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Geographical information systems-based methods can be handled as powerful tools in assessing and quantifying impacts and, thus, supporting strategies for disaster risk reduction (DRR). This is particularly relevant on scenarios of global climate change and intensified increased human interventions on riverine systems. The Madeira River in Porto Velho city (Brazilian Amazon) is a good example of susceptible area to both of these factors. We take advantage of the 2014 flood, the largest recorded for this region, for combining remote sensing and geographic information system with socio, health, and infrastructure data to quantify spatially the flood impacts. Using high resolution airborne images, we applied a machine learning classification algorithm for detecting urban areas. Our results show that at the flood extent related to the highest river level at least 0.65 of urban area, 87 km of urban streets, four public …