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Machine learning on historic air photographs for mapping risk of unexploded bombs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2005) 3617 LNCS 735-742
DOI: 10.1007/11553595_90
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Abstract

We describe an automatic procedure for building risk maps of unexploded ordnances (UXO) based on historic air photographs. The system is baaed 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. © Springer-Verlag Berlin Heidelberg 2005.

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APA

Merler, S., Furlanello, C., & Jurman, G. (2005). Machine learning on historic air photographs for mapping risk of unexploded bombs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3617 LNCS, pp. 735–742). https://doi.org/10.1007/11553595_90

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