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Deep learning for geometric and semantic tasks in photogrammetry and remote sensing / Christian Helpke in Geo-spatial Information Science, vol 23 n° 1 (March 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 1 (March 2020) . - pp 10 - 19 Titre : Deep learning for geometric and semantic tasks in photogrammetry and remote sensing Type de document : Article/Communication Auteurs : Christian Helpke, Auteur ; Franz Rottensteiner, Auteur Année de publication : 2020 Article en page(s) : pp 10 - 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image aérienne [Termes IGN] intelligence artificielle [Termes IGN] photogrammétrie numérique [Termes IGN] télédétection Résumé : (auteur) During the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote sensing. Results have shown partly significant improvements in many projects all across the photogrammetric processing chain from image orientation to surface reconstruction, scene classification as well as change detection, object extraction and object tracking and recognition in image sequences. This paper summarizes the foundations of deep learning for photogrammetry and remote sensing before illustrating, by way of example, different projects being carried out at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, in this exciting and fast moving field of research and development. Numéro de notice : A2020-161 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2020.1718003 Date de publication en ligne : 03/02/2020 En ligne : https://doi.org/https://doi.org/10.1080/10095020.2020.1718003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94821 [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] analyse de mélange spectral d’extrémités multiples [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] 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]

An IEEE value loop of human-technology collaboration in geospatial information science / Liqiu Meng in Geo-spatial Information Science, vol 23 n° 1 (March 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 1 (March 2020) . - pp 61- 67 Titre : An IEEE value loop of human-technology collaboration in geospatial information science Type de document : Article/Communication Auteurs : Liqiu Meng, Auteur Année de publication : 2020 Article en page(s) : pp 61- 67 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique [Termes IGN] analyse géovisuelle [Termes IGN] approche holistique [Termes IGN] données localisées numériques [Termes IGN] enrichissement sémantique [Termes IGN] éthique [Termes IGN] géographie sociale [Termes IGN] information sémantique [Termes IGN] intégration de données [Termes IGN] intelligence artificielle [Termes IGN] interface homme-machine [Termes IGN] recherche interdisciplinaire [Termes IGN] web sémantique Résumé : (auteur) Geosensing and social sensing as two digitalization mainstreams in big data era are increasingly converging toward an integrated system for the creation of semantically enriched digital Earth. Along with the rapid developments of AI technologies, this convergence has inevitably brought about a number of transformations. On the one hand, value-adding chains from raw data to products and services are becoming value-adding loops composed of four successive stages – Informing, Enabling, Engaging and Empowering (IEEE). Each stage is a dynamic loop for itself. On the other hand, the “human versus technology” relationship is upgraded toward a game-changing “human and technology” collaboration. The information loop is essentially shaped by the omnipresent reciprocity between humans and technologies as equal partners, co-learners and co-creators of new values. The paper gives an analytical review on the mutually changing roles and responsibilities of humans and technologies in the individual stages of the IEEE loop, with the aim to promote a holistic understanding of the state of the art of geospatial information science. Meanwhile, the author elicits a number of challenges facing the interwoven human-technology collaboration. The transformation to a growth mind-set may take time to realize and consolidate. Research works on large-scale semantic data integration are just in the beginning. User experiences of geovisual analytic approaches are far from being systematically studied. Finally, the ethical concerns for the handling of semantically enriched digital Earth cover not only the sensitive issues related to privacy violation, copyright infringement, abuse, etc. but also the questions of how to make technologies as controllable and understandable as possible for humans and how to keep the technological ethos within its constructive sphere of societal influence. Numéro de notice : A2020-163 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10095020.2020.1718004 Date de publication en ligne : 23/01/2020 En ligne : https://doi.org/10.1080/10095020.2020.1718004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94823 [article]