Spatial Modelling
Non spatial modeling techniques and statistical modeling techniques are frequently used in conjunction with spatial modeling to provide insight into data and allow GEOTERRAIMAGE to model and predict various processes, for example income levels, population, traffic flow behavior, land use change, water demand and risk indexes. Using machine learning techniques has allowed GEOTERRAIMAGE to advance land cover and land use class recognition and classification within satellite and aerial imagery, and non-spatial data has been modelled into imagery classifications to enrich the result into tangible business information for a number of business verticals related to agriculture, natural resources and urban dynamics.
Non spatial modeling techniques and statistical modeling techniques are frequently used in conjunction with spatial modeling to provide insight into data and allow GEOTERRAIMAGE to model and predict various processes, for example income levels, population, traffic flow behavior, land use change, water demand and risk indexes. Using machine learning techniques has allowed GEOTERRAIMAGE to advance land cover and land use class recognition and classification within satellite and aerial imagery, and non-spatial data has been modelled into imagery classifications to enrich the result into tangible business information for a number of business verticals related to agriculture, natural resources and urban dynamics.
