Home 2017-04-07T08:43:36+02:00

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  • Figure 1: EFFICACY OF MACHINE LEARNING AND LIDAR DATA FOR CROP TYPE MAPPING

EFFICACY OF MACHINE LEARNING AND LIDAR DATA FOR CROP TYPE MAPPING

By Atman Prins Accurate crop type maps are important for obtaining agricultural statistics such as water use or harvest estimations. The traditional approach to obtaining maps of cultivated fields is by manually digitising the [...]

TerraClim – StoryMap

We progressing well with the TerraClim project, and major milestone in the efficiency and accuracy of interpolations.The regional interpolation analysis divided the Western Cape study area into smaller regions using the local terrain and climatic [...]

Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms

by Atman Prins The second experiment used the methods developed for the first experiment to perform a five-class classification. The five classes consisted of maize, cotton, groundnuts, orchards and non-agriculture. Sentinel-2 and aerial imagery [...]

CURRENT PROJECTS / PUBLICATIONS

2020-10-01T09:56:25+02:00

Forest Mapping

Commercial plantations of introduced tree species provide most of the timber and fibre requirements of South Africa. However, not much is known about how much water is used for commercial forestry, how the [...]

2020-05-22T09:04:34+02:00

TerraClim

Winetech has appointed the Centre for Geographical Analysis (CGA) to develop a prototype online spatial decision support system (SDSS) for the wine industry. The project is being carried out in close collaboration with [...]