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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 IGN] artefact [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] planification urbaine [Termes IGN] réseau social [Termes IGN] sentiment [Termes IGN] Sydney (Nouvelle-Galles du Sud) [Termes IGN] traitement du langage naturel [Termes IGN] transport public [Termes 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]

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 IGN] changement d'utilisation du sol [Termes IGN] croissance urbaine [Termes IGN] développement durable [Termes IGN] image Landsat [Termes IGN] impact sur l'environnement [Termes IGN] multiple endmember spectral mixture analysis [Termes IGN] réseau neuronal artificiel [Termes IGN] service écosystémique [Termes IGN] Sydney (Nouvelle-Galles du Sud) [Termes 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 IGN] apprentissage non-dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] déconvolution [Termes IGN] image à haute résolution [Termes IGN] réseau neuronal artificiel [Termes IGN] scène [Termes 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]

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

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 10 (October 2004) . - pp 1173 - 1177 Titre : GPS and GIS assisted radar interferometry Type de document : Article/Communication Auteurs : Linlin Ge, Auteur ; X. Li, Auteur ; Chris Rizos, Auteur ; M. Omura, Auteur Année de publication : 2004 Article en page(s) : pp 1173 - 1177 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] carrière souterraine [Termes IGN] géoréférencement [Termes IGN] image radar moirée [Termes IGN] interferométrie différentielle [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] mine [Termes IGN] positionnement par GPS [Termes IGN] réflecteur [Termes IGN] subsidence [Termes IGN] surveillance géologique [Termes IGN] Sydney (Nouvelle-Galles du Sud) Résumé : (Auteur) Error in radar satellite orbit determination is a common problem in radar interferometry (INSAR). For example, when we try to locate a radar test site with known geographic coordinates using the geocoding information in SLC (the latitude and longitude of the four image corners), the location is well away from the true position. Another example is when there is a significant disturbance in the differential INSAR result, we sometimes are not sure whether it is from ground deformation or atmospheric heterogeneity. Even after these are corrected, we need to export the INSAR results to a GIS format so that they can be overlaid as layers over orthophotos and mine plans (in the case of mining subsidence) in order to interpret the results. Therefore, it is proposed to use both GPS and GIS to assist radar interferometry. Results are presented with an application to monitoring subsidence due to underground mining southwest of Sydney, Australia. Numéro de notice : A2004-365 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26892 [article]

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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)

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 13 (July 2004) . - pp 2625 - 2639 Titre : Integrating imaging spectroscopy (445-2543nm) and geographic information systems for post-disaster management: a case of hailstorm damage in Sydney Type de document : Article/Communication Auteurs : S. Bhaskaran, Auteur ; B. Datt, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 2625 - 2639 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification spectrale [Termes IGN] correction d'image [Termes IGN] dommage matériel [Termes IGN] géoréférencement [Termes IGN] grêle [Termes IGN] habitat urbain [Termes IGN] image hyperspectrale [Termes IGN] intégration de données [Termes IGN] précipitation [Termes IGN] réalité de terrain [Termes IGN] résistance des matériaux [Termes IGN] risque naturel [Termes IGN] Sydney (Nouvelle-Galles du Sud) [Termes IGN] système d'information géographique [Termes IGN] télédétection aérienne [Termes IGN] urgence [Termes IGN] vulnérabilité Résumé : (Auteur) This paper demonstrates a methodology for the analysis and integration of airborne hyperspectral sensor data (445-2543nm) with GIS data in order to develop a vulnerability map which has the potential to assist in decision making during post-disaster emergency operations. Hailstorms pose a threat to people as well as property in Sydney, Australia. Emergency planning demands current, large-scale spatio-temporal information on urban areas that may be susceptible to hailstones. Several regions, dominated by less resistant roofing materials, have a higher vulnerability to hailstorm damage than others. Post-disaster operations must focus on allocating dynamic resources to these areas. Remote sensing data, particularly airborne hyperspectral sensor data, consist of spectral bands with narrow bandwidths, and have the potential to quantify and distinguish between urban features such as roofing materials and other man-made features. A spectral library of surface materials from urban areas was created by using a full range spectroradiometer. The image was atmospherically corrected using the empirical line method. A spectral angle mapper (SAM) method, which is an automated method for comparing image spectra to laboratory spectra, was used to develop a classification map that shows the distribution of roofing materials with different resistances to hailstones. Surface truthing yielded high percentage accuracy. Spatial overlay technique was performed in a GIS environment where several types of cartographic data such as special hazard locations, population density, data about less mobile people and the street network were overlaid on the classified geo-referenced hyperspectral image. The integrated database product, which merges high quality spectral information and cartographic GIS data, has vast potential to assist emergency organizations, city planners and decision makers in formulating plans and strategies for resource management. Numéro de notice : A2004-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26788 [article]

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