Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery
Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery
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DOI：
10.1007/s00521-020-05449-7
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发表时间：
2020-10-27
影响因子：
6
通讯作者：
Kemper, Thomas
中科院分区：
文献类型：
--
作者：
Corbane, Christina;Syrris, Vasileios;Kemper, Thomas
在空间上保持一致和最新的人类住区地图对于处理与城市化和可持续性有关的政策至关重要，特别是在世界日益城市化的时代。哥白尼地球观测方案的哨兵-2号开放和免费数据的提供为在全球范围内全面绘制人类住区地图提供了新的机会。本文提出了一个基于深度学习的框架，用于从Sentinel-2图像的全球复合图像中以10 m的空间分辨率全自动提取建成区。一个多神经元建模方法建立在一个简单的卷积神经网络架构的像素图像分类建成区。所提出的模型的核心特征是大小为5 x 5像素的图像块，足以描述Sentinel-2图像的建成区域，以及具有总数为1，448，578个可训练参数和4个2D卷积层和2个扁平层的轻量级拓扑结构。在全球哨兵-2图像合成图上部署该模型，提供了关于2018参考年建成区的最详细和最完整的地图报告。使用覆盖世界各地277个站点的建筑足迹的独立参考数据集对结果进行验证，建立了由所提出的框架和模型鲁棒性产生的构建层的可靠性。这项研究的结果有助于利用遥感数据自动绘制建成区地图领域的前沿研究，并为分析城乡连续体中人类住区的空间分布建立了一个新的参考层。
Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 x 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural-urban continuum.