A machine learning evaluation of aerial light hour for classification of terrestrial and urban structures. In activities such as urban planning or military operations, 3D models of the environment can be indispensable tools. One such model is a point cloud. This is a set of points in three-dimensional space that models the surface of the terrain. All these points together can give a high resolution representation of the landscape. A point cloud such as this can be generated by a light hour sensor. When mounted on an aircraft, the sensor measures the distance to the ground using lasers. From this distance, a latitude-longitude elevation coordinate can be extrapolated, giving us a point in space. By repeating this process, a point cloud is formed. The problem now is identifying objects in the point cloud. As a human, it is fairly easy to see that this is a building and that this is the ground. However, these datasets can contain billions of points, so manually labeling each point would be tedious and time-consuming. Therefore, there is a need for a faster and automated way to label these points. And that's where we come in. Our goal was to evaluate the effectiveness of a variety of machine learning classification algorithms at identifying objects of interest in point clouds. These objects include bare earth, buildings, cars, trees, bushes, and power lines. First, we had to determine how to tackle the problem. The approach used in this project was a hierarchy of all versus one classifications, filtering out points at each level. For example, first separating the data into bare earth and not bare earth. Then further separating not bare earth into either trees or not trees, continuing in this manner until every point has been given a label. Working towards this end, we tested our classifiers on several different binary problems. Because a few attempts have been made to classify light-art point clouds using features solely based on positions in space, we began with some common and simple classifiers before moving on to more complex algorithms. At first, we tried several binary classifiers, namely Bays, Linear Describinent Analysis, or LDA, and Quadratic Describinent Analysis, or QDA. These classifiers were trying to find the line that best separates the data. Here's a histogram of the data projected into the optimal one-dimensional space by LDA. If the data were linearly separable, there would be no overlap between the two colors. However, there is significant overlap between the distributions, indicating that the data is not linearly separable, so these classifiers will not perform very well. Next, we looked into using kernel methods. Kernels are tools that can be used to add non-linearity to an algorithm. By using kernels with LDA or QDA, these classifiers attempt to find a non-linear boundary for separating our data. Even with the addition of kernels, our classifiers still were unable to separate the data. This leads us to conclude that either more complex classifiers are needed, or the different features should be investigated.