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Innovative approaches, tools and visualization techniques for analysing land use structures and dynamics of cities and regions (Editorial) / Robert Hecht in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 4 n° 2 (December 2020) . - n° 19 Titre : Innovative approaches, tools and visualization techniques for analysing land use structures and dynamics of cities and regions (Editorial) Type de document : Article/Communication Auteurs : Robert Hecht, Auteur ; Martin Behnisch, Auteur Année de publication : 2020 Article en page(s) : n° 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] analyse spatiale [Termes IGN] changement d'occupation du sol [Termes IGN] croissance urbaine [Termes IGN] utilisation du sol [Termes IGN] visualisation cartographique [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Geospatial analysis and visualisation can be used to gain knowledge about land use structure and its changes on different spatial scales, which in turn is key to unlock the potential for sustainable land use development. This editorial provides a frame to a set of papers of the topical collection “Innovative approaches, tools and visualization techniques for analyzing land use structures and dynamics of cities and regions”, which was initiated in conjunction with the 2017 International Land Use Symposium taken place in Dresden, Germany. It first introduces current, urging land use, development and management challenges. Further on, the editorial presents the individual contributions and reflects their affiliation to the themes “Mapping and Monitoring Approaches” and “Planning, Decision Support and Participation”. Although the objectives, methods and underlying data used in the papers of this topical collection greatly vary, as pieces of a puzzle they contribute to a better analysis and understanding of current and future land use structures and dynamics of cities and regions. Numéro de notice : A2020-797 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-020-00060-9 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1007/s41651-020-00060-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96614 [article]

Automatic identification of building types based on topographic databases – a comparison of different data sources / Robert Hecht in International journal of cartography, vol 1 n° 1 (August 2015)  

Public [article]  inInternational journal of cartography > vol 1 n° 1 (August 2015) . - pp 18 - 31 Titre : Automatic identification of building types based on topographic databases – a comparison of different data sources Type de document : Article/Communication Auteurs : Robert Hecht, Auteur ; Gotthard Meinel, Auteur ; Manfred F. Buchroithner, Auteur Année de publication : 2015 Article en page(s) : pp 18 - 31 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] apprentissage automatique [Termes IGN] base de données topographiques [Termes IGN] bâtiment [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] emprise au sol [Termes IGN] généralisation cartographique automatisée [Termes IGN] identification automatique [Termes IGN] reconnaissance de formes Résumé : (auteur) Data, maps and services of the national mapping and cadastral agencies contain geometric information on buildings, particularly building footprints. However, building type information is often not included. In this paper, we propose a data-driven approach for automatic classification of building footprints that make use of pattern recognition and machine learning techniques. Using a Random Forest Classifier the suitability of five different data sources (e.g. topographic raster maps, cadastral databases or digital landscape models) is investigated with respect to the achieved accuracies. The results of this study show that building footprints obtained from topographic databases such as digital landscape models, cadastral databases or 3D city models can be classified with an accuracy of 90–95%. When classifying building footprints on the basis of topographic maps the accuracy is considerably lower (as of 76–88%). The automatic classification of building footprints provides an important contribution to the acquisition of new small-scale indicators on settlement structure, such as building density, floor space ratio or dwelling/population densities. In addition to its importance for urban research and planning, the results are also relevant for cartographic disciplines, such as map generalization, automated mapping and geovisualization. Numéro de notice : A2015-434 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2015.1055644 En ligne : https://doi.org/10.1080/23729333.2015.1055644 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76883 [article]

Data fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction / R. Beger in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 66 n° 6 supplement (December 2011) . - pp 40 - 51 Titre : Data fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction Type de document : Article/Communication Auteurs : R. Beger, Auteur ; C. Gedrange, Auteur ; Robert Hecht, Auteur ; M. Neubert, Auteur Année de publication : 2011 Article en page(s) : pp 40 - 51 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] axe médian [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image aérienne [Termes IGN] lasergrammétrie [Termes IGN] orthoimage [Termes IGN] précision des données [Termes IGN] semis de points [Termes IGN] voie ferrée Résumé : (Auteur) The quality of remotely sensed data in regards of accuracy and resolution has considerably improved in recent years. Very small objects are detectable by means of imaging and laser scanning, yet there are only few studies to use such data for large scale mapping of railroad infrastructure. In this paper, an approach is presented that integrates extremely high resolution ortho-imagery and dense airborne laser scanning point clouds. These data sets are used to reconstruct railroad track centre lines. A feature level data fusion is carried out in order to combine the advantages of both data sets and to achieve a maximum of accuracy and completeness. The workflow consists of three successive processing steps. First, object-based image analysis is used to derive a railroad track mask from ortho-imagery. This spatial location information is then combined with the height information to classify the laser points. Lastly, the location of railroad track centre lines from these classified points were approximated using a feature extraction method based on an adapted random sample consensus algorithm. This workflow is tested on two railroad sections and was found to deliver very accurate results in a quickly and highly automated manner. Numéro de notice : A2011-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2011.09.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2011.09.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31411 [article]

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