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Assessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])
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Titre : Assessment and prediction of urban growth for a mega-city using CA-Markov model Type de document : Article/Communication Auteurs : Veerendra Yadav, Auteur ; Sanjay Kumar Ghosh, Auteur Année de publication : 2021 Article en page(s) : pp 1960 - 1992 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] coefficient de corrélation
[Termes IGN] croissance urbaine
[Termes IGN] mégalopole
[Termes IGN] modèle de Markov
[Termes IGN] modèle de simulation
[Termes IGN] OpenStreetMap
[Termes IGN] Tamil Nadu (Inde ; état)
[Termes IGN] urbanisationRésumé : (auteur) Most of World’s mega-cities are facing high population growth. To accommodate the increased population, new built-up areas are emerging at the periphery or fringe area of cities. New urbanisation has an adverse impact on the existing Land Use Land Cover (LULC). To monitor and analyse the impact of urbanisation, LULC change analysis has become the primary concern for LULC monitoring agencies. In this study, LULC change of Chennai has been assessed during 1981–2011 using temporal Landsat data. All the dataset has been classified using Maximum Likelihood Classifier (MLC). Quantitative change in LULC has been carried out using Pearson’s Correlation Coefficient, Transition Potential Matrix, Land Use Dynamic Degree and MLC. Further, spatio-temporal change analysis has been performed using Post-classification comparison technique. Cellular Automata-Markov (CA-Markov) Model used for LULC prediction for 2021–2051. The urban area of Chennai has increased from 40.74 to 103.52 km2 during 1981–2011. Further, LULC prediction using the CA-Markov model shows that the urban area of Chennai district may increase from 103.52 to 140.79 km2 during 2011–2051. During the period 1981–2051, the prediction model indicates that mostly vegetation and barren land will be converted into urban land class. Numéro de notice : A2021-692 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2019.1690054 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1690054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98507
in Geocarto international > vol 36 n° 17 [15/09/2021] . - pp 1960 - 1992[article]Mapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)
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Titre : Mapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches Type de document : Article/Communication Auteurs : He Zhang, Auteur ; Marijn Bauters, Auteur ; Pascal Boeckx, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 3777 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] Congo (bassin)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle numérique de terrain
[Termes IGN] photogrammétrie aérienne
[Termes IGN] point d'appui
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] surveillance forestièreRésumé : (auteur) Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests. Numéro de notice : A2021-754 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13183777 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.3390/rs13183777 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98746
in Remote sensing > vol 13 n° 18 (September-2 2021) . - n° 3777[article]3D map creation using crowdsourced GNSS data / Terence Lines in Computers, Environment and Urban Systems, vol 89 (September 2021)
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Titre : 3D map creation using crowdsourced GNSS data Type de document : Article/Communication Auteurs : Terence Lines, Auteur ; Anahid Basiri, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] approche participative
[Termes IGN] Bootstrap (statistique)
[Termes IGN] cartographie 3D
[Termes IGN] données GNSS
[Termes IGN] données localisées 2,5D
[Termes IGN] hauteur du bâti
[Termes IGN] interface de programmation
[Termes IGN] régression logistique
[Termes IGN] signal GNSS
[Termes IGN] trajet multiple
[Termes IGN] vision par ordinateurRésumé : (auteur) 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. Numéro de notice : A2021-535 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101671 Date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101671 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97998
in Computers, Environment and Urban Systems > vol 89 (September 2021)[article]Automatic building detection with polygonizing and attribute extraction from high-resolution images / Samitha Daranagama in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)
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Titre : Automatic building detection with polygonizing and attribute extraction from high-resolution images Type de document : Article/Communication Auteurs : Samitha Daranagama, Auteur ; Apichon Witayangkurn, Auteur Année de publication : 2021 Article en page(s) : n° 606 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de Douglas-Peucker
[Termes IGN] apprentissage profond
[Termes IGN] détection du bâti
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] lissage de courbe
[Termes IGN] orthophotoplan numérique
[Termes IGN] polygonation
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Buildings can be introduced as a fundamental element for forming a city. Therefore, up-to-date building maps have become vital for many applications, including urban mapping and urban expansion analysis. With the development of deep learning, segmenting building footprints from high-resolution remote sensing imagery has become a subject of intense study. Here, a modified version of the U-Net architecture with a combination of pre- and post-processing techniques was developed to extract building footprints from high-resolution aerial imagery and unmanned aerial vehicle (UAV) imagery. Data pre-processing with the logarithmic correction image enhancing algorithm showed the most significant improvement in the building detection accuracy for aerial images; meanwhile, the CLAHE algorithm improved the most concerning UAV images. This study developed a post-processing technique using polygonizing and polygon smoothing called the Douglas–Peucker algorithm, which made the building output directly ready to use for different applications. The attribute information, land use data, and population count data were applied using two open datasets. In addition, the building area and perimeter of each building were calculated as geometric attributes. Numéro de notice : A2021-684 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi10090606 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.3390/ijgi10090606 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98410
in ISPRS International journal of geo-information > vol 10 n° 9 (September 2021) . - n° 606[article]A deep translation (GAN) based change detection network for optical and SAR remote sensing images / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
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Titre : A deep translation (GAN) based change detection network for optical and SAR remote sensing images Type de document : Article/Communication Auteurs : Xinghua Li, Auteur ; Zhengshun Du, Auteur ; Yanyuan Huang, Auteur ; Zhenyu Tan, Auteur Année de publication : 2021 Article en page(s) : pp 14 - 34 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] détection de changement
[Termes IGN] image à très haute résolution
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] méthode robuste
[Termes IGN] polarisation
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal profond
[Termes IGN] zone d'intérêtRésumé : (Editeur) With the development of space-based imaging technology, a larger and larger number of images with different modalities and resolutions are available. The optical images reflect the abundant spectral information and geometric shape of ground objects, whose qualities are degraded easily in poor atmospheric conditions. Although synthetic aperture radar (SAR) images cannot provide the spectral features of the region of interest (ROI), they can capture all-weather and all-time polarization information. In nature, optical and SAR images encapsulate lots of complementary information, which is of great significance for change detection (CD) in poor weather situations. However, due to the difference in imaging mechanisms of optical and SAR images, it is difficult to conduct their CD directly using the traditional difference or ratio algorithms. Most recent CD methods bring image translation to reduce their difference, but the results are obtained by ordinary algebraic methods and threshold segmentation with limited accuracy. Towards this end, this work proposes a deep translation based change detection network (DTCDN) for optical and SAR images. The deep translation firstly maps images from one domain (e.g., optical) to another domain (e.g., SAR) through a cyclic structure into the same feature space. With the similar characteristics after deep translation, they become comparable. Different from most previous researches, the translation results are imported to a supervised CD network that utilizes deep context features to separate the unchanged pixels and changed pixels. In the experiments, the proposed DTCDN was tested on four representative data sets from Gloucester, California, and Shuguang village. Compared with state-of-the-art methods, the effectiveness and robustness of the proposed method were confirmed. Numéro de notice : A2021-574 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.007 Date de publication en ligne : 23/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98174
in ISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 14 - 34[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021091 SL Revue Centre de documentation Revues en salle Disponible 081-2021093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt GIScience integrated with computer vision for the examination of old engravings and drawings / Motti Zohar in International journal of geographical information science IJGIS, vol 35 n° 9 (September 2021)
PermalinkPermalinkA learning-based approach to automatically evaluate the quality of sequential color schemes for maps / Taisheng Chen in Cartography and Geographic Information Science, Vol 48 n° 5 (September 2021)
PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
PermalinkA multiagent systems with Petri Net approach for simulation of urban traffic networks / Mauricio Flores Geronimo in Computers, Environment and Urban Systems, vol 89 (September 2021)
PermalinkStochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network / Jussi Leinonen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])
PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)
PermalinkDeep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)
PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)
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