AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning

Abstract

A key challenge of supervised learning is the avail-ability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It isbased on rasterized statistical features extracted from surveys such as e.g. LiDAR measurements. Using simple combinations ofthe rasterized statistical layers, it is demonstrated that multipleclasses can be generated at accuracies of∼0.9. As proof of concept, we utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas with multiple land cover classes. The general method proposed here is platform independent, and it can be adapted to generate labels for other satellite modalities in order to enable machine learning on overhead imagery for land use classification and object detection