Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland–Urban Interface Mapping

Abstract

Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.

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“…It should be analyzed to determine the overall quality of the resulting data. The equation used is (Huang & Jin, 2022): Where the Intersection of Union (IoU) is the percentage of new polygons formed due to segmentation errors of the entire data, The Area of Overlap is the area that overlaps, and the Area of Union is the area that merges. The number of true Detection, false Detection, and undetected must be known to determine the distribution of buildings in an area.…”

Assessing the Accuracy of the Wildland–Urban Interface Index in Portuguese Rural Villages’ Context: A Case Study of the 2017 Pedrógão Grande Wildfire

“…Considering these points, the task of identifying WUI areas has been pursued among researchers across the globe. Huang and Jin [21] developed a deep-learning-based methodology for identifying the building footprint, vegetation cover and distance to remote areas in California, USA, over eight years. The work is interesting as it uses only satellite images available through a governmental agency as the primary data source.…”