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"A Patch-Based Deep Learning Approach for Land-Classification of Sentinel-2 Satellite Imagery." D. Smith. M. Roper. Department of Computer and Information Sciences, University of Strathclyde. 2019. Download PDF (BibTeX) ACSBD

The application of machine learning to the remote sensing field has produced many exciting new advancements. One of these is in land-classification: identifying the land-cover content present in satellite imagery. The use of deep neural networks has provided stateof-the-art accuracies for land-cover classification. This project experiments with deep learning architectures and novel image handling to methods to improve the classification accuracies achievable by Global Surface Intelligence, a geospatial analytics company. By implementing segmentation and patch-based training, we successfully show that by expanding their current methodology to consider spatial content, they can achieve increased classification accuracies.