Modernizing Construction Indicators Through Machine Learning and Satellite Imagery

Articles and reports: 11-522-X202100100019
Description: Official statistical agencies must continually seek new methods and techniques that can increase both program efficiency and product relevance. The U.S. Census Bureau’s measurement of construction activity is currently a resource-intensive endeavor, relying heavily on monthly survey response via questionnaires and extensive field data collection. While our data users continually require more timely and granular data products, the traditional survey approach and associated collection cost and respondent burden limits our ability to meet that need. In 2019, we began research on whether the application of machine learning techniques to satellite imagery could accurately estimate housing starts and completions while meeting existing monthly indicator timelines at a cost equal to or less than existing methods. Using historical Census construction survey data in combination with targeted satellite imagery, the team trained, tested, and validated convolutional neural networks capable of classifying images by their stage of construction demonstrating the viability of a data science-based approach to producing official measures of construction activity.

Key Words: Official Statistics; Housing Starts, Machine Learning, Satellite Imagery

Issue Number: 2021001
Author(s): Smith, Aidan D.; Ferronato, Hector
Main Product: Statistics Canada International Symposium Series: Proceedings
Format Release date More information
PDF October 15, 2021

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