GIS, DV and RS in Urban Disaster Managament Lorena Montoya, ITC Email: montoya@itc.nl Abstract Successful strategies depend on the availability of accurate information presented in an appropriate and timely manner. Information is also important as it increases the transparency and accountability of the decision-making process and it can therefore contribute to good governance. Unfortunately, supplying decision-makers with raw geographic information is a very common practice which has generally yielded negative results. Simply, decision makers require information that allows them to establish the cost-benefit implications of the various strategies that can be implemented. Consequently, loss forecasts are essential as they establish the amount of damage that can be avoided by investing a given amount of financial resources. One of the biggest challenges in the field of loss forecasting relates to bridging the information gap that exists concerning building inventories. Although cadastral databases are usually available either in analogue or digital format, they tend to be designed for land registration and fiscal purposes only. For this reason, the classification of building types contained in these databases is often useless for natural hazard loss estimation. In the medium or long-term, cadastral databases can be transformed into multi-functional ones and there are already a few municipal governments that are moving in this direction. In the short term, however, low-cost and rapid alternatives must be identified so that disaster management can be carried out. The case of the Costa Rican city of Cartago provides an interesting case study; it represents a typical example of a medium-sized Costa Rican city that is located in a highly hazard-prone area and which has been devastated by earthquakes and lahars (mudflows of volcanic origin) on a number of occasions. In terms of generating a building inventory for Cartago, the paper explores a low-cost and rapid method that involves the combination of digital video (DV), remote sensing (RS) and global positioning systems (GPS) for the data capture phase and the use of a geographic information system (GIS) for the processing and display phases. Finally, aggregation and generalisation techniques will be discussed as means of managing the uncertainties involved.