Skip to main content
Conference ProceedingsOPEN ACCESS

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
17Citations
Citations of this article
20Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free