Remote sensing machine learning algorithms in environmental stress detection: Case study of Pan-European south section of Corridor 10 in Serbia

Abstract

The construction of the Pan-European Corridor 10 is one of the major projects in the Republic of Serbia, and it enters the final phase. A vast natural area suffered a significant change to complete the project and therefore is the existence of a need to monitor those changes. Nature requires adequate and accurate detection of environmental stresses which inevitably arise after implementation of such large construction projects. Conversely to traditional field monitoring of the environment, this paper will present the remote sensing method which includes usage of European Space Agency's Sentinel 2A optical satellite data processed with different Machine Learning algorithms. An accuracy assessment is performed on land cover map results, and change detection carried out with best resulting data.

Keywords

• Environment Monitoring
• Gaussian Mixture Model
• Random Forest
• K-Nearest Neighbors
• Confusion Matrix