Abstract

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently
updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results.
The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds
existing knowledge into the processing to enhance change detection. It also allows targeting specific types of
changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection
method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within
the object and a set of training data. More emphasis is put on the building edges to capture the structural damage
in quantifying change after disaster. Once the change is quantified, based on training data, the method can be
used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis
over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby
giving managers a better understanding of productive management and recovery practices. The recovery and
monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative
boundaries over time.