Machine learning methods for classification of the green infrastructure in city areas

Abstract

Reducing green urban infrastructure pose a huge threat to cities sustainability. It is important to monitor and track the health of vegetation. For efficient planning of urban development and maintenance of green urban infrastructure, the key is to have up to date maps. Using satellite imagery is the easiest way to cover large city areas. In order to map vegetation, there are many possible solutions; however, the most effective way is using machine learning methods. Machine learning is divided into supervised and unsupervised classification and each can be divided into several different methods. Many authors have considered different methods and they use them to access accuracy on satellite image information extraction. They have used different satellite images and naturally higher resolution imagery results in better classification. However, there is still lack of comprehensive analysis of more supervised machine learning methods in similar cities. This paper aims to provide analysis of four different machine learning methods: support vector machine, artificial neural network, naïve Bayes and random forest. The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space that distinctly classifies the data points where hyperplanes are decision boundaries that help to classify the data points and support vectors data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Artificial neural networks are brain-inspired systems which are intended to replicate the way that humans learn. Neural networks consist of input and output layers, and hidden layers. They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize. A Naive Bayes classifier is a probabilistic machine learning model that is used for classification task and the crux of the classifier is based on the Bayes theorem. Random Forest creates a forest and makes it random and is an ensemble of Decision Trees, most of the time trained with the bagging method which general idea is that a combination of learning models increases the overall result. All of the mentioned methods will be tested on Sentinel-2A imagery. Sentinel-2A multispectral imager has thirteen sensors which is useful in vegetation extraction. Methods will be compared using error matrix and kappa statistics.