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We describe a technique for an Autonomous Surface Vehicle (ASV) to learn an obstacle map by classifying overhead imagery. Classification labels are supplied by a front-facing sonar, mounted under the water line on the ASV. We use aerial imagery from two online sources for each of two water bodies (a small lake and a harbor) and train classifiers using features generated from each image source separately, followed by combining their output. Data collected using a sonar mounted on the ASV weredoi:10.1109/iros.2011.6094610 dblp:conf/iros/HeidarssonS11 fatcat:l2avyjuk4bfn7ihvw4sz27j24q