The availability of geospatial information has increased dramatically over the years and huge amounts of data are collected each day. Earth observation satellites such as Landsat deliver several hundred gigabytes of data every day, and while free, the sheer sizes of such datasets create computing and storage difficulties even with improved capabilities of current personal computers. Recent developments in cloud computing applications such as the Google Earth Engine® (GEE) can offer solutions to these difficulties by providing an online warehouse of satellite data and superior computing capability needed to analyze these data. With such platforms, the burden of downloading huge amounts of data, storing and processing them can be greatly reduced allowing researchers to concentrate vital data analysis to address complex and expensive environmental challenges being faced today. We present results on Landsat scale mapping of seasonal landscape fires in eastern Zambia using the GEE platform. Our methodology involves automatic training sample selection from burned areas and a subsequent ensemble classification using Random Forests. We mapped fire events using Landsat 8 data – over two districts - from June to October of 2013 with classification accuracies above 90%. Of the 22637 square kilometers mapped, about 60% of it had burned by end of October. The peak of the burning occurred in August with land cover specific fire distribution e.g. in cropland, coinciding with typical land management cycles - which confirms the anthropogenic link of fire in the area.