The aim is to develop deep learning biodiversity modelling tools that can in theory predict the expected species diversity of any organism group, given a set of publicly available geospatial data-products. We train the model on biodiversity data of insects derived from a Sweden-wide environmental DNA (eDNA) inventory. By combining this data with spatial information such as temperature, precipitation, elevation, ground cover classification, NDVI, human impact indices etc., we can train a convolutional neural network to predict the expected number of insects at any given location.