Machine Learning based land use classification and identification of driving factors in Google Earth Engine

Abriha, DávidLi, Junyi2026-01-082026-01-082025-11-26https://hdl.handle.net/2437/401814This thesis utilizes Google Earth Engine and machine learning to classify land use and analyze driving factors in Dalian, China, from 2000 to 2020. It employs a Random Forest algorithm on Landsat imagery to generate land use maps for four categories: agricultural land, vegetation, water, and built-up areas. Using Principal Component Analysis and Multiple Linear Regression, it identifies the primary drivers of land use change. The research demonstrates the effectiveness of cloud computing platforms for large-scale, long-term land use monitoring and provides a scientific basis for regional land management.38enLand use classificationGoogle Earth EngineDriving factors of LULCMachine Learning based land use classification and identification of driving factors in Google Earth EngineEarth SciencesHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.