In response to challenges in land cover classification (LCC), many researchers have experimented recently with classification methods based on artificial intelligence techniques. For LCC mapping of the vegetated Asahi River in Japan, the current study uses deep learning (DL)-based DeepLabV3+ module for image segmentation of aerial photographs. We modified the existing model by concatenating data on its resultant output port to access the airborne laser bathymetry (ALB) dataset, including voxel-based laser points and vegetation height (i.e. digital surface model data minus digital terrain model data). Findings revealed that the modified approach improved the accuracy of LCC greatly compared to our earlier unsupervised ALB-based method, with 25 and 35% improvement, respectively, in overall accuracy and the macro F1-score for November 2017 dataset (no–leaf condition). Finally, by estimating flow-resistance parameters in flood modelling using LCC mapping-derived data, we conclude that the upgraded DL methodology produces better fit between numerically analyzed and observed peak water levels.

为了应对土地覆盖分类 (LCC) 的挑战，许多研究人员最近尝试了基于人工智能技术的分类方法。对于日本植被茂盛的朝日河的 LCC 制图，目前的研究使用基于深度学习 (DL) 的 DeepLabV3+ 模块对航空照片进行图像分割。我们通过在其结果输出端口上连接数据来修改现有模型，以访问机载激光测深（ALB）数据集，包括基于体素的激光点和植被高度（即数字表面模型数据减去数字地形模型数据）。结果表明，与我们早期的基于 ALB 的无监督方法相比，改进后的方法大大提高了 LCC 的准确性，分别提高了 25% 和 35%，2017 年 11 月数据集（无叶条件）的整体准确性和宏观 F1 分数。最后，通过使用 LCC 映射衍生数据估计洪水建模中的流动阻力参数，我们得出结论，升级后的 DL 方法在数值分析和观察到的峰值水位之间产生了更好的拟合。