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

• URL: http://arxiv.org/abs/2012.06907v1
• Date: Sat, 12 Dec 2020 21:12:41 GMT
• Title: PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data
• Authors: Wang Zhou, Levente J. Klein, Siyuan Lu
• Abstract summary: An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The framework gathers required data at the location coordinates, assembles the training data, performs quality check, and trains multiple machine learning models for subsequent deployment. This use case exemplifies how PAIRS AutoGeo enables users to leverage machine learning without extensive geospatial expertise.
• Score: 7.742399489996169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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