In Wi-Fi fingerprinting indoor localization, automating radio map database maintenance is one of the crucial issues, as it is a labour-intensive and long-term task for collecting and filtering samples to keep an up-to-date and accurate database. In particular, those access points (APs) newly installed in the environment should update radio maps and be included in the database to improve localization performance. This study presents an IWFUCIA system that automates indoor radio map database maintenance (RMapDM) using crowdsourced samples without accurate location annotation. The IWFUCIA incorporates the newly installed APs detection and identification, the significant APs feature selection, fingerprint integration updating, and online localization algorithms. After collecting new crowdsourced samples, we apply Willmott’s index of agreement (WIA) based on the Supported Vector Machine (SVM) regression to detect and identify a newly installed AP and the original existing ones. After getting the new APs, we propose a correlated coefficient and t-test score algorithm to select only those significant AP-based feature samples. We also proposed a fingerprint integration model to fuse original existing and new APs to update the database. Extensive experiments have been conducted in our teaching building to validate and evaluate the effectiveness of IWFUCIA. The results show that our IWFUCIA is robust for long-term maintenance and updating the outdated radio map database server. The average localization accuracy achieves 0.466 m, which significantly outperforms the localization positioning approaches with the original radio map by 84.96%, outdated radio maps by the changed APs powers removed, increased and decreased by 26.32%, 55.36%, and 73.14%, respectively.