Machine Learning on Historic Air Photographs for Mapping Risk of Unexploded Bombs
نویسندگان
چکیده
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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تاریخ انتشار 2005