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The potential of LiDAR and UAV-photogrammetric data analysis to interpret archaeological sites: A case study of Chun Castle in South-West England / Israa Kadhim in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)
[article]
Titre : The potential of LiDAR and UAV-photogrammetric data analysis to interpret archaeological sites: A case study of Chun Castle in South-West England Type de document : Article/Communication Auteurs : Israa Kadhim, Auteur ; Fanar M. Abed, Auteur Année de publication : 2021 Article en page(s) : n° 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] château
[Termes IGN] classification ISODATA
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Cornouailles
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image captée par drone
[Termes IGN] photogrammétrie aérienne
[Termes IGN] semis de points
[Termes IGN] site archéologique
[Termes IGN] structure-from-motionRésumé : (auteur) With the increasing demands to use remote sensing approaches, such as aerial photography, satellite imagery, and LiDAR in archaeological applications, there is still a limited number of studies assessing the differences between remote sensing methods in extracting new archaeological finds. Therefore, this work aims to critically compare two types of fine-scale remotely sensed data: LiDAR and an Unmanned Aerial Vehicle (UAV) derived Structure from Motion (SfM) photogrammetry. To achieve this, aerial imagery and airborne LiDAR datasets of Chun Castle were acquired, processed, analyzed, and interpreted. Chun Castle is one of the most remarkable ancient sites in Cornwall County (Southwest England) that had not been surveyed and explored by non-destructive techniques. The work outlines the approaches that were applied to the remotely sensed data to reveal potential remains: Visualization methods (e.g., hillshade and slope raster images), ISODATA clustering, and Support Vector Machine (SVM) algorithms. The results display various archaeological remains within the study site that have been successfully identified. Applying multiple methods and algorithms have successfully improved our understanding of spatial attributes within the landscape. The outcomes demonstrate how raster derivable from inexpensive approaches can be used to identify archaeological remains and hidden monuments, which have the possibility to revolutionize archaeological understanding. Numéro de notice : A2021-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10010041 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/ijgi10010041 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97053
in ISPRS International journal of geo-information > vol 10 n° 1 (January 2021) . - n° 41[article]
Titre : UAV photogrammetry and remote sensing Type de document : Monographie Auteurs : Fernando Carvajal-Ramírez, Éditeur scientifique ; Francisco Agüera-Vega, Éditeur scientifique ; Patricio Martínez-Carricondo, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 258 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-0365-1453-6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image captée par drone
[Termes IGN] indice de végétation
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] occupation du sol
[Termes IGN] orthophotographie
[Termes IGN] photogrammétrie aérienne
[Termes IGN] point d'appui
[Termes IGN] reconstruction 3D
[Termes IGN] réseau antagoniste génératif
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] zone tamponRésumé : (éditeur) The concept of remote sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of Earth observation satellites.The emergence of unmanned aerial vehicles (UAV) with Global Navigation Satellite System (GNSS) controlled navigation and sensor-carrying capabilities has increased the number of publications related to new remote sensing from much closer distances. Previous knowledge about the behavior of the Earth's surface under the incidence different wavelengths of energy has been successfully applied to a large amount of data recorded from UAVs, thereby increasing the special and temporal resolution of the products obtained.More specifically, the ability of UAVs to be positioned in the air at pre-programmed coordinate points; to track flight paths; and in any case, to record the coordinates of the sensor position at the time of the shot and at the pitch, yaw, and roll angles have opened an interesting field of applications for low-altitude aerial photogrammetry, known as UAV photogrammetry. In addition, photogrammetric data processing has been improved thanks to the combination of new algorithms, e.g., structure from motion (SfM), which solves the collinearity equations without the need for any control point, producing a cloud of points referenced to an arbitrary coordinate system and a full camera calibration, and the multi-view stereopsis (MVS) algorithm, which applies an expanding procedure of sparse set of matched keypoints in order to obtain a dense point cloud. The set of technical advances described above allows for geometric modeling of terrain surfaces with high accuracy, minimizing the need for topographic campaigns for georeferencing of such products.This Special Issue aims to compile some applications realized thanks to the synergies established between new remote sensing from close distances and UAV photogrammetry. Note de contenu : 1- Using UAV-based photogrammetry to obtain correlation between the vegetation indices and chemical analysis of agricultural crops
2- Photogrammetry using UAV-mounted GNSS RTK: Georeferencing strategies without GCPs
3- Quality assessment of photogrammetric methods—A workflow for reproducible UAS orthomosaics
4- 3D reconstruction of power lines using UAV images to monitor corridor clearance
5- UAV-based terrain modeling under vegetation in the Chinese Loess Plateau: A deep learning and terrain correction ensemble frameword
6- UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points
7- UAV + BIM: Incorporation of photogrammetric techniques in architectural projects with building information modeling versus classical work processes
8- Structure from motion of multi-angle RPAS imagery complements larger-scale airborne Lidar data for cost-effective snow monitoring in mountain forests
9- Use of UAV-photogrammetry for quasi-vertical wall surveying
10- Deep learning-based single image super-resolution: An investigation for dense scene reconstruction with UAS photogrammetry
11- Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classificationNuméro de notice : 28664 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-1453-6 En ligne : https://doi.org/10.3390/books978-3-0365-1453-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99850 Adjusting the regular network of squares resolution to the digital terrain model surface shape / Dariusz Gościewski in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
[article]
Titre : Adjusting the regular network of squares resolution to the digital terrain model surface shape Type de document : Article/Communication Auteurs : Dariusz Gościewski, Auteur ; Małgorzata Gerus-Gościewska, Auteur Année de publication : 2020 Article en page(s) : n° 761 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] erreur moyenne quadratique
[Termes IGN] interpolation
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] morphologie
[Termes IGN] semis de points
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) A regular network of squares is formed by points uniformly distributed (mostly in the square corners) over the surface that is represented by the network. Each point (node) of the network has specified coordinates (X and Y) with a fixed constant distance between them. The third coordinate in a node (H) is determined by the application of interpolation based on the points distributed (usually dispersed as a point cloud e.g., from LiDAR) over the surface of the area surrounding the node. The regular network of squares formed in this manner allows the representation of a digital terrain model (DTM) to be performed in spatial information systems (SIP, GIS). The main problem that arises during the construction of such a network is the proper determination of its resolution (the base distance between the coordinates X and Y) depending on the topography. This article presents a method of the regular network of squares resolution determination depending on the morphological shape of the terrain surface. Following the application of the procedures being described, a differently shaped terrain is assigned various network densities. This enables the minimisation of inaccuracies of the surface model being formed. Consequently, a regular network of squares is formed with different base square sizes, which is adjusted with its resolution to the morphology of the surface it describes. Such operations allow the terrain model accuracy to be maintained over the entire area while reducing the number of points stored in the DTM database to the minimum. Numéro de notice : A2020-807 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9120761 Date de publication en ligne : 20/12/2020 En ligne : https://doi.org/10.3390/ijgi9120761 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96952
in ISPRS International journal of geo-information > vol 9 n° 12 (December 2020) . - n° 761[article]Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)
[article]
Titre : Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy Type de document : Article/Communication Auteurs : Vasil Yordanov, Auteur ; Maria Antonia Brovelli, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] cartographie géomorphologique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] effondrement de terrain
[Termes IGN] figuré linéaire
[Termes IGN] indice de risque
[Termes IGN] inventaire
[Termes IGN] Lombardie
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] modèle statistique
[Termes IGN] régression logistiqueRésumé : (auteur) Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations. Numéro de notice : A2020-695 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00344-1 Date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.1007/s12518-020-00344-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96244
in Applied geomatics > vol 12 n° 4 (December 2020) . - 23 p.[article]Automatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)
[article]
Titre : Automatic building footprint extraction from UAV images using neural networks Type de document : Article/Communication Auteurs : Zoran Kokeza, Auteur ; Miroslav Vujasinović, Auteur ; Miro Govedarica, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 545 - 561 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] cartographie cadastrale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection du bâti
[Termes IGN] empreinte
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] modèle numérique de surface
[Termes IGN] orthoimage
[Termes IGN] zone d'intérêtRésumé : (Auteur) Up-to-date cadastral maps are crucial for urban planning. Creating those maps with the classical geodetic methods is expensive and time-consuming. Emerge of Unmanned Aerial Vehicles (UAV) made a possibility for quick acquisition of data with much more details than it was possible before. The topic of the research refers to the challenges of automatic extraction of building footprints on high-resolution orthophotos. The objectives of this study were as follows: (1) to test the possibility of using different publicly available datasets (Tanzania, AIRS and Inria) for neural network training and then test the generalisation capability of the model on the Area Of Interest (AOI); (2) to evaluate the effect of the normalised digital surface model (nDSM) on the results of neural network training and implementation. Evaluation of the results shown that the models trained on the Tanzania (IoU 36.4%), AIRS (IoU 64.4%) and Inria (IoU 7.4%) datasets doesn't satisfy the requested accuracy to update cadastral maps in study area. Much better results are achieved in the second part of the study, where the training of the neural network was done on tiles (256x256) of the orthophoto of AOI created from data acquired using UAV. A combination of RGB orthophoto with nDSM resulted in a 2% increase of IoU, achieving the final IoU of over 90%. Numéro de notice : A2020-777 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.04.545-561 Date de publication en ligne : 26/10/2020 En ligne : http://www.geodetski-vestnik.com/en/2020-4 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96706
in Geodetski vestnik > vol 64 n° 4 (December 2020 - February 2021) . - pp 545 - 561[article]Réservation
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