Semantic Segmentation with Neural Networks in Environment Monitoring

Abstract

The Finnish Environment Institute (SYKE) has at least two missions which require surveying large land areas: finding invasive alien species and monitoring the state of Finnish lakes. Various methods to accomplish these tasks exist, but they traditionally rely on manual labor by experts or citizen activism, and as such do not scale well. This thesis explores the usage of computer vision to dramatically improve the scaling of these tasks. Specifically, the aim is to fly a drone over selected areas and use a convolutional neural network architecture (U-net) to create segmentations of the images. The method performs well on select biomass estimation task classes due to large enough datasets and easy-to-distinguish core features of the classes. Furthermore, a qualitative study of datasets was performed, yielding an estimate for a lower bound of number of examples for an useful dataset. ACM Computing Classification System (CCS): CCS → Computing methodologies → Machine learning → Machine learning approaches → Neural networks