Detection of spatial change is indispensable in applications such as autonomous driving. In this paper, we present a method to detect spatial changes from a previously known sensor map of an environment using a suite of radar sensors mounted on a vehicle. In particular, this paper proposes a technique to detect a change in the position of a semi-static pole from the last measurement. We focus on feature construction as well as a supervised learning, trained using the respective features which describe the statistical similarity between the known map $(M)$ and current radar sensor scan $(S)$ of the same environment. In our experiments, we assessed different classification methods and feature configurations. Here, the support vector machine (SVM) trained using a combination of six statistical similarity features outperformed its competitors in classifying the change in position of a semi-static pole from the previously known sensor map $(M)$ with an F1 score of 0.87.