
In addition, the algorithms allow massive datasets to be processed more efficiently and rapidly, making it possible to monitor the changes in human settlements regularly and equally importantly, to collect the same information from heterogeneous satellite data. This can help to reduce risks in areas that experience recurrent disasters and to focus post-disaster humanitarian interventions on the most likely populated places in disaster affected countries and regions.
The European Commission's Joint Research Centre, in collaboration with the European Space Agency's (ESA) Earth Observation Ground Segment Department (EOP-G) has produced the first prototype of a new Global Human Settlement Layer (GHSL) using European radar satellite (ENVISAT) capacity and advanced automatic pattern recognition algorithms.
One of the major problems in disaster-struck areas in less developed countries is the lack of relevant and up to date pre-disaster information that can help to quickly locate and assess the type and extent of damage, especially in populated places. The GHSL will help to focus damage analysis very quickly over populated places, leading to improvements in emergency rescue and humanitarian relief operations.
The GHSL will help to improve the quantification of the building stock which is valuable information both for risk assessment activities and for emergency rescue operations. As the building stock is an indicator of human presence, this critical piece of information on population (often lacking in remote areas) can help the first responder communities to focus their efforts in a particular area…
An 18th century map of an African village. From Antoine-François Prevost d'Exiles: Histoire generale des voyages ou nouvelle collection de toutes les relations de voyages par met et par terre..., Paris: Didot 1749–1758.
No comments:
Post a Comment