This paper introduces a novel geospatial identification approach to distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Carried out within the framework of the World Bank’s Country Disaster Risk Profiles (CDRP) project initiative - currently being implemented in Central America - global applicability and easy transferability is considered crucial. Therefore global spatial datasets are used throughout in the setup of the disaggregated property stock exposure model, one of the key elements for subsequent disaster risk and loss estimation. After initial urban-rural classification at a 1km grid level, predominantly residential areas need to be identified as opposed to areas of mixed use in order to spatially link accordingly compiled property stock information (e.g. from global tabular databases such as PAGER-STR). Impervious Surface Area (ISA) data based on remotely sensed nighttime lights from the DMSP-OLS sensor are used as proxy to identify areas of peak human activity. Intense lighting in that context is associated with a high likelihood of commercial and/or industrial presence, commonly clustered in certain parts of a city (such as central business districts and or peripheral commercial zones). Areas of low light intensity, in turn, can be considered more likely residential. Several light intensity threshold are tested for Cuenca City, Ecuador, in order to best match the situation on the ground, where local-level cadastral land use data show a 75-25 distribution ratio of residential vs. mixed use. Results will be presented first-hand in this paper and future work will be addressed highlighting the relevance of remote sensing data for top-down modeling approaches at wide spatial scale.