Large-Scale Building Footprint Extraction from Open-Sourced Satellite Imagery via Instance Segmentation Approach

Abstract

Building footprints are one of the primary data sources in the urban geographic information system (GIS) database. They are crucial for applications such as urban planning and population estimation. However, high-quality remote sensing imagery is not always available as open-sourced data for a wide area. Therefore, a versatile method was proposed in this study for building footprint extraction pipelines for a wide area. The proposed method was based on an instance segmentation approach using open-sourced satellite images. First, a reference-based color normalization algorithm was applied to eliminate the color differences in the satellite images from different sources. Subsequently, a mask R-CNN model was fine-tuned for the footprint extraction task on Detectron2. Finally, a modified image mosaicking approach was proposed for a large scene. The Hyogo prefecture, Japan, was used as the test area to evaluate the performance of the method.