Automatic Georeferencing of Topographic Raster Maps
In recent years, many scientific institutions have digitized their collections, which often include a large variety of topographic raster maps. These raster maps provide accurate (historical) geographical information but cannot be integrated directly into a geographical information system (GIS) due...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/11/7/387 |
| _version_ | 1827596993452572672 |
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| author | Kenzo Milleville Steven Verstockt Nico Van de Weghe |
| author_facet | Kenzo Milleville Steven Verstockt Nico Van de Weghe |
| author_sort | Kenzo Milleville |
| collection | DOAJ |
| description | In recent years, many scientific institutions have digitized their collections, which often include a large variety of topographic raster maps. These raster maps provide accurate (historical) geographical information but cannot be integrated directly into a geographical information system (GIS) due to a lack of metadata. Additionally, the text labels on the map are usually not annotated, making it inefficient to query for specific toponyms. Manually georeferencing and annotating the text labels on these maps is not cost-effective for large collections. This work presents a fully automated georeferencing approach based on text recognition and geocoding pipeline. After recognizing the text on the maps, publicly available geocoders were used to determine a region of interest. The approach was validated on a collection of historical and contemporary topographic maps. We show that this approach can geolocate the topographic maps fairly accurately, resulting in an average georeferencing error of only 316 m (1.67%) and 287 m (0.90%) for 16 historical maps and 9 contemporary maps spanning 19 km and 32 km, respectively (scale 1:25,000 and 1:50,000). Furthermore, this approach allows the maps to be queried based on the recognized visible text and found toponyms, which further improves the accessibility and quality of the collection. |
| first_indexed | 2024-03-09T03:21:57Z |
| format | Article |
| id | doaj.art-df16086485174443a5050f186a52f11f |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T03:21:57Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-df16086485174443a5050f186a52f11f2023-12-03T15:08:46ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-07-0111738710.3390/ijgi11070387Automatic Georeferencing of Topographic Raster MapsKenzo Milleville0Steven Verstockt1Nico Van de Weghe2IDLab, Ghent University—IMEC, Technologiepark-Zwijnaarde 122, 9052 Ghent, BelgiumIDLab, Ghent University—IMEC, Technologiepark-Zwijnaarde 122, 9052 Ghent, BelgiumDepartment of Geography, Ghent University, Krijgslaan 281 (S8), 9000 Ghent, BelgiumIn recent years, many scientific institutions have digitized their collections, which often include a large variety of topographic raster maps. These raster maps provide accurate (historical) geographical information but cannot be integrated directly into a geographical information system (GIS) due to a lack of metadata. Additionally, the text labels on the map are usually not annotated, making it inefficient to query for specific toponyms. Manually georeferencing and annotating the text labels on these maps is not cost-effective for large collections. This work presents a fully automated georeferencing approach based on text recognition and geocoding pipeline. After recognizing the text on the maps, publicly available geocoders were used to determine a region of interest. The approach was validated on a collection of historical and contemporary topographic maps. We show that this approach can geolocate the topographic maps fairly accurately, resulting in an average georeferencing error of only 316 m (1.67%) and 287 m (0.90%) for 16 historical maps and 9 contemporary maps spanning 19 km and 32 km, respectively (scale 1:25,000 and 1:50,000). Furthermore, this approach allows the maps to be queried based on the recognized visible text and found toponyms, which further improves the accessibility and quality of the collection.https://www.mdpi.com/2220-9964/11/7/387text recognitiongeolocalizationcomputer visionraster mapsgeoreferencing |
| spellingShingle | Kenzo Milleville Steven Verstockt Nico Van de Weghe Automatic Georeferencing of Topographic Raster Maps ISPRS International Journal of Geo-Information text recognition geolocalization computer vision raster maps georeferencing |
| title | Automatic Georeferencing of Topographic Raster Maps |
| title_full | Automatic Georeferencing of Topographic Raster Maps |
| title_fullStr | Automatic Georeferencing of Topographic Raster Maps |
| title_full_unstemmed | Automatic Georeferencing of Topographic Raster Maps |
| title_short | Automatic Georeferencing of Topographic Raster Maps |
| title_sort | automatic georeferencing of topographic raster maps |
| topic | text recognition geolocalization computer vision raster maps georeferencing |
| url | https://www.mdpi.com/2220-9964/11/7/387 |
| work_keys_str_mv | AT kenzomilleville automaticgeoreferencingoftopographicrastermaps AT stevenverstockt automaticgeoreferencingoftopographicrastermaps AT nicovandeweghe automaticgeoreferencingoftopographicrastermaps |