Object- versus pixel-based building detection for disaster response

Recent disasters have shown that there is a growing interest for remotely sensed data to support decision makers and emergency teams in the field. Fast and accurate detection of buildings and sustained damage is of great importance. Current methods rely on numerous photo-interpreters to visually analyze the data. Multiple pixel-based methods exist to classify pixels as being part of a building or not but results vary widely and precision is often poor with very high resolution images. This paper proposes an object-based solution to building detection and compares it to a traditional approach. Object-based classification clearly provides adequate results in much less time and thus is ideal for disaster response.