Research on multi-scale prediction method of land cover change based on deep autoregressive model and remote sensing data fusion

This study aims to improve the accuracy and reliability of land cover change prediction. By fusing the deep autoregressive model with remote sensing data, a new prediction method is proposed in this thesis. The method combines the advantages of deep learning in feature extraction with the characteristics of autoregressive model in time series prediction, which effectively improves the prediction accuracy. The results show that the proposed method exhibits significant effects in multi-scale spatio-temporal analysis, ecological service impact studies, remote sensing land cover classification, and land use change studies. This thesis verifies the effectiveness and practicability of the method through actual case studies, provides a scientific basis for government departments to formulate ecological protection and land use planning, and provides an important reference for promoting the construction of ecological civilization. Supplementary Material File (research on multi-scale prediction method of land cover change based on deep autoregressive model and remote sensing data fusion.docx) - Download - 39.70 KB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 127views 65downloads Citations Download citation Jie Xiong. Research on multi-scale prediction method of land cover change based on deep autoregressive model and remote sensing data fusion. Authorea. 08 April 2025. DOI: https://doi.org/10.22541/au.174411921.18807571/v1 DOI: https://doi.org/10.22541/au.174411921.18807571/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.