The problem I am exploring right now is a binary classification problem about classifying road intersections into roundabouts or not roundabouts. The available input data consists of the GPS latitude / longitude points contained inside the intersection polygons. So each sample contains a list of GPS points that we know that are contained in the intersection.

As such, I am interested in Machine Learning / Deep Learning techniques for classifying geospatial vector data specifically (as opposed to raster data). I've searched the web quite a bit and it seems to me that most of the ML research on geospatial data focuses on raster data. The only paper researching learning techniques applied on geospatial vector data I found is this: https://arxiv.org/abs/1806.03857, which refers to Polygon data, not Points.

I was considering taking the (projected and scaled) point coordinates as features, but since each intersection contains a different number of points, the feature vectors will have variable-length. My question is, how do I go about feature engineering in this case? I suspect that simply taking the point coordinates and zero-padding until the feature vectors have a fixed length, isn't going to work, due to the dimensionality curse, especially given that I only have about 800 intersection samples.


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