Webinar 14 - Crop mapping in Zankalon, Egypt using different ML classifiers in Python

Accurate information about agriculture and crops is essential for informed decision-making in various applications. While various methods can be employed to collect such data, satellite earth observation emerges as a favourable approach due to its extensive coverage and diverse data types. The European Commission initiated the Copernicus program a few years ago, introducing a novel series of Earth Observation satellites called the Sentinels. These satellites, part of a new generation of remote sensing technology, play a crucial role in enhancing the observation, identification, mapping, assessment, and monitoring of crop dynamics across different spatial and temporal resolutions.
In this webinar we will investigate different crop types in selected agriculture area in Zankalon, Egypt. During the webinar we will demonstrate the capacity of application of most commonly used machine learning algorithms to identify crop types with EO data with use of Python available within Innovation Lab.
The introduction and overview of the machine learning algorithms and classifiers will be provided with a special focus on supervised ML methods such as: Random Forest, Support Vector Machines and unsupervised ML methods such as K-means.