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Automated extraction and validation of Stone Pine (Pinus pinea L.) trees from UAV-based digital surface models / Asli Ozdarici-Ok in Geo-spatial Information Science, vol 26 n° inconnu ([01/08/2023])  

Public [article]  inGeo-spatial Information Science > vol 26 n° inconnu [01/08/2023] Titre : Automated extraction and validation of Stone Pine (Pinus pinea L.) trees from UAV-based digital surface models Type de document : Article/Communication Auteurs : Asli Ozdarici-Ok, Auteur ; Ali Ozgun Ok, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface [Termes IGN] Pinus pinea [Termes IGN] semis de points [Termes IGN] Turquie Résumé : (auteur) Stone Pine (Pinus pinea L.) is currently the pine species with the highest commercial value with edible seeds. In this respect, this study introduces a new methodology for extracting Stone Pine trees from Digital Surface Models (DSMs) generated through an Unmanned Aerial Vehicle (UAV) mission. We developed a novel enhanced probability map of local maxima that facilitates the computation of the orientation symmetry by means of new probabilistic local minima information. Four test sites are used to evaluate our automated framework within one of the most important Stone Pine forest areas in Antalya, Turkey. A Hand-held Mobile Laser Scanner (HMLS) was utilized to collect the reference point cloud dataset. Our findings confirm that the proposed methodology, which uses a single DSM as an input, secures overall pixel-based and object-based F1-scores of 88.3% and 97.7%, respectively. The overall median Euclidean distance revealed between the automatically extracted stem locations and the manually extracted ones is computed to be 36 cm (less than 4 pixels), demonstrating the effectiveness and robustness of the proposed methodology. Finally, the comparison with the state-of-the-art reveals that the outcomes of the proposed methodology outperform the results of six previous studies in this context. Numéro de notice : A2022-620 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2022.2090864 Date de publication en ligne : 21/07/2022 En ligne : https://doi.org/10.1080/10095020.2022.2090864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101364 [article]

Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery / Maryam Hosseini in Computers, Environment and Urban Systems, vol 101 (April 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 101 (April 2023) . - n° 101950 Titre : Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery Type de document : Article/Communication Auteurs : Maryam Hosseini, Auteur ; Andres Sevtsuk, Auteur ; Fabio Miranda, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'objet [Termes IGN] Etats-Unis [Termes IGN] image aérienne [Termes IGN] navigation pédestre [Termes IGN] segmentation sémantique [Termes IGN] système d'information géographique [Termes IGN] trottoir [Termes IGN] vision par ordinateur Résumé : (auteur) While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. Numéro de notice : A2023-187 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2023.101950 Date de publication en ligne : 22/02/2023 En ligne : https://doi.org/10.1016/j.compenvurbsys.2023.101950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102961 [article]

Automatic detection of thin oil films on water surfaces in ultraviolet imagery / Ming Xie in Photogrammetric record, vol 38 n° 181 (March 2023)  

Public [article]  inPhotogrammetric record > vol 38 n° 181 (March 2023) . - pp 47 - 62 Titre : Automatic detection of thin oil films on water surfaces in ultraviolet imagery Type de document : Article/Communication Auteurs : Ming Xie, Auteur ; Xiurui Zhang, Auteur ; Ying Li, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 47 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] détection automatique [Termes IGN] filtre optique [Termes IGN] hydrocarbure [Termes IGN] image AVIRIS [Termes IGN] marée noire [Termes IGN] niveau de gris (image) [Termes IGN] rayonnement ultraviolet [Termes IGN] segmentation d'image [Termes IGN] seuillage binaire [Termes IGN] surface de la mer Résumé : (auteur) Among the various remote sensing technologies that have been applied to monitor oil spills on the sea surface, passive ultraviolet (UV) imaging is a controversial one that has raised some disputes in the community of oil spill remote sensing. As a result, the research and applications of oil spill detection using passive UV imaging have not been as developed as other methods. In order to clarify some existing questions on oil spill detection using passive UV remote sensing technology, this paper discusses the needs of thin oil film detection, examines the feasibility of thin oil film detection using passive UV imaging through field experiments under controlled conditions and validates it with the UV imagery derived from the airborne visible/infrared imaging spectrometer (AVIRIS) observation of the Deepwater Horizon oil spill. Two types of fully automatic models are designed to extract the thin oil films on the water surface: (1) a binary classification model based on an adaptive threshold; (2) an unsupervised image segmentation model based on image clustering and greyscale histogram analysis. The two models are tested on the UV imagery obtained through both field experiments and AVIRIS observations. The results indicate that the binary classification model can extract the thin oil films with reasonable accuracy under stable imaging conditions, while the unsupervised image clustering model can robustly detect the thin oil films at the cost of higher computational complexity. These results infer that passive UV imaging is an effective way to detect thin oil films and could be applied to provide early warning at the beginning stage of oil spills and reduce further damage. It may also be applied as a supplementary method for oil spill detection to achieve comprehensive oil spill monitoring. Numéro de notice : A2023-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12439 Date de publication en ligne : 09/02/2023 En ligne : https://doi.org/10.1111/phor.12439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102866 [article]

Multi-sensor airborne lidar requires intercalibration for consistent estimation of light attenuation and plant area density / Grégoire Vincent in Remote sensing of environment, vol 286 (March 2023)  

Public [article]  inRemote sensing of environment > vol 286 (March 2023) . - n° 113442 Titre : Multi-sensor airborne lidar requires intercalibration for consistent estimation of light attenuation and plant area density Type de document : Article/Communication Auteurs : Grégoire Vincent, Auteur ; Philippe Verley, Auteur ; Benjamin Brede, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 113442 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] canopée [Termes IGN] densité de la végétation [Termes IGN] données lidar [Termes IGN] forêt tropicale [Termes IGN] Guyane (département français) [Termes IGN] image captée par drone [Termes IGN] plan de vol [Termes IGN] rayonnement lumineux [Termes IGN] réflectance végétale [Termes IGN] semis de points [Termes IGN] zone d'intérêt Résumé : (auteur) Leaf area is a key structural characteristic of forest canopies because of the role of leaves in controlling many biological and physical processes occurring at the biosphere-atmosphere transition. High pulse density Airborne Laser Scanning (ALS) holds promise to provide spatially resolved and accurate estimates of plant area density (PAD) in forested landscapes, a key step in understanding forest functioning: phenology, carbon uptake, transpiration, radiative balance etc. Inconsistencies between different ALS sensors is a barrier to generating globally harmonised PAD estimates. The basic assumption on which PAD estimation is based is that light attenuation is proportional to vegetation area density. This study shows that the recorded extinction strongly depends on target detectability which is influenced by laser characteristics (power, sensitivity, wavelength). Three different airborne laser scanners were flown over a wet tropical forest at the Paracou research station in French Guiana. Different sensors, flight heights and transmitted power levels were compared. Light attenuation was retrieved with an open source ray-tracing code (http://amapvox.org). Direct comparison revealed marked differences (up-to 25% difference in profile-averaged light attenuation rate and 50% difference at particular heights) that could only be explained by differences in scanner characteristics. We show how bias which may occur under various acquisition conditions can generally be mitigated by a sensor intercalibration. Alignment of light weight lidar attenuation profiles to ALS reference attenuation profiles is not always satisfactory and we discuss what are the likely sources of discrepancies. Neglecting the dependency of apparent light attenuation on scanner properties may lead to biases in estimated vegetation density commensurate to those affecting light attenuation estimates. Applying intercalibration procedures supports estimation of plant area density independent of acquisition characteristics. Numéro de notice : A2023-169 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113442 Date de publication en ligne : 06/01/2023 En ligne : https://doi.org/10.1016/j.rse.2022.113442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102928 [article]

Validation of Island 3D-mapping based on UAV spatial point cloud optimization: a case study in Dongluo Island of China / Jian Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 3 (March 2023)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 3 (March 2023) . - pp 173 - 182 Titre : Validation of Island 3D-mapping based on UAV spatial point cloud optimization: a case study in Dongluo Island of China Type de document : Article/Communication Auteurs : Jian Wu, Auteur ; Shifeng Fu, Auteur ; Peng Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 173 - 182 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] cartographie 3D [Termes IGN] Chine [Termes IGN] île [Termes IGN] image captée par drone [Termes IGN] modélisation 3D [Termes IGN] semis de points [Termes IGN] télédétection aérienne Résumé : (Auteur) The unmanned aerial vehicle (UAV) remote sensing is of small volume, low cost, fine timeliness, and high spatial resolution, and has the special advantage on island surveying. Focus on the inaccurate elevation of non-ground point cloud without lidar device, this study explored a methodology for island three-dimensional (3D) mapping and modelling based on spatial point clouds optimization with a K-Nearest Neighbors Adaptive Inverse Distance Weighted (K-AIDW) interpolation algorithm. By classifying the UAV point clouds into ground, vegatetation, and structure, the K-AIDW algorithm was applied to optimize the elevations of non-ground point clouds (vegetation and structure) to recalculate Z values. The aerophotogrammetry result was generated based on the optimized spatial point clouds. Finally, the 3D model of Dongluo Island was reconstructed and rendered in Metashape. The accuracy evaluation result shows that the max-errors of ground control points (–0.0154 in X, 0.0305 in Y, and 0.0133 in Z) and the checkpoints (–0.091 in X, –0.176 in Y, and 0.338 in Z) can meet the error-tolerance requirements of the corresponding terrain on the 1:500 scale set by the national standard of GB/T 23236-2009 in China. It is found that the K-AIDW algorithm displayed the best Z accuracy (root-mean-square error of 0.2538) compared with IDW (0.3668) and no-optimized (1.6012), proving it is an effective methodology for improving 3D-modelling accuracy of island. Numéro de notice : A2023-172 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00109R2 Date de publication en ligne : 01/03/2023 En ligne : https://doi.org/10.14358/PERS.22-00109R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102923 [article]

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Comparative analysis of different CNN models for building segmentation from satellite and UAV images / Batuhan Sariturk in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 2 (February 2023)  

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Detection of growth change of young forest based on UAV RGB images at single-tree level / Xiaocheng Zhou in Forests, vol 14 n° 1 (January 2023)  

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Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)  

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A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)  

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How to optimize the 2D/3D urban thermal environment: Insights derived from UAV LiDAR/multispectral data and multi-source remote sensing data / Rongfang Lyu in Sustainable Cities and Society, vol 88 (January 2023)  

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PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)  

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Des relevés sur mesure pour la sentinelle des Pyrénées / Marielle Mayo in Géomètre, n° 2209 (janvier 2023)

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Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning / Stefano Puliti in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)  

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Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)  

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UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) / Martin Štroner in European journal of remote sensing, vol 56 n° 1 (2023)  

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