Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine

• Published in: Journal of environmental planning and management Vol. 66; no. 3; pp. 665 - 697
• Main Authors: Feizizadeh, Bakhtiar, Omarzadeh, Davoud, Kazemi Garajeh, Mohammad, Lakes, Tobia, Blaschke, Thomas
• Format: Journal Article
• Language: English
• Published: Abingdon Routledge 23.02.2023; Taylor & Francis Ltd
• Subjects: Algorithms; Analysis; Automation; Change detection; Classification; Cloud computing; Cognitive style; comparative approach Google Earth Engine; Data; data collection; Daylight saving; Decision analysis; Decision makers; Decision making; Decision trees; Earth; Environmental protection; Internet; Iran; Land cover; Land use; land use/cover mapping; Landsat; Landsat satellites; Learning algorithms; Machine learning; Mapping; Regression analysis; Reliability; Reliability analysis; Remote monitoring; Remote sensing; Satellite imagery; Satellites; Spatial analysis; spatial uncertainty analysis; Support vector machines; Time series; time series analysis; Trend analysis; Uncertainty; Uncertainty analysis; Urmia lake basin; Iran

With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated classification techniques enable the (semi-) automated remote monitoring and analysis of large areas. Onl...