanding and decision.