Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAM
Map symbols play a crucial role in cartographic representation. Among these symbols, icons are particularly valued for their vivid and intuitive designs, making them widely utilized in tourist maps. However, the diversity and complexity of these symbols present significant challenges to cartographic...
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MDPI AG
2025-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| author | Di Cao Xinran Yan Jingjing Li Jiayao Li Lili Wu |
| author_facet | Di Cao Xinran Yan Jingjing Li Jiayao Li Lili Wu |
| author_sort | Di Cao |
| collection | DOAJ |
| description | Map symbols play a crucial role in cartographic representation. Among these symbols, icons are particularly valued for their vivid and intuitive designs, making them widely utilized in tourist maps. However, the diversity and complexity of these symbols present significant challenges to cartographic workflows. Icon design often relies on manual drawing, which is not only time-consuming but also heavily dependent on specialized skills. Automating the extraction of symbols from existing maps could greatly enhance the map symbol database, offering a valuable resource to support both symbol design and map production. Nevertheless, the intricate shapes and dense distribution of symbols in tourist maps complicate the accurate and efficient detection and extraction using existing methods. Previous studies have shown that You Only Look Once (YOLO) series models demonstrate strong performance in object detection, offering high accuracy and speed. However, these models are less effective in fine-grained boundary segmentation. To address this limitation, this article proposes integrating YOLO models with the Segment Anything Model (SAM) to tackle the challenges of combining efficient detection with precise segmentation. This article developed a dataset consisting of both paper-based and digital tourist maps, with annotations for five main categories of symbols: human landscapes, natural sceneries, humans, animals, and cultural elements. The performance of various YOLO model variants was systematically evaluated using this dataset. Additionally, a user interaction mechanism was incorporated to review and refine detection results, which were subsequently used as prompts for the SAM to perform precise symbol segmentation. The results indicate that the YOLOv8x model achieved excellent performance on the tourist map dataset, with an average detection accuracy of 94.4% across the five symbol categories, fully meeting the requirements for symbol detection tasks. The inclusion of a user interaction mechanism enhanced the reliability and flexibility of detection outcomes, while the integration of the SAM significantly improved the precision of symbol boundary extraction. In conclusion, the integration of YOLOv8x and SAM provides a robust and effective solution for automating the extraction of map symbols. This approach not only reduces the manual workload involved in dataset annotation, but also offers valuable theoretical and practical insights for enhancing cartographic efficiency. |
| format | Article |
| id | doaj-art-e91f59583ef24c2facb11cd18edee5ae |
| institution | DOAJ |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-e91f59583ef24c2facb11cd18edee5ae2025-08-20T03:12:22ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-01-011425510.3390/ijgi14020055Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAMDi Cao0Xinran Yan1Jingjing Li2Jiayao Li3Lili Wu4School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, ChinaMap symbols play a crucial role in cartographic representation. Among these symbols, icons are particularly valued for their vivid and intuitive designs, making them widely utilized in tourist maps. However, the diversity and complexity of these symbols present significant challenges to cartographic workflows. Icon design often relies on manual drawing, which is not only time-consuming but also heavily dependent on specialized skills. Automating the extraction of symbols from existing maps could greatly enhance the map symbol database, offering a valuable resource to support both symbol design and map production. Nevertheless, the intricate shapes and dense distribution of symbols in tourist maps complicate the accurate and efficient detection and extraction using existing methods. Previous studies have shown that You Only Look Once (YOLO) series models demonstrate strong performance in object detection, offering high accuracy and speed. However, these models are less effective in fine-grained boundary segmentation. To address this limitation, this article proposes integrating YOLO models with the Segment Anything Model (SAM) to tackle the challenges of combining efficient detection with precise segmentation. This article developed a dataset consisting of both paper-based and digital tourist maps, with annotations for five main categories of symbols: human landscapes, natural sceneries, humans, animals, and cultural elements. The performance of various YOLO model variants was systematically evaluated using this dataset. Additionally, a user interaction mechanism was incorporated to review and refine detection results, which were subsequently used as prompts for the SAM to perform precise symbol segmentation. The results indicate that the YOLOv8x model achieved excellent performance on the tourist map dataset, with an average detection accuracy of 94.4% across the five symbol categories, fully meeting the requirements for symbol detection tasks. The inclusion of a user interaction mechanism enhanced the reliability and flexibility of detection outcomes, while the integration of the SAM significantly improved the precision of symbol boundary extraction. In conclusion, the integration of YOLOv8x and SAM provides a robust and effective solution for automating the extraction of map symbols. This approach not only reduces the manual workload involved in dataset annotation, but also offers valuable theoretical and practical insights for enhancing cartographic efficiency.https://www.mdpi.com/2220-9964/14/2/55iconstourist mapsYOLOSAMsymbol automated extraction |
| spellingShingle | Di Cao Xinran Yan Jingjing Li Jiayao Li Lili Wu Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAM ISPRS International Journal of Geo-Information icons tourist maps YOLO SAM symbol automated extraction |
| title | Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAM |
| title_full | Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAM |
| title_fullStr | Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAM |
| title_full_unstemmed | Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAM |
| title_short | Automated Icon Extraction from Tourism Maps: A Synergistic Approach Integrating YOLOv8x and SAM |
| title_sort | automated icon extraction from tourism maps a synergistic approach integrating yolov8x and sam |
| topic | icons tourist maps YOLO SAM symbol automated extraction |
| url | https://www.mdpi.com/2220-9964/14/2/55 |
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