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Multipurpose temporal GIS model for cadastral data management / Joseph Mango in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1205 - 1230 Titre : Multipurpose temporal GIS model for cadastral data management Type de document : Article/Communication Auteurs : Joseph Mango, Auteur ; Christophe Claramunt, Auteur ; Jamila Ngondo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1205 - 1230 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] données cadastrales [Termes IGN] historique des données [Termes IGN] outil d'aide à la décision [Termes IGN] parcelle cadastrale [Termes IGN] SIG temporel [Termes IGN] système d'information foncière [Termes IGN] Tanzanie [Termes IGN] Zambie Résumé : (auteur) Past and current cadastral records are among the most valuable information that different countries need to solve land management and planning problems. However, many countries still face critical challenges in adopting modern temporal cadastral systems, including a sound integration of time constructs, efficient data integration and representation methods in the designed models. This research developed a new temporal GIS model to manage spatial and non-spatial temporal cadastral data, namely cadastral parcels, land-use and land-ownerships. Three-time dimensions defined by decision and valid and transaction times were formulated to qualify parcels data. A hybrid approach fusing on the Base State with Amendment and Space-Time Composite models is used to store significant parcel changes and their relationships in two interdependent sub-databases. We used administrative plot identifiers to associate with land use and ownership records, experiencing distinct temporal variations in the third sub-database within the same main repository. We experimented our model with data from Tanzania, and the results from queries demonstrate that the designed model can store all three temporal cadastral data and track their variations semantically and effectively. This model is very useful for storing cadastral parcels, reasons, events, and the transformed parcels’ values to improve decision-making processes. Numéro de notice : A2022-406 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2009483 Date de publication en ligne : 15/12/2022 En ligne : https://doi.org/10.1080/13658816.2021.2009483 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100719 [article]

Revising cadastral data on land boundaries using deep learning in image-based mapping / Bujar Fetai in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 5 (May 2022) . - n° 298 Titre : Revising cadastral data on land boundaries using deep learning in image-based mapping Type de document : Article/Communication Auteurs : Bujar Fetai, Auteur ; Dejan Grigillo, Auteur ; Anka Lisec, Auteur Année de publication : 2022 Article en page(s) : n° 298 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] cadastre étranger [Termes IGN] cartographie cadastrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] données cadastrales [Termes IGN] limite cadastrale [Termes IGN] point d'appui [Termes IGN] Slovénie Résumé : (auteur) One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries. Numéro de notice : A2022-357 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11050298 Date de publication en ligne : 04/05/2022 En ligne : https://doi.org/10.3390/ijgi11050298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100562 [article]

Accuracy issues for spatial update of digital cadastral maps / David Pullar in ISPRS International journal of geo-information, vol 11 n° 4 (April 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 4 (April 2022) . - n° 221 Titre : Accuracy issues for spatial update of digital cadastral maps Type de document : Article/Communication Auteurs : David Pullar, Auteur ; Stephen Donaldson, Auteur Année de publication : 2022 Article en page(s) : n° 221 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre étranger [Termes IGN] Australie [Termes IGN] base de données foncières [Termes IGN] compensation [Termes IGN] données cadastrales [Termes IGN] lever cadastral [Termes IGN] méthode des moindres carrés [Termes IGN] mise à jour [Termes IGN] parcelle cadastrale [Termes IGN] plan parcellaire [Termes IGN] précision des données Résumé : (auteur) All geospatial data are updated periodically. Cadastral parcel mapping, however, has special update requirements that set it apart from other geospatial data. Mapped boundaries change continuously to fit with new survey plans. Additionally, new parcels have to be fitted and aligned with adjoining parcels to merge them into existing cadastral mapping. This is preferably performed by a spatial adjustment approach to systematically improve its accuracy over time. This paper adapts methods for analysis and adjustment of survey networks to improve the accuracy of cadastral mapping with better coordinate positioning and survey plan dimensions. Case studies for both hypothetical and real cadastral mapping are used to illustrate the issues and spatially resolve errors. Adjustment results achieve an accuracy consistent with other GIS layers and boundary features visible in high-resolution orthoimagery. Graphical charts based on stress–strain relationships provide a simplified means to interpret post-adjustment results to identify and fix potential errors. Numéro de notice : A2022-285 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11040221 Date de publication en ligne : 24/03/2022 En ligne : https://doi.org/10.3390/ijgi11040221 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100305 [article]

Consideration on how to introduce gamification tools to enhance citizen engagement in crowdsourced cadastral surveys / K. Apostolopoulos in Survey review, vol 54 n° 383 (March 2022)  

Public [article]  inSurvey review > vol 54 n° 383 (March 2022) . - pp 142 - 152 Titre : Consideration on how to introduce gamification tools to enhance citizen engagement in crowdsourced cadastral surveys Type de document : Article/Communication Auteurs : K. Apostolopoulos, Auteur ; Chryssy Potsiou, Auteur Année de publication : 2022 Article en page(s) : pp 142 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre étranger [Termes IGN] approche participative [Termes IGN] base de données foncières [Termes IGN] citoyen [Termes IGN] données cadastrales [Termes IGN] enquête [Termes IGN] Grèce [Termes IGN] participation du public [Termes IGN] réseau social [Termes IGN] téléphone intelligent Résumé : (auteur) The major objective of this research is to investigate the progress of citizen participation in cadastral surveying and to consider ways on how to introduce gamification tools for further improvement. A brief literature review is presented in the areas of the Sustainable Development Agenda 2030 related to land administration and citizen engagement, e-government and citizen participation and gamification tools for citizen engagement. This paper, also, includes an investigation of the progress in introducing volunteerism and citizen participation to the Hellenic Cadastre. A case study is held by a group of volunteers in order to assess the developed tools designed either by the private sector or by the cadastral agency. Numéro de notice : A2022-240 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT/SOCIETE NUMERIQUE Nature : Article DOI : 10.1080/00396265.2021.1888027 Date de publication en ligne : 23/02/2021 En ligne : https://doi.org/10.1080/00396265.2021.1888027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100164 [article]

Building detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)  

Public [article]  inGeodetski vestnik > vol 65 n° 4 (December 2021 - February 2022) . - pp 559 - 576 Titre : Building detection with convolutional networks trained with transfer learning Type de document : Article/Communication Auteurs : Simon Šanca, Auteur ; Krištof Oštir, Auteur ; Alen Mangafić, Auteur Année de publication : 2021 Article en page(s) : pp 559 - 576 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification automatique d'objets [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] données cadastrales [Termes IGN] image aérienne [Termes IGN] image infrarouge couleur [Termes IGN] image proche infrarouge [Termes IGN] image RVB [Termes IGN] orthoimage couleur [Termes IGN] segmentation d'image [Termes IGN] Slovénie Résumé : (Auteur) Building footprint detection based on orthophotos can be used to update the building cadastre. In recent years deep learning methods using convolutional neural networks have been increasingly used around the world. We present an example of automatic building classification using our datasets made of colour near-infrared orthophotos (NIR-R-G) and colour orthophotos (R-G-B). Building detection using pretrained weights from two large scale datasets Microsoft Common Objects in Context (MS COCO) and ImageNet was performed and tested. We applied the Mask Region Convolutional Neural Network (Mask R-CNN) to detect the building footprints. The purpose of our research is to identify the applicability of pre-trained neural networks on the data of another colour space to build a classification model without re-learning. Numéro de notice : A2021-930 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2021.04.559-593 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.04.559-593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99409 [article]

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