Automated global delineation of human settlements from 40 years of Landsat satellite data archives
Automated global delineation of human settlements from 40 years of Landsat satellite data archives
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DOI：
10.1080/20964471.2019.1625528
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发表时间：
2019-04-03
期刊：
影响因子：
4
通讯作者：
Soille, Pierre
中科院分区：
文献类型：
--
作者：
Corbane, Christina;Pesaresi, Martino;Soille, Pierre
本文介绍了对1975年至2014年间收集的地球观测数据记录的分析，以评估全球人类住区层项目框架内建筑物表面的范围和时间演变。这项研究所产生的信息规模之大，使人们能够对从农村小村庄到特大城市的整个人类住区连续体进行评估。与第一次编制GHSL基线数据相比，该研究采用了改进的处理方法。主要的改进包括在Sentinel-1数据的构建区域上使用更精细的学习集，从而可以测试增量学习在大数据分析中的附加值。在这里，GHSL组合网格和方法的新功能进行了描述，并与以前的使用一组参考建筑物的足迹为277个感兴趣的领域。结果表明，GHSL基线的第一次生产和最新的GHSL多时相组合网格之间的平衡精度增益为3.6%的精度措施逐步改善。在全球范围内建立跨时间的建成层的可靠性，也进行了验证的多时间组件。
This paper presents the analysis of Earth Observation data records collected between 1975 and 2014 for assessing the extent and temporal evolution of the built-up surface in the frame of the Global Human Settlement Layer project. The scale of the information produced by the study enables the assessment of the whole continuum of human settlements from rural hamlets to megacities. The study applies enhanced processing methods as compared to the first production of the GHSL baseline data. The major improvements include the use of a more refined learning set on built-up areas derived from Sentinel-1 data which allowed testing the added-value of incremental learning in big data analytics. Herein, the new features of the GHSL built-up grids and the methods are described and compared with the previous ones using a reference set of building footprints for 277 areas of interest. The results show a gradual improvement in the accuracy measures with a gain of 3.6% in the balanced accuracy, between the first production of the GHSL baseline and the latest GHSL multitemporal built-up grids. A validation of the multitemporal component is also conducted at the global scale establishing the reliability of the built-up layer across time.