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.

中文翻译：

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机载激光雷达渔业调查中基于机器学习的感兴趣区域检测

用于渔业调查的机载激光雷达数据通常不包含可用于识别鱼类的基于物理的特征；因此，必须人工识别鱼，这是一个耗时的过程。为了减少识别鱼类所需的时间，监督式机器学习已成功应用于渔业调查的激光雷达数据，以自动化识别含有鱼类的高概率区域的过程。使用来自黄石湖和墨西哥湾的数据，进行了多次实验来模拟真实世界的场景。尽管人类无法完全从循环中移除，但在黄石湖和墨西哥湾数据集中需要人工检查的数据量分别减少了 61.14% 和 26.8%。