Remote sensing for land cover mapping across Victoria, Australia – a machine learning application

Abstract

This paper presents a detailed land cover mapping study for the state of Victoria in Australia conducted through a machine learning approach (random forest algorithm) using Sentinel-2 imagery. The study uses a hierarchical classification, based on the FAO’s Land Cover Classification Scheme. This paper highlights the importance of spatial random sampling in assessing ground data, and details methods for land cover mapping, remote sensing analysis, calibration as well as validation. The land cover mapping procedure involves the use of a fine-tuned random forest classifier, and an overlaying mask generation technique to improve classification accuracy. The resulting 2021/22 land cover map is accessible through the Victorian Land Use Information System (VLUIS) and has undergone rigorous technical validation with an overall accuracy of 86%. This data set is publicly accessible and regularly released to provide valuable information for a variety of applications, including agricultural policy development, strategic planning, climate change modelling and environmental monitoring.