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 ML for Remote Sensing Usability Cards 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.