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 The emergence of OpenStreetMap (OSM) as a comprehensive, community-driven geographic data platform has opened new possibilities for automated mapping approaches. A Systematic Literature Review of Data Quality Within OpenStreetMap(Kaur et al.,2017) highlighted that while positional accuracy and completeness were the most researched quality aspects, OSM data showed particular promise when compared against authoritative sources. OSM provides rich, regularly updated information about urban features, including building types, commercial establishments, and points of interest. This wealth of data, combined with advances in Natural Language Processing (NLP) and machine learning, presents an opportunity to develop more efficient and objective methods for functional map generation.
 
 The Classification of High-Resolution Remote-Sensing Images Using OpenStreetMap Information(Wan et al.,2017) study demonstrated the potential of this approach, achieving 87.9% classification accuracy by extracting OSM objects and applying morphological erosion to improve training data quality. Their method of superimposing road data onto classification results proved particularly effective in reducing confusion between roads and other features. Recent developments in text embedding techniques, particularly the advent of transformer-based models like the Universal Sentence Encoder (USE), have dramatically improved our ability to extract meaningful semantic information from textual data. Similarly, advances in clustering algorithms have enhanced our capability to identify patterns and groupings in high-dimensional data spaces. The Cluster-Based Training Data Preselection and Classification for Remote Sensing Images(Bian et al.,2010) study validated this approach, showing that their Cluster-based Classification Algorithm (CCA) improved classification accuracy compared to traditional methods, particularly when working with limited labeled samples. Similarly, A Clustering-Based KNN Improved Algorithm (CLKNN) for Text Classification(Zhou et al.,2010) introduced an innovative approach to address the inherent limitations of traditional KNN classification, particularly in handling uneven sample distributions. By incorporating a clustering step before KNN classification and implementing a dynamic adjustment mechanism for the neighborhood number, CLKNN significantly improved classification precision while reducing processing time and boundary region misclassification.
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 Analysis of Urban Functional Areas Based on Graph Clustering Neural Networks(Bai et al.,2025) highlights the growing importance of urban functional areas in optimizing spatial layouts and urban planning. Traditional methods often rely on road network units, which have limitations in accurately identifying functional areas. This research integrates multi-source data, including OpenStreetMap road networks, Points of Interest (POI) data, nighttime light data, and land use information, to create a more comprehensive classification system. By employing Graph Clustering Neural Networks (GCNN), the study achieves high-precision clustering of urban functional areas, leveraging deep learning techniques for better classification accuracy. This method demonstrates significant advantages over conventional approaches, as it effectively captures spatial relationships and integrates multi-source information for more reliable urban functional zone classification. The findings provide valuable insights for policymakers, improving urban planning, land use optimization, and resource allocation.
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 Multi-Type and Fine-Grained Urban Green Space Function Mapping Using BERT Model and Multi-Source Data(Cao et al.,2024) present an innovative approach to urban green space (UGS) classification by integrating Natural Language Processing (NLP) with remote sensing data. The study identifies limitations in traditional UGS classification, which often neglects semantic information in geospatial data. To address this, the authors utilize the BERT model for text classification of Points of Interest (POI) data, improving the recognition of functional attributes in urban green spaces. By combining POI data with urban functional zoning (UFZ) datasets and OpenStreetMap road networks, the study creates a more refined classification model. This approach enables a fine-grained differentiation of UGS into 19 distinct functional categories, surpassing existing classification techniques in accuracy. The findings highlight the potential of deep learning and NLP in geospatial analysis, offering a new direction for urban environmental management and spatial planning.
 
 In the realm of environmental feature detection, the Identification of Water and Green Regions in OpenStreetMap Using Modified Densitometry 3-Channel Algorithm(Kasu et al.,2019) presented a novel approach for automated identification of natural features in urban landscapes. Their Modified Densitometry 3-Channel algorithm, which combines RGB-based color thresholding with mathematical morphology techniques, achieved impressive accuracy rates of 82.92% for water region segmentation and 80.48% for green region detection. This advancement in natural feature identification has proven particularly valuable for city planning and environmental management applications.
 
 A CNN-Based Functional Zone Classification Method for Aerial Images(Zhang et al.,2016) explores the application of Convolutional Neural Networks (CNN) in classifying urban functional zones using aerial imagery. Unlike traditional GIS-based methods, which require extensive manual annotations, this study employs CNN models trained on high-resolution satellite images to identify and categorize functional zones such as residential, commercial, and industrial areas. The proposed method demonstrates the capability of CNNs in feature extraction, pattern recognition, and automated classification of urban landscapes. Experimental results indicate that CNN-based models outperform traditional machine learning techniques, such as support vector machines and decision trees, in accuracy and efficiency. This study contributes to the advancement of automated urban mapping, providing a scalable and adaptable approach to functional zone classification using deep learning and remote sensing data.
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 Our research addresses these challenges by proposing a novel methodology that combines text-based clustering with advanced NLP techniques to automate the generation of high-fidelity functional maps. This approach leverages the rich textual data available in OpenStreetMap, including building names, business descriptions, and point-of-interest information, to classify urban areas into distinct functional zones. By processing this data through sophisticated text embedding and clustering algorithms, we aim to create a more efficient, objective, and scalable approach to functional mapping.
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 The significance of this research extends beyond mere automation of existing processes. As cities continue to grow and evolve, the need for current, accurate functional maps becomes increasingly critical for:
 - Urban planning and development decisions
 - Infrastructure and service allocation