论文标题
来自Sentinel-2卫星图像映射的全球人类定居点的卷积神经网络
Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery
论文作者
论文摘要
人类住区的空间一致和最新地图对于解决与城市化和可持续性有关的政策至关重要从Sentinel-2图像的全球复合物中进行10 m的空间分辨率。建立了一个简单的卷积神经网络建立，用于对像素的图像分类的简单卷积神经网络建筑，该方法是开发的。所提出的模型的核心特征是5 x 5 x 5 x 5 x 5 x 5 X型图像的图像斑块，以描述1 x 5 x 5 X型的图像。可训练的参数和4个2D卷积层和2个扁平的层。在全球Sentinel-2图像复合材料中，该模型的部署提供了有关2018年参考年度内置区域的最详细，最完整的地图报告。验证结果的验证，具有独立的参考数据集的建筑物的独立参考数据集，该建筑物覆盖了全球277个站点的架子，涵盖了跨越构建模型的构建层，并建立了构建层的层面层，并构建了构建层的层面层。
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 data-set 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.
