Citation: Shi, K.; Yan, J.; Yang, J. A Semantic Partition Algorithm Based on Improved K-Means Clustering for Large-Scale Indoor Areas. ISPRS Int. J. Geo-Inf. 2024, 13, 41. https://doi.org/ 10.3390/ijgi13020041 Academic Editors: Wolfgang Kainz and Hartwig H. Hochmair Received: 11 November 2023 Revised: 19 January 2024 Accepted: 22 January 2024 Published: 27 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Geo-Information Article A Semantic Partition Algorithm Based on Improved K-Means Clustering for Large-Scale Indoor Areas Kegong Shi, Jinjin Yan * and Jinquan Yang Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266500, China; kegong.shi@hrbeu.edu.cn (K.S.); yjq980314@hrbeu.edu.cn (J.Y.) * Correspondence: jinjin.yan@hrbeu.edu.cn Abstract: Reasonable semantic partition of indoor areas can improve space utilization, optimize property management, and enhance safety and convenience. Existing algorithms for such partitions have drawbacks, such as the inability to consider semantics, slow convergence, and sensitivity to outliers. These limitations make it difficult to have partition schemes that can match the real-world observations. To obtain proper partitions, this paper proposes an improved K-means clustering algorithm (IK-means), which differs from traditional K-means in three respects, including the distance measurement method, iterations, and stop conditions of iteration. The first aspect considers the semantics of the spaces, thereby enhancing the rationality of the space partition. The last two increase the convergence speed. The proposed algorithm is validated in a large-scale indoor scene, and the results show that it has outperformance in both accuracy and efficiency. The proposed IK-means algorithm offers a promising solution to overcome existing limitations and advance the effectiveness of indoor space partitioning algorithms. This research has significant implications for the semantic area partition of large-scale and complex indoor areas, such as shopping malls and hospitals. Keywords: area semantic partition; improved K-means; large-scale indoor areas 1. Introduction With the rapid urbanization and expansion of indoor environments in large-scale settings [1,2], the partition of indoor areas has become a significant concern [3]. Reasonable partitioning plays a crucial role in improving space utilization, optimizing management, and enhancing safety and convenience. It has wide-ranging applications in various fields such as indoor navigation [4,5], security monitoring [6], and resource management [7,8]. A semantic-based area partition is able to bring in numerous benefits to large-scale indoor scenes (such as shopping malls and hospitals), including improved business operations, enhanced customer service, and increased safety, leading to better experiences. For instance, in shopping malls, a semantic-based area partition can assist new businesses in selecting suitable shop locations based on attributes of their products. This allows the mall to effec- tively manage the procurement, sales, and inventory of different product categories, thereby enhancing overall operational efficiency. In particular, such a partition can assist shopping malls in determining suitable locations for signage installation, which can effectively help customers quickly locate the desired products and save shopping time. Analyzing rest areas enables the placement of flammable and explosive items in safe zones away from those areas, mitigating potential hazards and safety risks. In hospitals, dividing areas based on different departments (semantic categories) improves the efficiency of patient diagnosis and treatment. Patients can more accurately determine the areas they need to visit, like inpatient wards and diagnostic sections, which helps in reducing wait times and confusion, thereby improving the overall patient experience. Indoor area partition can be seen as an application of regional clustering [9]. K-means is a widely utilized machine learning algorithm among various partitioning methods [10]. ISPRS Int. J. Geo-Inf. 2024, 13, 41. https://doi.org/10.3390/ijgi13020041 https://www.mdpi.com/journal/ijgi