An automated classification and change detection system for rapid update of land-cover maps of South Africa using Landsat data.

Recent land cover maps are essential to spatial planning and assessment by non-/governmental agencies. The current land cover mapping methods employed in South Africa are slow and expensive and the most recent national land cover map dates back to 2000. The CSIR is developing an automated land-cover mapping system for the South African region. This system uses widely available Landsat satellite image time series data, together with supervised machine learning, change detection, and image preprocessing techniques. In this presentation the implementation of this end-to-end system will be addressed. Specifically, we will discuss the use of an open source random forest implementation (Weka), a change detection algorithm (IRMAD), as well as tools used for satellite image preprocessing (Web enabled Landsat data, fmask cloud masking) and on-line validation tools. Furthermore the approach used in optimising automatic land-cover production accuracy for operational use will be discussed.