MS Defense: Usable Machine Learning for Remote Sensing Data

The desired output for most real-world tasks using machine learning (ML) and remote sensing data is a set of dense predictions that form a predicted map for a geographic region. However, most prior work involving ML and remote sensing follows the traditional practice of reporting metrics on a set of independent, geographically-sparse samples and does not perform dense predictions. To reduce the labor of producing dense prediction maps, we present OpenMapFlow---an open-source python library for rapid map creation with ML and remote sensing data. OpenMapFlow provides 1) a data processing pipeline for users to create labeled datasets for any region, 2) code to train state-of-the-art deep learning models on custom or existing datasets, and 3) a cloud-based architecture to deploy models for efficient map prediction. We demonstrate the benefits of OpenMapFlow through experiments on three binary classification tasks: cropland, crop type (maize), and building mapping. We show that OpenMapFlow drastically reduces the time required for dense prediction compared to traditional workflows. To more broadly understand method adoption we present a framework to assess usability for machine learning with remote sensing data and use this framework to conduct a case study of a workflow developed with OpenMapFlow. We hope this library will stimulate novel research in areas such as domain shift, unsupervised learning, and societally-relevant applications and along with the usability framework lessen the barrier to adopting research methods for real-world tasks.

Examining Committee

Chair:

Dr. Abhinav Shrivastava

Members:

Dr. Hannah Kerner (Arizona State University)

Dr. Hal Daumé