Dynamic construction of high-resolution remote sensing image sample datasets and intelligent interpretation applications
In the era of artificial intelligence,the interpretation of remote sensing images is moving towards automation and in-telligence.High-quality sample datasets are crucial for this development.A massive amount of high-quality spatio-temporal geospatial information and derived products has been accumulated in China,which serve as an important source for deep learn-ing-driven intelligent interpretation of remote sensing images.Leveraging existing data resources can promote the depth and breadth of artificial intelligence and remote sensing interpretation applications.Aiming at the limitations of existing sample datasets,such as regional restrictions,limited timeliness and sample types,this paper proposes a dynamic construction tech-nique for a high-resolution remote sensing image intelligent interpretation sample datasets based on existing data resources.Firstly,a business-driven framework for sample datasets demand generation,dynamic construction,and intelligent application is proposed based on the characteristics analysis of publicly available sample datasets for feature extraction,land cover classifi-cation,and change detection.Secondly,this research investigates methods and implementation processes for sample generation based on historical interpretation results,as well as sample refinement through SAM(segment anything model)large model prompt learning-guided cleaning.Furthermore,a sample dataset with regional,temporal,scale,multi-sensor,and multi-type features is designed,along with spatial-temporal-land cover relationships.The study explores the dynamic reconstruction process of sample dataset quantification-retrieval-combination,achieving dynamic management of spatiotemporal samples and multidimensional retrieval.Finally,intelligent interpretation applications such as land cover classification,feature extraction,and change detection are conducted to validate the feasibility of the proposed methods.The aim is to promote the effective utili-zation of sample datasets based on existing foundational data,as well as the interconnection of sample construction-manage-ment-application and data-model-business,providing reference ideas for the construction and application of high-resolution re-mote sensing image intelligent interpretation sample datasets.