Lake morphometric features like surface area, volume, mean, and maximum depth are important predictors of many physical, biological, and ecological processes. Lake bathymetric maps that present the lake basin contours are thus an integral part of limnological investigations. Accurate but cumbersome traditional bathymetric surveys measure the depth using a lead line or echosounder. Recently, airborne bathymetric mapping using imagery or laser scanning has been attempted in shallow freshwater and coastal habitats. However, these methods depend on the ability of light to penetrate the water column, which can be problematic in eutrophic lakes and shallow lakes. To alleviate these issues, we developed and tested a deep learning model (based on the U-net) using data from 153 lakes in Denmark to predict bathymetry using the topography of the surrounding terrain. The deep learning model performed much better (pixel-wise mean absolute error: validation set = 1.75 and test set = 2.15 m) than baseline interpolation approaches (validation set = 3.12 m). In addition, the deep learning model generated more realistic bathymetry maps that did not suffer from interpolation artifacts. We find that the model performance improves slightly with increasing model size (number of trainable parameters) and the extent of the surrounding terrain. In addition, our pretraining procedure improved performance and reduced the time for model convergence. Because the model only relies on digital elevation data which are widely available, it can be fine-tuned and used to predict lake bathymetry in other geographical regions.

中文翻译：

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使用深度学习根据周围地形的地形预测湖泊水深

湖泊形态特征（如表面积、体积、平均深度和最大深度）是许多物理、生物和生态过程的重要预测因子。因此，呈现湖盆轮廓的湖泊测深图是湖泊学研究的一个组成部分。准确但繁琐的传统测深测量使用引线或回声测深仪来测量深度。最近，已在浅水淡水和沿海栖息地尝试使用图像或激光扫描进行机载测深测绘。然而，这些方法取决于光穿透水柱的能力，这在富营养化湖泊和浅水湖泊中可能会出现问题。为了缓解这些问题，我们使用丹麦 153 个湖泊的数据开发并测试了深度学习模型（基于 U-net），以利用周围地形的地形来预测水深测量。深度学习模型的性能（逐像素平均绝对误差：验证集 = 1.75，测试集 = 2.15 m）比基线插值方法（验证集 = 3.12 m）好得多。此外，深度学习模型生成了更真实的测深地图，并且不受插值伪影的影响。我们发现，随着模型大小（可训练参数的数量）和周围地形范围的增加，模型性能略有提高。此外，我们的预训练过程提高了性能并减少了模型收敛的时间。由于该模型仅依赖于广泛可用的数字高程数据，因此可以对其进行微调并用于预测其他地理区域的湖泊水深测量。