Remote Sensing Education for Life Long Learning across the East Midlands Region
TL;DRAbstract
Over the last decade, remote sensing technologies have provided useful tools for a diverse range of sectors. G-STEP (GMES—space technology exchange partnership) is the University of Leicester’s flagship project for innovation, training and education in GMES (global monitoring for environment and security) and GIS (geographical information systems) based technologies. G-STEP (part funded by the ERDF) is the first GMES accelerator initiative in the UK and is breaking new ground in the application of GMES and GIS. The core G-STEP team initiates and develops Knowledge Exchange Practices between the University and Life Long Learning partners in the East Midlands Region, through training packages and collaborative research projects. The programme integrates innovative research, to direct downstream applied training, supported by analytical processing of attendee assessment data. Collated information derived from these sources presents a unique opportunity to incorporate a statistically viabl
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Over the last decade, remote sensing technologies have provided useful tools for a diverse range of sectors. G-STEP (GMES—space technology exchange partnership) is the University of Leicester’s flagship project for innovation, training and education in GMES (global monitoring for environment and security) and GIS (geographical information systems) based technologies. G-STEP (part funded by the ERDF) is the first GMES accelerator initiative in the UK and is breaking new ground in the application of GMES and GIS. The core G-STEP team initiates and develops Knowledge Exchange Practices between the University and Life Long Learning partners in the East Midlands Region, through training packages and collaborative research projects. The programme integrates innovative research, to direct downstream applied training, supported by analytical processing of attendee assessment data. Collated information derived from these sources presents a unique opportunity to incorporate a statistically viabl
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