Using Aerial Imagery and GIS in Automated Building Footprint Extraction and Shape Recognition for Earthquake Risk Assessment of Urban Inventories

摘要

Earthquakes cause massive loss of property and lives, and mitigating their potential effects requires accurate modeling and simulation of their impacts. Earthquake building damage modeling and risk assessment applications require accurate accounts of inventories at risk and their attributes such as structure type, usage, size, number of stories, shape, year built, value, etc. This paper describes the development of algorithms for automatically extracting and recognizing 2-D building shape information using integrated aerial imagery processing and Geographic Information Systems data. We use vector parcel geometries and their attributes to simplify the building extraction task by limiting the processing geography. Extraction is significantly improved by innovatively weighting the histograms. Extracted buildings are cleaned, simplified, and run through 2-D shape recognition routines that classify the footprint. We discuss reasons for successes and failures in both extraction and recognition.