人工智能与遥感科学交叉研究综述：现状与展望

Remote sensing science, through multi-platform, multi-scale, and multi-modal observations, provides key technical support for understanding the structure of the Earth system and environmental evolution. It also holds significant strategic importance in fields such as resource investigation, ecological monitoring, urban management, and disaster emergency response. The advent of advanced observation methodologies, including high-resolution optical remote sensing, Synthetic Aperture Radar (SAR), hyperspectral imaging, and lidar, has led to a proliferation of remote sensing data of unparalleled scale, variety, and continuously enhancing resolution. This paradigm shift has propelled Earth observation into a new era characterized by the management of voluminous data sets. However, the high-dimensional structural differences, spatio-temporal scale inconsistencies, and information redundancy brought about by multi-source heterogeneous data have significantly limited the traditional interpretation mode that relies on manual rules and experience-driven in terms of accuracy, efficiency, and generalization ability. Therefore, there is an urgent need to promote the transformation of remote sensing science towards an autonomous and intelligent paradigm. The advent of Artificial Intelligence (AI) has furnished a novel theoretical foundation and technical trajectory for the domain of remote sensing science. Intelligent methods, exemplified by deep learning, large models, self-supervised learning, and cross-modal representation, possess the capacity to automatically extract multi-level semantic features from substantial remote sensing data. This capability enables the efficient recognition, inference, and prediction of complex ground objects, environmental elements, and spatiotemporal dynamic processes. Presently, AI has achieved significant advancements in a variety of domains, including ground object classification, object detection, semantic segmentation, change detection, 3D scene reconstruction, and environmental element inversion. These developments have demonstrated the potential value of enhancing accuracy, generalizability, and decision-making speed in various application scenarios, such as geological remote sensing, ecological monitoring, agricultural situation assessment, urban remote sensing, and disaster damage assessment. Concurrently, novel research paradigms are emerging, characterized by the utilization of large remote sensing models and cross-modal fusion as their fundamental framework. These paradigms signify a paradigm shift in remote sensing science, transitioning from an intelligent interpretation oriented towards single tasks to an integrated intelligence oriented towards scene understanding and system cognition. Despite the rapid advancements in artificial intelligence, which are profoundly impacting the field of remote sensing science, significant challenges persist. These challenges include inadequate coordination between multi-source observation mechanisms and model representations, constrained generalizability across regions, disasters, and sensors, deficient model interpretability and credibility, and suboptimal data-driven and physical prior fusion mechanisms. In order to promote remote sensing science from“observation”to“cognition and decision support,”it is essential to build a new generation of intelligent remote sensing systems with physical consistency, dynamic adaptability, and sustainable evolution capabilities. In summary, AI is driving remote sensing science into a new phase that is centered on intelligent representation, cross-modal fusion, and knowledge-driven inference. This paper systematically reviews the fusion progress from three dimensions of observation technology, intelligent method, and typical application. It analyzes the key challenges and looks forward to the future development direction. The purpose of this analysis is to provide a reference for the construction of a unified, generalized, and reliable intelligent remote sensing theory system.