Global environmental changes have increased the frequency of natural disasters and the demand for rapid postdisaster mapping. In this regard, remote sensing (RS) is a leading technology because it provides consistent near-real-time images. In this chapter, we studied different disasters, Joplin MO Tornado (2011), Hurricane Harvey (2017), and Hurricane Michael (2018), using satellite sensors such as Landsat 5 and Sentinel 2 and airborne imagery acquired within the National Agriculture Imagery Program (NAIP) and by the National Oceanic and Atmospheric Administration (NOAA). We compared different RS methods, such as pixel- and object-based classification techniques and spectral/spatial feature analysis to compare the potential of vertical and oblique images to produce regional- and building-level damage maps. We illustrated several large-scale and zoomed scenes for visual interpretation and the corresponding assessment analysis. Finally, the further development of RS technology and its effect on the development of the algorithm are discussed.