Geomancer: An Open-Source Framework for Geospatial Feature Engineering
It addresses the problem of complex geospatial data processing for machine learning practitioners, though it appears incremental as it builds on existing data warehousing and feature engineering concepts.
The paper introduces Geomancer, an open-source framework that simplifies geospatial feature engineering for large-scale machine learning tasks by allowing users to define and apply features from geospatial datasets, with applications in property value estimation and area valuation.
This paper presents Geomancer, an open-source framework for geospatial feature engineering. It simplifies the acquisition of geospatial attributes for downstream, large-scale machine learning tasks. Geomancer leverages any geospatial dataset stored in a data warehouse, users need only to define the features (Spells) they want to create, and cast them on any spatial dataset. In addition, these features can be exported into a JSON file (SpellBook) for sharing and reproducibility. Geomancer has been useful to some of our production use-cases such as property value estimation, area valuation, and more. It is available on Github, and can be installed from PyPI.