Unsupervised Extraction of Features a nd High Resolution Data Using AIMEE

Alexis Hanna Smith et all

IMGeospatial, Buckinghamshire, United Kingdom.

of poverty comes from the inability to evaluate and realise potential production from land and
to understand its vulnerability to influences, e.g. infrastructure and natural disasters. Quick and
accurate identification, measurement and evaluation of geogr aphical features over large areas give the capability to overcome poverty like never before.
Understanding what can be produced, where and by how much, is important to all commodities but in particular food. However, this must be looked at in the context o f the wider environment and
resources like water, timber and utilities as part of the sustainability of production.
Even when production systems are understood, they are vulnerable to changes in the environment, or natural disasters. These changes not only affect primary production but also the infrastructure
around it; removing resources, logistics and social support. Gathering large amounts of remote sensing data combined with AI such as AIMEE can increase productivity and its resilience.

Event: Land Governance in an Interconnected World_Annual World Bank Conference on Land and Poverty_2018

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Document type:Unsupervised Extraction of Features a nd High Resolution Data Using AIMEE (42 kB - pdf)