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Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? / Oliver Lock in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 4 (December 2020) . - pp 275 - 292 Titre : Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? Type de document : Article/Communication Auteurs : Oliver Lock, Auteur ; Chris Pettit, Auteur Année de publication : 2020 Article en page(s) : pp 275 - 292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] artefact [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données massives [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] sentiment [Termes descripteurs IGN] Sydney (Nouvelle-Galles du Sud) [Termes descripteurs IGN] traitement du langage naturel [Termes descripteurs IGN] transport public [Termes descripteurs IGN] ville intelligente Résumé : (auteur) We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion. Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city. With such pressures on existing public transportation systems, this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services. This research forms a case study of the use of passively collected forms of big data in cities – focusing on Sydney, Australia. Firstly, it examines social media data (Tweets) related to public transport performance. Secondly, it joins this to longitudinal big data – delay information continuously broadcast by the network over a year, thus forming hundreds of millions of data artifacts. Topics, tones, and sentiment are modeled using machine learning and Natural Language Processing (NLP) techniques. These resulting data, and models, are compared to opinions derived from a citizen survey among users. The validity of such data and models versus the intentions of users, in the context of systems that monitor and improve transport performance, are discussed. As such, key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques. Numéro de notice : A2020-787 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1815596 date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.1080/10095020.2020.1815596 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96545 [article]

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches / S.M. Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 89 (July 2020) . - n° 102085 Titre : Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches Type de document : Article/Communication Auteurs : S.M. Hamylton, Auteur ; R.H. Morris, Auteur ; R.C. Carvalho, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 102085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] Nouvelle-Galles du Sud [Termes descripteurs IGN] pesticide [Termes descripteurs IGN] réserve naturelle [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance de la végétation Résumé : (auteur) We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected. Numéro de notice : A2020-716 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102085 date de publication en ligne : 03/03/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102085 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96287 [article]

Assessing environmental impacts of urban growth using remote sensing / John C. Trinder in Geo-spatial Information Science, vol 23 n° 1 (March 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 1 (March 2020) . - pp 20 - 39 Titre : Assessing environmental impacts of urban growth using remote sensing Type de document : Article/Communication Auteurs : John C. Trinder, Auteur ; Qingxiang Liu, Auteur Année de publication : 2020 Article en page(s) : pp 20 - 39 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] développement durable [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] impact sur l'environnement [Termes descripteurs IGN] multiple endmember spectral mixture analysis [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] Sydney (Nouvelle-Galles du Sud) [Termes descripteurs IGN] Wuhan (Chine) Résumé : (auteur) This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities. Numéro de notice : A2020-162 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2019.1710438 date de publication en ligne : 21/01/2020 En ligne : https://doi.org/10.1080/10095020.2019.1710438 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94822 [article]

Remote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5148 - 5157 Titre : Remote sensing scene classification by unsupervised representation learning Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Xiangtao Zheng, Auteur ; Yuan Yuan, Auteur Année de publication : 2017 Article en page(s) : pp 5148 - 5157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage non-dirigé [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] déconvolution [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] scène [Termes descripteurs IGN] Sydney (Nouvelle-Galles du Sud) Résumé : (Auteur) With the rapid development of the satellite sensor technology, high spatial resolution remote sensing (HSR) data have attracted extensive attention in military and civilian applications. In order to make full use of these data, remote sensing scene classification becomes an important and necessary precedent task. In this paper, an unsupervised representation learning method is proposed to investigate deconvolution networks for remote sensing scene classification. First, a shallow weighted deconvolution network is utilized to learn a set of feature maps and filters for each image by minimizing the reconstruction error between the input image and the convolution result. The learned feature maps can capture the abundant edge and texture information of high spatial resolution images, which is definitely important for remote sensing images. After that, the spatial pyramid model (SPM) is used to aggregate features at different scales to maintain the spatial layout of HSR image scene. A discriminative representation for HSR image is obtained by combining the proposed weighted deconvolution model and SPM. Finally, the representation vector is input into a support vector machine to finish classification. We apply our method on two challenging HSR image data sets: the UCMerced data set with 21 scene categories and the Sydney data set with seven land-use categories. All the experimental results achieved by the proposed method outperform most state of the arts, which demonstrates the effectiveness of the proposed method. Numéro de notice : A2017-664 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2702596 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2702596 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87103 [article]

Surveying graffiti, an emerging culture / Anonyme in Position, n° 81 (February - March 2016)

Public [article]  inPosition > n° 81 (February - March 2016) . - pp 40 - 42 Titre : Surveying graffiti, an emerging culture Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2016 Article en page(s) : pp 40 - 42 Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] dessin [Termes descripteurs IGN] droit [Termes descripteurs IGN] espace public [Termes descripteurs IGN] Nouvelle-Galles du Sud Numéro de notice : A2016-237 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80713 [article]

Updated best practice for EDM calibrations in New South Wales / Volker Janssen in Position, n° 78 (August - September 2015)

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How good is AUSGeoid09 in the Blue Mountains ? / Joseph Allerton in Position, n° 77 (June - July 2015)

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Markov land cover change modeling using pairs of time-series satellite images / Priyakant Sinha in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

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GPS and GIS assisted radar interferometry / L. Ge in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 10 (October 2004)

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Integrating imaging spectroscopy (445-2543nm) and geographic information systems for post-disaster management: a case of hailstorm damage in Sydney / S. Bhaskaran in International Journal of Remote Sensing IJRS, vol 25 n° 13 (July 2004)

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Densification surveys in New South Wales coping with distortions / D.A. Kinlyside (1992)

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The use of Landsat MSS data to determine the distribution of locust eggbeds in the Riverina region of New South Wales, Australia / K.P. Bryceson in International Journal of Remote Sensing IJRS, vol 10 n° 11 (November 1989)

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Large area crop classification in New South Wales, Australia, using Landsat data / K.W. Dawbin in International Journal of Remote Sensing IJRS, vol 9 n° 2 (February 1988)

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Nouvelle géographie universelle / E. Reclus (1889)

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