Abstract
We describe an automatic procedure for building risk maps of unexploded ordnances (UXO) based on historic air photographs. The system is based on a cost-sensitive version of AdaBoost regularized by hard point shaving techniques, and integrated by spatial smoothing. The result is a map of the spatial density of craters, an indicator of UXO risk.
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© 2005 Springer-Verlag Berlin Heidelberg
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Merler, S., Furlanello, C., Jurman, G. (2005). Machine Learning on Historic Air Photographs for Mapping Risk of Unexploded Bombs. In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, vol 3617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553595_90
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DOI: https://doi.org/10.1007/11553595_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28869-5
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