The proposed method is a quite robust approach that is based on unsupervised classification and cannot be considered to be case-dependent. Despite that, it has some limitations that are generally common to all change detection-based algorithms. The main one is that in some areas, a pre-event image (SAR or/and optical) cannot be available. This limitation nowadays is quite reduced because of the increased number of satellites and the global coverage of historical data. Of course, it would important to have updated images as much as possible to limit false alarms due to changes in the scene not due to damage. A very important opportunity to overcome these limitations will be, in the future, the availability of Copernicus Sentinel mission’s archives. Sentinel missions are able to routinely obtain a global coverage, which is expected to minimize the lack of updated pre-event data. Actually, it is not easy to determine a requirement on the maximum temporal separation between pre- and postimages. It typically depends on the regions we are considering (some places in the world are changing faster than others), and only a general consideration can be done: a low temporal span reflects (nominally) more accurate results. In this study, the temporal baseline is 3 and 5 months, respectively, for optical and SAR data. Clearly, the change detection indices are affected by other factors if the period between the images’ acquisition is much longer. It is worth noting that the proposed method, being based on city block areas, is quite robust with respect to changes not related to an earthquake, because we can guess that modifications caused by temporal changes are small (within the blocks) if compared with the changes due to such catastrophic event. In other words, these sources of errors are averaged within the blocks. However, some of these affecting factors are taken into account. For example, changes due to different illuminations at different times are taken into account by applying the atmospheric correction to images; the vegetation coverage is removed by using an appropriate mask.