Title: MASADA USER GUIDE
Authors: POLITIS PANAGIOTISCORBAN CHRISTINAMAFFENINI LUCAKEMPER THOMASPESARESI MARTINO
Publisher: Publications Office of the European Union
Publication Year: 2017
JRC N°: JRC106667
ISBN: 978-92-79-68865-2
ISSN: 1831-9424
Other Identifiers: EUR 28609 EN
OP KJ-NA-28609-EN-N
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC106667
DOI: 10.2760/522941
Type: EUR - Scientific and Technical Research Reports
Abstract: This user guide accompanies the MASADA tool which is a public tool for the detection of built-up areas from remote sensing data. MASADA stands for Massive Spatial Automatic Data Analytics. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the production of settlement layers at regional scale, by processing high and very high resolution satellite imagery. The tool builds on the Symbolic Machine Learning (SML) classifier; a supervised classification method of remotely sensed data which allows extracting built-up information using a coarse resolution settlement map or a land cover information for learning the classifier. The image classification workflow incorporates radiometric, textural and morphological features as inputs for information extraction. Though being originally developed for built-up areas extraction, the SML classifier is a multi-purpose classifier that can be used for general land cover mapping provided there is an appropriate training data set. The tool supports several types of multispectral optical imagery. It includes ready-to-use workflows for specific sensors, but at the same time, it allows the parametrization and customization of the workflow by the user. Currently it includes predefined workflows for SPOT-5, SPOT-6/7, RapidEye and CBERS-4, but it was also tested with various high and very high resolution1 sensors like GeoEye-1, WorldView-2/3, Pléiades and Quickbird.
JRC Directorate:Space, Security and Migration

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