RASOR QGIS plugins
EO-based exposure feature extraction plugin
The RASOR project includes improved products coming from other FP7 risk-related projects like SENSUM. The synergy among all these products is fundamental in order to provide the end-users with a set of consistent products. A QGIS plugin along with source code is presented; the algorithms are capable to extract vulnerability indicators both from medium- and very-high-resolution optical datasets, e.g. built-up areas and building footprints .
A user and developer guide including code syntax is available here.
|Medium Resolution||High Resolution|
|Unsupervised built-up area||Building footprints|
|Age of built-up area||Building height|
The table below summarizes the list of algorithms available from the QGIS plugin. A more detailed explanation of the techniques follows.
Medium-resolution imagery, in particular Landsat datasets, represent a very important source of information. The capability to cover large areas with a good level of detail combined with the rich archive and the open-data policy are among the main advantages. The algorithms working on medium resolution are in the process of being included into the ESA Grid Processing On Demand (G-POD) and Cloud Toolbox systems.
Unsupervised built-up area extraction
Urban area monitoring is a key-point for the vulnerability of an area of interest. The tool provided is capable to detect built-up areas with very limited user intervention . Different methodologies are proposed to leave more choice to the end users and cover the different challenges found in test-cases. An example of the obtained results is shown in Figure 1.
Age of built-up areas
Built-up areas in developing countries are generally in a process of significant expansion, leading to changes in the vulnerability of the area. The revisit time of satellites gives a helpful contribution to monitor the evolution of urban areas. Newly developed areas can be highlighted and further tracked leading to a constantly-updated status of the region of interest . An example of the process is displayed in Figure 2.
High-resolution optical imagery provides a closer look to the area of interest, offering a chance to extract per-building vulnerability indicators. Unfortunately, very high-resolution datasets generally do not come with open data policies.
A formerly developed semi-automatic algorithm is capable of detecting and extracting footprints from very high-resolution imagery. Some user skills are required to define a training set for the supervised classification. A supervised approach was selected because it has proved more reliable in respect of a pure-unsupervised one. The outcome of the classification is also used for two more indicators: roof type and building height. An example is shown in Figure 3.
A renowned approach to calculating the height of buildings is based on the shadow length. The supervised classification outcome of the building footprints extraction includes a class related to shadows. The proposed algorithm is able to combine footprints and shadows with the satellite acquisition time. Height computed from shadows is automatically associated to footprints during the process without any user intervention.
Shape regularity of buildings is computed as a by-product of the extracted footprints. Calculation is based on the ratio between lengths of the two main directions of the building: a building is considered irregular if the ratio exceeds 4.
The outcome of the supervised classification mentioned earlier can be useful to determine the roof material. For example, red roofs are made of tiles with high probability; cement flat roofs are instead more likely to appear grey.
Building footprints represent a fundamental input for the determination of density. The proposed algorithm is capable of processing footprints and determining the density in a radius defined by the user. An example is shown in Figure 4.
 M. Harb, D. De Vecchi, F. Dell'Acqua "Phisical Vulnerability Proxies from Remotes Sensing: Reviewing, Implementing and Disseminating Selected Techniques", Geoscience and Remote Sensing Magazine, IEEE (Volume:3 , Issue: 1 ), pp.20-33, March 2015.
 D. De Vecchi, M. Harb, D. A. Galeazzo, F. Dell'Acqua "Exposure Monitoring from Optical Earth Observation data: an Open-Source and integrated set of Tools", Earth Observation Open Science 2.0, 10-12 October 2015, Frascati, Rome, Italy.
 M. Harb, F. Dell'Acqua, D. De Vecchi "Multi-Risk building exposure and physical vulnerability mapping from optical satellite images: developing an integrated toolset", Geoscience and Remote Sensing Symposium, 2014 IEEE International, pp.1164-1166, Quebec City, QC, 2014.
 M. Harb, D. De Vecchi, F. Dell'Acqua "Automatic hybrid-based built-up area extraction from Landsat 5, 7, and 8 data sets", Urban Remote Sensing Event (JURSE), 2015 Joint, pp.1-4, Lausanne, Switzerland, 2015.
 D. De Vecchi, M. Harb, F. Dell'Acqua "A PCA-based hybrid approach for built-up area extraction from Landsat 5, 7 and 8 datasets", Geoscience and Remote Sensing Symposium, 2015 IEEE International, Milan, Italy, 2015.
 D. De Vecchi, D. A. Galeazzo, M. Harb, F. Dell'Acqua "Unsupervised change detection for urban expansion monitoring: an object-based approach", Geoscience and Remote Sensing Symposium, 2015 IEEE International, Milan, Italy, 2015.
Flood Map Detection plugin
RASOR Layers QGIS plug-in
The RASOR QGIS plug-in was also expanded from the previous release. It is now possible to:• download a layer form the rasor catalog;