Distributed Inference and API Hosting for an Image Analysis Service: A Case Study on Land Cover Mapping

The AI for Earth Land Cover Mapping project aims to produce a high-resolution land cover map of the United States using machine learning and computer vision. In addition to the core algorithmic challenges facing related to training ML models on large geospatial image sets, additional challenges arise when trying to expose the resulting models to end users (e.g., geospatial analysts). Developing APIs to make ML models widely available requires expertise distinct from the underlying geospatial processing and machine learning, and even for experienced developers, maintaining a real-time inference system is both cumbersome and expensive. Consequently, models described in the literature or made available in binary form often remain inaccessible to end users, who may be unfamiliar with machine learning and/or lack the computational resources to run models at scale. In this presentation, we will use our Land Cover Mapping work as a case study to present one path to making a trained machine learning model available as a spatial querying API. We will discuss the use of Azure Machine Learning Service to run parallel inference and generate a nationwide land cover map that can be served without the complexities of a real-time inference system, and we will discuss the use of the Microsoft AI for Earth open-source API framework to turn that set of cached results into a Web-based API. We hope this case study can provide a template for other scientists whose primary focus is on geospatial analysis techniques, but who also seek opportunities to expose those techniques to a broad audience.