Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning Approach
Historical maps contain valuable topographic information, including altimetry in the form of annotated elevation contours. These contours are essential for understanding past terrain configurations, particularly in areas affected by human activities such as mining or dam construction. To make this d...
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MDPI AG
2025-05-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/5/201 |
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| author | Jakub Vynikal Jan Pacina |
| author_facet | Jakub Vynikal Jan Pacina |
| author_sort | Jakub Vynikal |
| collection | DOAJ |
| description | Historical maps contain valuable topographic information, including altimetry in the form of annotated elevation contours. These contours are essential for understanding past terrain configurations, particularly in areas affected by human activities such as mining or dam construction. To make this data usable in modern GIS applications, the contours must be vectorized—a process that often requires extensive manual work due to noise, inconsistent symbology, and topological disruptions like annotations or sheet boundaries. In this study, we apply a convolutional neural network (U-Net) to improve the automation of this vectorization process. Leveraging a recent benchmark for historical map vectorization, our method demonstrates increased robustness to disruptive factors and reduces the need for manual corrections. |
| format | Article |
| id | doaj-art-521149eac06445c98d3679dcecee555e |
| institution | DOAJ |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-521149eac06445c98d3679dcecee555e2025-08-20T03:14:39ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-05-0114520110.3390/ijgi14050201Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning ApproachJakub Vynikal0Jan Pacina1Department of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, Thákurova 7, 166 36 Prague, Czech RepublicDepartment of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, Thákurova 7, 166 36 Prague, Czech RepublicHistorical maps contain valuable topographic information, including altimetry in the form of annotated elevation contours. These contours are essential for understanding past terrain configurations, particularly in areas affected by human activities such as mining or dam construction. To make this data usable in modern GIS applications, the contours must be vectorized—a process that often requires extensive manual work due to noise, inconsistent symbology, and topological disruptions like annotations or sheet boundaries. In this study, we apply a convolutional neural network (U-Net) to improve the automation of this vectorization process. Leveraging a recent benchmark for historical map vectorization, our method demonstrates increased robustness to disruptive factors and reduces the need for manual corrections.https://www.mdpi.com/2220-9964/14/5/201automatic vectorizationdeep learningelevation contourshistorical geographyhistorical mapsneural networks |
| spellingShingle | Jakub Vynikal Jan Pacina Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning Approach ISPRS International Journal of Geo-Information automatic vectorization deep learning elevation contours historical geography historical maps neural networks |
| title | Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning Approach |
| title_full | Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning Approach |
| title_fullStr | Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning Approach |
| title_full_unstemmed | Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning Approach |
| title_short | Automatic Elevation Contour Vectorization: A Case Study in a Deep Learning Approach |
| title_sort | automatic elevation contour vectorization a case study in a deep learning approach |
| topic | automatic vectorization deep learning elevation contours historical geography historical maps neural networks |
| url | https://www.mdpi.com/2220-9964/14/5/201 |
| work_keys_str_mv | AT jakubvynikal automaticelevationcontourvectorizationacasestudyinadeeplearningapproach AT janpacina automaticelevationcontourvectorizationacasestudyinadeeplearningapproach |