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Auteur Yao-Yi Chiang |
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Towards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)
[article]
Titre : Towards the automated large-scale reconstruction of past road networks from historical maps Type de document : Article/Communication Auteurs : Johannes H. Uhl, Auteur ; Stefan Leyk, Auteur ; Yao-Yi Chiang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101794 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de groupement
[Termes IGN] analyse de sensibilité
[Termes IGN] carte ancienne
[Termes IGN] carte routière
[Termes IGN] carte topographique
[Termes IGN] classification par nuées dynamiques
[Termes IGN] données multitemporelles
[Termes IGN] Etats-Unis
[Termes IGN] extraction du réseau routier
[Termes IGN] histoire
[Termes IGN] paysage
[Termes IGN] réseau routier
[Termes IGN] transport routier
[Termes IGN] urbanisationRésumé : (auteur) Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 42 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography. Numéro de notice : A2022-947 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101794 Date de publication en ligne : 18/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100182
in Computers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101794[article]Recognizing text in raster maps / Yao-Yi Chiang in Geoinformatica, vol 19 n° 1 (January - March 2015)
[article]
Titre : Recognizing text in raster maps Type de document : Article/Communication Auteurs : Yao-Yi Chiang, Auteur ; Craig A. Knoblock, Auteur Année de publication : 2015 Article en page(s) : pp 1 - 27 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte ancienne
[Termes IGN] données maillées
[Termes IGN] information géographique
[Termes IGN] placement des écritures
[Termes IGN] reconnaissance de caractères
[Termes IGN] système d'information cartographique
[Termes IGN] toponymeRésumé : (auteur) Text labels in maps provide valuable geographic information by associating place names with locations. This information from historical maps is especially important since historical maps are very often the only source of past information about the earth. Recognizing the text labels is challenging because heterogeneous raster maps have varying image quality and complex map contents. In addition, the labels within a map do not follow a fixed orientation and can have various font types and sizes. Previous approaches typically handle a specific type of map or require intensive manual work. This paper presents a general approach that requires a small amount of user effort to semi-automatically recognize text labels in heterogeneous raster maps. Our approach exploits a few examples of text areas to extract text pixels and employs cartographic labeling principles to locate individual text labels. Each text label is then rotated automatically to horizontal and processed by conventional OCR software for character recognition. We compared our approach to a state-of-art commercial OCR product using 15 raster maps from 10 sources. Our evaluation shows that our approach enabled the commercial OCR product to handle raster maps and together produced significant higher text recognition accuracy than using the commercial OCR alone. Numéro de notice : A2015-484 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-014-0203-9 Date de publication en ligne : 21/02/2014 En ligne : https://doi.org/10.1007/s10707-014-0203-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77246
in Geoinformatica > vol 19 n° 1 (January - March 2015) . - pp 1 - 27[article]Automatic and accurate extraction of road intersections from raster maps / Yao-Yi Chiang in Geoinformatica, vol 13 n° 2 (June 2009)
[article]
Titre : Automatic and accurate extraction of road intersections from raster maps Type de document : Article/Communication Auteurs : Yao-Yi Chiang, Auteur ; C. Shahabi, Auteur ; Craig A. Knoblock, Auteur ; C.C. Chen, Auteur Année de publication : 2009 Article en page(s) : pp 121 - 157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carrefour
[Termes IGN] carte numérique
[Termes IGN] conflation
[Termes IGN] connexité (topologie)
[Termes IGN] données maillées
[Termes IGN] extraction automatique
[Termes IGN] extraction du réseau routier
[Termes IGN] intégration de données
[Termes IGN] réseau routierRésumé : (Auteur) Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources. Copyright Springer Numéro de notice : A2009-073 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-008-0046-3 En ligne : https://doi.org/10.1007/s10707-008-0046-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29703
in Geoinformatica > vol 13 n° 2 (June 2009) . - pp 121 - 157[article]Réservation
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