First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data

Geospatial data treatment is an important task since it is a big part of big data. Nowadays, geospatial data exploitation is lacking in terms of artificial intelligence. In this work, we focus on the usage of an machine learning models to exploit a geospatial data. We will follow a complete workflow from the collection and first descriptive analysis of the data to the preprocess and evaluation of the different machine learning algorithms. From unload dataset we will predict if the unload will lead to civil work, in other words, it is a classification problem. We conclude that combining machine learning and geospatial data we can get a lot out of it.