MASADA - Massive Spatial Automatic Data Analytics
- 1. Joint Research Centre (JRC) European Commission
Description
MASADA 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 resolution sensors like GeoEye-1, WorldView-2/3, Pléiades and Quickbird. The software is designed for the processing of single scenes but also for batch processing of large data sets.
Related materials
This resource is associated with the Earth Observations Toolkit for Sustainable Cities and Human Settlements. If you want to learn more about it, please, check the EO Toolkit portal (https://eotoolkit.unhabitat.org/), a place where you will find use cases, learning material, and many other tools and resources related to the Sustainable Development Goal 11 and New Urban Agenda.