Using Non-authoritative Sources During Emergencies in Urban Areas - Computational Approaches for Urban Environments - page 330

The incorporation of multiple data sources or methods for improved performance
or increased accuracy is not limited to the field of remote sensing. Boosting, a
common machine learning technique, has been shown to be an effective method
for generating accurate prediction rules by combining rough, or less than accurate,
algorithms together (Freund et al. 1999 ). While the individual algorithms may be
singularly weak, their combination can result in a strong learner. Furthermore,
redundancies in observations provide an increase in the confidence of observations
or estimates, while data from multiple sources can provide information when they
might not do so if used in isolation.
Figure 14.1 shows the stacked layer approach used to fuse heterogeneous data at
different spatial, temporal, and radiometric resolutions. The data are first processed
in a GIS environment by resampling them at the highest resolution available using

Satellite

High Resolution Data

Aerial

DEM

Inundation Model

High Resolution Cells

Land

Fig. 14.1 Stacked layer approach used in the methodology. Data comes in different formats and

with different resolutions