Geosot Grid Remote Sensing Intelligent Interpretation Model based on Fine-Tuning Resnet-18: A Case Study of Construction Land

To meet the demand of the real-time monitoring of urban land use, this work establishes a grid-coding index under the framework of a global subdivision grid and constructs a grid remote sensing intelligent interpretation model, based on the fine-tuning ResNet-18 model. The proposed model realizes real-time sharing and query statistics of grid learning results. Setting construction land identification as our objective, SPOT 6 satellite data of September 2018 and Gaofen-l satellite data of October 2018 were used in an experiment to train and validate the fine-tuning ResNet-18 model. The overall test accuracies of each data set were 97% and 91%, respectively, effectively realizing intelligent change monitoring and distribution statistics with regard to construction land.