Spatial Modelling

GEOTERRAIMAGE employs the latest spatial and non-statistical modelling techniques to extract business intelligence from earth observed imagery and ancillary datasets. GEOTERRAIMAGE was an early adopter of machine learning and deep learning methods, as well as advancing its modelling capacity and scalability using cloud technologies. In addition to hosting the latest proprietary spatial software to enhance desktop modelling capabilities, GEOTERRAIMAGE also makes use of strong data science development programming languages (such as R and Python) to build custom modelling scripts and programs that are focused and product/project specific, and deployable to the cloud. These advanced methods only form part of larger business systems and have been incorporated into traditional spatial and non-spatial modeling methods.

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.

GEOTERRAIMAGE employs the latest spatial and non-statistical modelling techniques to extract business intelligence from earth observed imagery and ancillary datasets. GEOTERRAIMAGE was an early adopter of machine learning and deep learning methods, as well as advancing its modelling capacity and scalability using cloud technologies. In addition to hosting the latest proprietary spatial software to enhance desktop modelling capabilities, GEOTERRAIMAGE also makes use of strong data science development programming languages (such as R and Python) to build custom modelling scripts and programs that are focused and product/project specific, and deployable to the cloud. These advanced methods only form part of larger business systems and have been incorporated into traditional spatial and non-spatial modeling methods.

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.

TurbineVu360

This innovative tool assesses the visual impact of wind farms in a given landscape. As an essential component of public participation in developing renewable energy projects, this data-driven assessment tool promotes transparency and efficiency. TurbineVu 360, is an advanced spatial analytics solution, which evaluates visual impact of numerous turbines in existing and planned wind farms. It is automated and scalable, delivers rapid results, and eliminates the need for in-field assessments. Given a set of turbine locations and tower/blade measurements, a detailed report is generated along with spatial intelligence of numbers of turbines in view. Read the product sheet for more information.

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