In this short post, I would like to help you conduct your own machine learning classification of Sentinel-2 data using the open source package R. The process is pretty straightforward if you have experience in remote sensing and image classification. Even if you don’t have extensive experience, basic knowledge of remote sensing terminology is sufficient.
The field of machine learning is moving fast, and it seems that new fancy algorithms coming out every week. Sometimes, it is confusing to figure out which algorithms are best suited for which purpose. This is particularly the case when it comes to land-use and land-cover classification using multidimensional satellite imagery because most of the new algorithms are tested with either binary or uni-dimensional data.
A couple of years ago, my colleague and friend Emma Li Johansson approached me with an interesting proposition. Her idea was that the combination of remotely-sensed land cover and and land use change maps and participatory paintings of people’s perceptions of land change might provide useful information in understanding the drivers of change. She didn’t have to say any more because I was sold. It was a solid idea with an innovative interdisciplinary workflow and high potential to provide valuable outcomes.