Observed increases in the frequency of extreme climatic events such as tropical storms amplify the vulnerability of communities to disasters in developing countries. For example, in developing Zimbabwe, the low adaptive capacity of the population associated with recurrent economic stressors compromises the ability of communities to cope with natural disasters. As such rapid assessment of the disruption of livelihood systems as well as damage to structures caused by disasters is critical for implementing measures to restore affected communities to the pre-disaster state. Considering that all disasters whether they are natural or anthropogenic in origin, have the following key characteristics: location, magnitude, intensity, onset, duration and frequency, the increasing availability and accessibility of earth observation data coupled with advances in geographic information systems (GIS) provide vast opportunities for developing rapid assessment of damage. Specifically, the availability of cloud computing applications and the access to high resolution satellite imagery with increased revisit time have boosted the robustness of earth observation in monitoring the intensity, frequency, duration and impact of extreme weather events which result in disasters such as cyclonic winds and torrential storms that trigger flooding. In recent years, unmanned aerial vehicles (UAVs) or drones have provided an additional platform for collecting near real time data for post-disaster damage assessment. The main advantages of using UAVs are that the critical datasets for post-disaster damage assessment can be acquired rapidly, at relatively low cost at the desired frequency. Using Cyclone Idai which hit the eastern parts of Zimbabwe particularly Chimanimani district in March of 2019 and was declared a national disaster, as a case study. This chapter describes a simple and repeatable change detection approach for integrating satellite and drone imagery in a GIS framework to undertake comprehensive cyclone damage assessment. The methodology described in this chapter is ideal for giving a quantitative measure of the type and extent of physical damage for disaster management practitioners and emergency rescue personnel operating in resource-constrained environmental settings.