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 La...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.展开更多
文摘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.
