A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs
A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs
复制标题
基于深度学习 CNN 模型的多尺度测绘方法，用于重建高分辨率城市 DEM
DOI：
10.3390/w12051369
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发表时间：
2020-05-01
期刊：
影响因子：
3.4
通讯作者：
Kabir, Syed Rezwan
中科院分区：
文献类型：
--
作者：
Jiang, Ling;Hu, Yang;Kabir, Syed Rezwan
高分辨率城市数字高程模型（DEM）数据集的稀缺，特别是在某些发展中国家，对洪水风险管理等许多与水有关的应用提出了挑战。解决这个问题的一个方法是开发有效的方法来重建高分辨率的DEM，从他们的低分辨率的等价物，更广泛地提供。然而，目前的高分辨率DEM重建方法主要集中在自然地形。城市地形通常是复杂的人工和自然特征的结合，很少有人对城市地形进行尝试。提出了一种基于卷积神经网络（CNN）的多尺度映射方法，以处理复杂的城市地形特征，重建高分辨率的城市DEM。提出的多尺度CNN模型首先使用包含不同分辨率地形特征的城市DEM进行训练，然后用于从低分辨率等效物重建指定（高）分辨率的城市DEM。两个层次的精度评估方法也被设计来评估所提出的城市DEM重建方法的性能，在数值精度和形态精度。建议的DEM重建方法被应用到一个121平方公里的城市化地区在伦敦，英国。与其他常用的方法相比，目前基于CNN的方法产生了上级的结果，提供了一个具有成本效益的创新方法，以获取高分辨率DEM在其他数据稀缺的地区。
The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.