Thesis Title
Shadow-Based Automatic Building Height Estimation From High Spatial Resolution Satellite Imagery
Date of Graduation
Summer 2022
Degree
Master of Science in Geospatial Sciences
Department
Geography, Geology, and Planning
Committee Chair
Xin Miao
Abstract
Three-dimensional city (3D) models are very useful in supporting natural disaster preparation and response. LiDAR surveying is currently the main method by which 3D city models are created; however, LiDAR data on a local scale is hard to obtain for developing countries. This project sought to test whether or not urban feature height data obtained using the photogrammetric sun-angle shadow method is a viable alternative to LiDAR-derived 3D city models. A core element of this work was the development of a toolset to be shared freely to the public to promote crowdsourcing of 3D building data. Prior works were reviewed and a shadow-overlapping method for estimating building heights was selected and implemented in the Python programming language. Shadow detection methods in the literature were also reviewed and eight were modified into a simple command-line Python tool to batch process shadow detection using multiple algorithms. The Missouri State University, Springfield, MO, campus was selected as the study site for testing the shadow-overlapping process and LiDAR/DEM data was used to create building footprints with ground truth height values. For the shadow detection methods that produced the most accurate results, roughly 60% of building height estimates were within 10 feet (or one floor) of the true height, and buildings whose heights were between 37-50 feet consistently had the lowest margins of error. A systematic finding, however, was that shorter buildings’ heights were overestimated and taller buildings were underestimated. A similar pattern was identified with building size/square footage with smaller buildings being overestimated and larger buildings being underestimated. In the end, the results suggested that the shadow-overlapping method is likely not reliable enough to produce height estimations comparable to LiDAR-derived methods and that these height estimates are not suitable for downstream calculations. However, for a simple/generalized 3D cartographic representation of an area, it appeared that this low-cost method could produce adequate results.
Keywords
3D city models, building height estimation, GIS, photogrammetry, Python, shadows
Subject Categories
Geographic Information Sciences | Remote Sensing | Spatial Science
Copyright
© Lonnie Lee Byrnside III
Recommended Citation
Byrnside, Lonnie Lee III, "Shadow-Based Automatic Building Height Estimation From High Spatial Resolution Satellite Imagery" (2022). MSU Graduate Theses. 3768.
https://bearworks.missouristate.edu/theses/3768
Open Access
Included in
Geographic Information Sciences Commons, Remote Sensing Commons, Spatial Science Commons