Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source,multi-temporal,and multi-scale earth observation data.In this paper,the latest d...Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source,multi-temporal,and multi-scale earth observation data.In this paper,the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer(GHSL)data are presented.Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre Earth Observation Data and Processing Platform.A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope.The paper presents the processing workflows and the results of the two main experiments,giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat data-sets,and the lessons learnt in terms of handling and processing big earth observation data.展开更多
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
基金This work is supported by two administrative arrangements with the Directorate General of Internal Market,Industry,Entrepreneurship and SME’s(GROWTH)and the Directorate General for Regional and Urban Policy of the European Commission(REGIO).
文摘Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source,multi-temporal,and multi-scale earth observation data.In this paper,the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer(GHSL)data are presented.Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre Earth Observation Data and Processing Platform.A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope.The paper presents the processing workflows and the results of the two main experiments,giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat data-sets,and the lessons learnt in terms of handling and processing big earth observation data.
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.
