  An automated machine learning framework for geospatial data named PAIRS
AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform.
The framework simplifies the development of industrial machine learning
solutions leveraging geospatial data to the extent that the user inputs are
minimized to merely a text file containing labeled GPS coordinates. PAIRS
AutoGeo automatically gathers required data at the location coordinates,
assembles the training data, performs quality check, and trains multiple
machine learning models for subsequent deployment. The framework is validated
using a realistic industrial use case of tree species classification.
Open-source tree species data are used as the input to train a random forest
classifier and a modified ResNet model for 10-way tree species classification
based on aerial imagery, which leads to an accuracy of $59.8\%$ and $81.4\%$,
respectively. This use case exemplifies how PAIRS AutoGeo enables users to
leverage machine learning without extensive geospatial expertise.
