Automatic generation of high-quality building samples using OpenStreetMap and deep learning

Abstract

Existing building annotation methods require significant human resources or other costs, making it challenging to achieve both low cost and high efficiency simultaneously. Crowdsourced OpenStreetMap (OSM) data, with its extensive volume and openness, is widely used for annotation purposes. However, issues such as missing quality information and poor data completeness have hindered its potential to generate deep-learning samples. In this context, our research developed an automated method for generating high-quality building samples based on OSM and deep learning. To address the impact of poor OSM data completeness, we designed a Region-Of-Interest (ROI) generation algorithm to alleviate the negative impact of missing annotations during model training. Leveraging the superior performance of models specialized in the building extraction domain, we devised a method for selecting high-quality samples. Experimental results on the open-source simulation datasets WHU-SIM, MASS-SIM, and real environments in the San Angelo and Washington regions demonstrated the effectiveness of this method. We produced high-quality building samples with a resolution of 0.3 m for the San Angelo and Washington areas, enriching the available data in the building extraction field. This research advances the application of OSM in the remote sensing domain and provides comprehensive insights into its potential for automated sample generation in deep learning.