Machine learning-based region of interest detection in airborne lidar fisheries surveys

Abstract. Airborne lidar data for fishery surveys often do not contain physics-based features that can be used to identify fish; consequently, the fish must be manually identified, which is a time-consuming process. To reduce the time required to identify fish, supervised machine learning was successfully applied to lidar data from fishery surveys to automate the process of identifying regions with a high probability of containing fish. Using data from Yellowstone Lake and the Gulf of Mexico, multiple experiments were run to simulate real-world scenarios. Although the human cannot be fully removed from the loop, the amount of data that would require manual inspection was reduced by 61.14% and 26.8% in the Yellowstone Lake and Gulf of Mexico datasets, respectively.

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