National mapping agencies are responsible for creating and maintaining country wide geospatial datasets that are highly accurate and homogenous.The Netherlands’Cadastre,Land Registry and Mapping Agency,in short,the K...National mapping agencies are responsible for creating and maintaining country wide geospatial datasets that are highly accurate and homogenous.The Netherlands’Cadastre,Land Registry and Mapping Agency,in short,the Kadaster,has created a database of information related to solar installations,using GeoAI.Deep Learning techniques were employed to detect small and medium-scale solar installations on buildings from very highresolution aerial images for the whole of the Netherlands.The impact of data pre-processing and postprocessing are addressed and evaluated.The process was automatized to deal with enormous data and the method was scaled-up nation-wide with the help of cloud solutions.In order to make this information visible,consistent and usable,we built-upon the existing TernausNet;a convolution neural network(CNN)architecture.Model metrics were evaluated after post-processing.The algorithm when used in combination with automated or custom post-processing improves the results.The precision and recall rates of the model for 3 different regions were evaluated and are on average about 0.93 and 0.92 respectively after implementation of post-processing.Use of custom post-processing improves the results by removing the false positives by at least 50%.The final results were compared with the existing national PV register.Overall,the results are not only useful for policy makers to assist them to take the necessary steps in achieving the energy transition goals but also serves as a register for infrastructure planning.展开更多
文摘National mapping agencies are responsible for creating and maintaining country wide geospatial datasets that are highly accurate and homogenous.The Netherlands’Cadastre,Land Registry and Mapping Agency,in short,the Kadaster,has created a database of information related to solar installations,using GeoAI.Deep Learning techniques were employed to detect small and medium-scale solar installations on buildings from very highresolution aerial images for the whole of the Netherlands.The impact of data pre-processing and postprocessing are addressed and evaluated.The process was automatized to deal with enormous data and the method was scaled-up nation-wide with the help of cloud solutions.In order to make this information visible,consistent and usable,we built-upon the existing TernausNet;a convolution neural network(CNN)architecture.Model metrics were evaluated after post-processing.The algorithm when used in combination with automated or custom post-processing improves the results.The precision and recall rates of the model for 3 different regions were evaluated and are on average about 0.93 and 0.92 respectively after implementation of post-processing.Use of custom post-processing improves the results by removing the false positives by at least 50%.The final results were compared with the existing national PV register.Overall,the results are not only useful for policy makers to assist them to take the necessary steps in achieving the energy transition goals but also serves as a register for infrastructure planning.
