Abstract Positive vertical bias in elevation data derived from NASA's Shuttle Radar Topography Mission (SRTM) is known to cause substantial underestimation of coastal flood risks and exposure. Previous attempts to correct SRTM elevations have used regression to predict vertical error from a small number of auxiliary data products, but these efforts have been focused on reducing error introduced solely by vegetative land cover. Here, we employ a multilayer perceptron artificial neural network to perform a 23-dimensional vertical error regression analyses, where in addition to vegetation cover indices, we use variables including neighborhood elevation values, population density, land slope, and local SRTM deviations from ICESat altitude observations. Using lidar data as ground truth, we train the neural network on samples of US data from 1–20 m of elevation according to SRTM, and assess outputs with extensive testing sets in the US and Australia. Our adjustment system reduces mean vertical bias in the coastal US from 3.67 m to less than 0.01 m, and in Australia from 2.49 m to 0.11 m. RMSE is cut by roughly one-half at both locations, from 5.36 m to 2.39 m in the US, and from 4.15 m to 2.46 in Australia. Using ICESat data as a reference, we estimate that global bias falls from 1.88 m to −0.29 m, and RMSE from 4.28 m and 3.08 m. The methods presented here are flexible and effective, and can be effectively applied to land cover of all types, including dense urban development. The resulting enhanced global coastal DEM (CoastalDEM) promises to greatly improve the accuracy of sea level rise and coastal flood analyses worldwide.

中文翻译：

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CoastalDEM：使用神经网络从 SRTM 改进的全球沿海数字高程模型

摘要 众所周知，来自美国宇航局航天飞机雷达地形任务 (SRTM) 的高程数据的正垂直偏差会导致对沿海洪水风险和暴露的严重低估。以前纠正 SRTM 高程的尝试使用回归来预测少量辅助数据产品的垂直误差，但这些努力的重点是减少仅由植被覆盖引入的误差。在这里，我们采用多层感知器人工神经网络来执行 23 维垂直误差回归分析，其中除了植被覆盖指数之外，我们还使用了包括邻域高程值、人口密度、土地坡度和与 ICESat 高度的局部 SRTM 偏差在内的变量观察。使用激光雷达数据作为地面实况，我们根据 SRTM 在 1-20 m 海拔的美国数据样本上训练神经网络，并在美国和澳大利亚使用广泛的测试集评估输出。我们的调整系统将美国沿海的平均垂直偏差从 3.67 m 减少到小于 0.01 m，将澳大利亚从 2.49 m 减少到 0.11 m。两个位置的 RMSE 大约减少了一半，美国从 5.36 m 减少到 2.39 m，澳大利亚从 4.15 m 减少到 2.46。使用ICESat数据作为参考，我们估计全球偏差从1.88 m下降到-0.29 m，RMSE从4.28 m和3.08 m下降。这里介绍的方法灵活有效，可以有效地应用于所有类型的土地覆盖，包括密集的城市开发。由此产生的增强型全球沿海 DEM (CoastalDEM) 有望大大提高全球海平面上升和沿海洪水分析的准确性。