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

PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data