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Termes IGN > sciences naturelles > physique > traitement d'image > photogrammétrie > photogrammétrie numérique > orthoimage
orthoimageSynonyme(s)Orthophotographie numérique |
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Forest road extraction from orthophoto images by convolutional neural networks / Erhan Çalişkan in Geocarto international, vol 38 n° inconnu ([01/01/2023])
[article]
Titre : Forest road extraction from orthophoto images by convolutional neural networks Type de document : Article/Communication Auteurs : Erhan Çalişkan, Auteur ; Yusuf Sevim, Auteur Année de publication : 2023 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chemin forestier
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction automatique
[Termes IGN] orthoimage
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Continuous monitoring of the forest road infrastructure and keeping track of the changes occurred are important for forestry practices, map updating, forest fire and forest transport decision support systems. In this context, the most of up to date data can be obtained by automatic forest road extraction from satellite images via machine learning (ML). Acquiring sufficient data is one of the most important factors which affect the success of ML and deep learning (DL). DL architectures yield more consistent results for complex data sets compared with ML algorithms. In the present study, three different deep learning (Resnet-18, MobileNet-V2 and Xception) architectures with semantic segmentation architecture were compared for extracting the forest road network from high-resolution orthophoto images and the results were analyzed. The architectures were evaluated through a multiclass statistical analysis based precision, recall, F1 score, intersection over union and overall accuracy (OA). The results present significant values obtained by the Resnet-18 architecture, with 99.72% of OA and 98.87% of precision and by the MobileNet-V2 architecture, with 97.76% of OA and 98.28% of precision. Also the results show that Resnet-18, MobileNet-V2 semantic segmentation architectures can be used efficiently for forest road extraction. Numéro de notice : A2022-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2060319 Date de publication en ligne : 06/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2060319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100380
in Geocarto international > vol 38 n° inconnu [01/01/2023] . - pp[article]Production of orthophoto map using mobile photogrammetry and comparative assessment of cost and accuracy with satellite imagery for corridor mapping: a case study in Manesar, Haryana, India / Manuj Dev in Annals of GIS, vol 29 n° 1 (January 2023)
[article]
Titre : Production of orthophoto map using mobile photogrammetry and comparative assessment of cost and accuracy with satellite imagery for corridor mapping: a case study in Manesar, Haryana, India Type de document : Article/Communication Auteurs : Manuj Dev, Auteur ; Shetru M. Veerabhadrappa, Auteur ; Ashutosh Kainthola, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 163 - 176 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Orthophotographie, orthoimage
[Termes IGN] aérotriangulation
[Termes IGN] analyse comparative
[Termes IGN] image panoramique
[Termes IGN] image satellite
[Termes IGN] modèle stéréoscopique
[Termes IGN] orthoimage
[Termes IGN] orthophotocarte
[Termes IGN] point d'appui
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] système de numérisation mobileRésumé : (auteur) The study aims to find a low-cost alternate technology to get imagery, using mobile platform, and produce digital orthophoto for corridor mapping, with a higher degree of accuracy and which can reduce the lag time of acquisition of data. The present study uses digital single-lens reflex cameras, mounted on a mobile vehicle, and acquisition of data in the video format rather than still photographs, as traditionally used in mobile mapping systems. The videos are used to create a set of images and orthophotos. A widespread ground control points were recorded in the study area, using the global navigation satellite system receiver, which measured the control points in real-time kinematic mode. Generation of digital orthophoto has been completed using the captured mobile imagery and ground control point. Furthermore, procurement of satellite imagery and aerial triangulation using ground control points have been done. While comparing the planimetric accuracy of orthophoto against satellite imagery using the ground control points, the achieved root mean square error value of produced orthophoto is 0.171 m in X axis and 0.205 m in Y axis. However, for Cartosat -1 satellite imagery, the RMSE value for X is 1.22 m and for Y is 1.98 m. This research proposes the alternate low-cost mobile mapping method to capture the imagery for orthophoto production. The cost of orthophoto production from mobile image was found 77% cheaper than the orthophoto cost from fresh/latest satellite imagery procurement, while the overall production was 70% cost-effective than the orthophoto maps made from archived imagery. Numéro de notice : A2023-161 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/19475683.2022.2141853 Date de publication en ligne : 12/11/2022 En ligne : https://doi.org/10.1080/19475683.2022.2141853 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102864
in Annals of GIS > vol 29 n° 1 (January 2023) . - pp 163 - 176[article]Coastal land use and shoreline evolution along the Nador lagoon Coast in Morocco / Khalid El Khalidi in Geocarto international, vol 37 n° 25 ([01/12/2022])
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Titre : Coastal land use and shoreline evolution along the Nador lagoon Coast in Morocco Type de document : Article/Communication Auteurs : Khalid El Khalidi, Auteur ; Amine Bourhili, Auteur ; Ingrida Bagdanavičiūtė, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7445 - 7461 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] changement d'occupation du sol
[Termes IGN] Corine Land Cover
[Termes IGN] érosion côtière
[Termes IGN] littoral méditerranéen
[Termes IGN] Maroc
[Termes IGN] orthoimage
[Termes IGN] photographie aérienne
[Termes IGN] surveillance du littoral
[Termes IGN] système d'information géographique
[Termes IGN] trait de côte
[Termes IGN] utilisation du solRésumé : (auteur) The coastal zone, a highly dynamic and complex environment, has important ecological and jurisdictional implications for governments and coastal managers. Based on the CORINE Land Cover classification system, this paper examined the effects of land use and land cover change (LULC) on the coastlines' dynamics along the ∼24 km barrier island of Nador lagoon on the Mediterranean coast of Morocco during a period of 62 years (1954–2016). The study utilized high-resolution orthoimages in the geographic information system (GIS) environment to characterize coastline evolution and LULC changes. The evolution of the coastline was assessed using a GIS tool, in particular the Digital Shoreline Analysis System (DSAS). The net rates of coastline change were calculated by using statistical methods: the End Point Rate (EPR) and the Linear Regression Rate (LRR). Results concerning the LULC changes showed that agricultural area and beach/dune classes decreased over the entire study period (62 years) by 11.14% and 28.45%, respectively. Urban fabric, shrub, forest, and saltmarsh/peat bog classes increased during the 62 years of evaluation by 2.69%, 19.92%, 16.77%, and 0.19%, respectively. Results regarding coastal analysis indicated that the accretion and erosion processes along the barrier island of the Nador lagoon (∼24km) were observed at 45% (10.6 km) and 55% (12.8 km) of the coastline, respectively. The beaches of Oulad Zehra and Oulad Aissa were characterized by erosion (−0.58 m/yr to −0.57 m/yr respectively), while accretion was observed on the beaches of Boukana and Kariat Arkmane at rates of +2.15 m/yr and +0.82 m/yr, respectively. This study highlighted that natural and anthropogenic processes have a strong influence on the erosion/accretion trends identified along the barrier island of Nador lagoon. The changes in LULC have affected the barrier island of the lagoon in two different forms: (1) a significant spatial conversion due to dune reforestation and (2) a fundamental spatial modification that affects the sea-lagoon connection (inlet) and the construction of new hard engineering structures. Numéro de notice : A2022-927 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1974958 Date de publication en ligne : 15/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1974958 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102660
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 7445 - 7461[article]Transfer learning from citizen science photographs enables plant species identification in UAV imagery / Salim Soltani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
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Titre : Transfer learning from citizen science photographs enables plant species identification in UAV imagery Type de document : Article/Communication Auteurs : Salim Soltani, Auteur ; Hannes Feilhauer, Auteur ; Robbert Duker, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données naturalistes
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] espèce végétale
[Termes IGN] filtrage de la végétation
[Termes IGN] identification de plantes
[Termes IGN] image captée par drone
[Termes IGN] orthoimage couleur
[Termes IGN] science citoyenne
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter scale acquired with Unoccupied Aerial Vehicles (UAV). However, such tasks often require ample training data, which is commonly generated in the field via geocoded in-situ observations or labeling remote sensing data through visual interpretation. Both approaches are laborious and can present a critical bottleneck for CNN applications. An alternative source of training data is given by using knowledge on the appearance of plants in the form of plant photographs from citizen science projects such as the iNaturalist database. Such crowd-sourced plant photographs typically exhibit very different perspectives and great heterogeneity in various aspects, yet the sheer volume of data could reveal great potential for application to bird’s eye views from remote sensing platforms. Here, we explore the potential of transfer learning from such a crowd-sourced data treasure to the remote sensing context. Therefore, we investigate firstly, if we can use crowd-sourced plant photographs for CNN training and subsequent mapping of plant species in high-resolution remote sensing imagery. Secondly, we test if the predictive performance can be increased by a priori selecting photographs that share a more similar perspective to the remote sensing data. We used two case studies to test our proposed approach with multiple RGB orthoimages acquired from UAV with the target plant species Fallopia japonica and Portulacaria afra respectively. Our results demonstrate that CNN models trained with heterogeneous, crowd-sourced plant photographs can indeed predict the target species in UAV orthoimages with surprising accuracy. Filtering the crowd-sourced photographs used for training by acquisition properties increased the predictive performance. This study demonstrates that citizen science data can effectively anticipate a common bottleneck for vegetation assessments and provides an example on how we can effectively harness the ever-increasing availability of crowd-sourced and big data for remote sensing applications. Numéro de notice : A2022-488 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100016 Date de publication en ligne : 23/05/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100956
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022) . - n° 100016[article]Adaptive transfer of color from images to maps and visualizations / Mingguang Wu in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)
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Titre : Adaptive transfer of color from images to maps and visualizations Type de document : Article/Communication Auteurs : Mingguang Wu, Auteur ; Yanjie Sun, Auteur ; Yaqian Li, Auteur Année de publication : 2022 Article en page(s) : pp 289 - 312 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] amélioration des couleurs
[Termes IGN] couleur (rédaction cartographique)
[Termes IGN] données vectorielles
[Termes IGN] esthétique cartographique
[Termes IGN] orthoimage couleur
[Termes IGN] relation sémantique
[Termes IGN] saillance
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Because crafting attractive and effective colors from scratch is a high-effort and time-consuming process in map and visualization design, transferring color from an inspiration source to maps and visualizations is a promising technique for both novices and experts. To date, existing image-to-image color transfer methods suffer from ambiguities and inconsistencies; no computational approach is available to transfer color from arbitrary images to vector maps. To fill this gap, we propose a computational method that transfers color from arbitrary images to a vector map. First, we classify reference images into regions with measures of saliency. Second, we quantify the communicative quality and esthetics of colors in maps; we then transform the problem of color transfer into a dual-objective, multiple-constraint optimization problem. We also present a solution method that can create a series of optimal color suggestions and generate a communicative quality-esthetic compromise solution. We compare our method with an image-to-image method based on two sample maps and six reference images. The results indicate that our method is adaptive to mapping scales, themes, and regions. The evaluation also provides preliminary evidence that our method can achieve better communicative quality and harmony. Numéro de notice : A2022-478 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1982009 Date de publication en ligne : 10/11/2021 En ligne : https://doi.org/10.1080/15230406.2021.1982009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100826
in Cartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 289 - 312[article]Effect of label noise in semantic segmentation of high resolution aerial images and height data / Arabinda Maiti in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkLearning from the past: crowd-driven active transfer learning for semantic segmentation of multi-temporal 3D point clouds / Michael Kölle in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkHybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy / Norbert Haala in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)PermalinkComparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)PermalinkNeural map style transfer exploration with GANs / Sidonie Christophe in International journal of cartography, vol 8 n° 1 (March 2022)PermalinkAutomatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkPermalinkContribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery / Calimanut-Ionut Cira (2022)PermalinkPhotogrammetric 3D mobile mapping of rail tracks / Philipp Glira in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)PermalinkBuilding detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)Permalink