摘要：Land coverage information is essential data in the management of watersheds. The challenge in providing land coverage information in the Kapuas watershed is the cloud cover and its significant area coverage,thus requiring a large image scene. The presence of a cloud-based spatial data processing platform that is Google Earth Engine (GEE) can be answered these challenges. Therefore this study aims to map land coverage in the Kapuas watershed using machine learningbased classification on GEE. The process of mapping land coverage in the Kapuas watershed requires about ten scenes of Landsat 8 satellite imagery. The selected year is 2019,with mapped land coverage classes consisting of water bodies, vegetation,non-vegetated (barren land),and built-up area. Machine learning that tested included CART,Random Forest,GMO Max Entropy,SVM Voting,and SVM Margin. The results of this study indicate that the best machine learning in mapping land coverage in the Kapuas watershed is GMO Max Entropy,then CART. This research still has many limitations,especially mapped the covering land classes. So that research needs to be developed with more detailed land coverage classes, more diverse and multi-time input data.
