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Semantic‐based urban growth prediction / Marvin Mc Cutchan in Transactions in GIS, Vol 24 n° 6 (December 2020)
[article]
Titre : Semantic‐based urban growth prediction Type de document : Article/Communication Auteurs : Marvin Mc Cutchan, Auteur ; Simge Özdal‐Oktay, Auteur ; Ioannis Giannopoulos, Auteur Année de publication : 2020 Article en page(s) : 1482 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] croissance urbaine
[Termes IGN] dynamique spatiale
[Termes IGN] Europe (géographie politique)
[Termes IGN] information sémantique
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] organisation spatiale
[Termes IGN] OWL
[Termes IGN] prévision
[Termes IGN] réseau neuronal artificiel
[Termes IGN] urbanisation
[Termes IGN] ville durableRésumé : (Auteur) Urban growth is a spatial process which has a significant impact on the earth’s environment. Research on predicting this complex process makes it therefore especially fruitful for decision‐making on a global scale, as it enables the introduction of more sustainable urban development. This article presents a novel method of urban growth prediction. The method utilizes geospatial semantics in order to predict urban growth for a set of random areas in Europe. For this purpose, a feature space representing geospatial configurations was introduced which embeds semantic information. Data in this feature space was then used to perform deep learning, which ultimately enables the prediction of urban growth with high accuracy. The final results reveal that geospatial semantics hold great potential for spatial prediction tasks. Numéro de notice : A2020-766 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12655 Date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1111/tgis.12655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96657
in Transactions in GIS > Vol 24 n° 6 (December 2020) . - 1482 - 1503[article]Understanding the role of individual units in a deep neural network / David Bau in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 48 (1 December 2020)
[article]
Titre : Understanding the role of individual units in a deep neural network Type de document : Article/Communication Auteurs : David Bau, Auteur ; Jun-Yan Zhu, Auteur ; Hendrik Strobelt, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 30071-30078 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] cadre conceptuel
[Termes IGN] détection d'objet
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal convolutif
[Termes IGN] scèneRésumé : (auteur) Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing. Numéro de notice : A2020-864 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1073/pnas.1907375117 En ligne : https://doi.org/10.1073/pnas.1907375117 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99086
in Proceedings of the National Academy of Sciences of the United States of America PNAS > vol 117 n° 48 (1 December 2020) . - n° 30071-30078[article]Learning-based hyperspectral imagery compression through generative neural networks / Chubo Deng in Remote sensing, vol 12 n° 21 (November 2020)
[article]
Titre : Learning-based hyperspectral imagery compression through generative neural networks Type de document : Article/Communication Auteurs : Chubo Deng, Auteur ; Yi Cen, Auteur ; Lifu Zhang, Auteur Année de publication : 2020 Article en page(s) : n° 3657 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage profond
[Termes IGN] compression d'image
[Termes IGN] compression par ondelettes
[Termes IGN] image hyperspectrale
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techniques or the principle component analysis (PCA) transform. In this paper, we provide an alternative approach for HSI compression via a generative neural network (GNN), which learns the probability distribution of the real data from a random latent code. This is achieved by defining a family of densities and finding the one minimizing the distance between this family and the real data distribution. Then, the well-trained neural network is a representation of the HSI, and the compression ratio is determined by the complexity of the GNN. Moreover, the latent code can be encrypted by embedding a digit with a random distribution, which makes the code confidential. Experimental examples are presented to demonstrate the potential of the GNN to solve image compression problems in the field of HSI. Compared with other algorithms, it has better performance at high compression ratio, and there is still much room left for improvements along with the fast development of deep-learning techniques. Numéro de notice : A2020-720 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213657 Date de publication en ligne : 08/11/2020 En ligne : https://doi.org/10.3390/rs12213657 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96310
in Remote sensing > vol 12 n° 21 (November 2020) . - n° 3657[article]Sea surface temperature and high water temperature occurrence prediction using a long short-term memory model / Minkyu Kim in Remote sensing, vol 12 n° 21 (November 2020)
[article]
Titre : Sea surface temperature and high water temperature occurrence prediction using a long short-term memory model Type de document : Article/Communication Auteurs : Minkyu Kim, Auteur ; Hung Yang, Auteur ; Jonghwa Kim, Auteur Année de publication : 2020 Article en page(s) : n° 3654 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] aquaculture
[Termes IGN] changement climatique
[Termes IGN] Corée du sud
[Termes IGN] données météorologiques
[Termes IGN] modèle de simulation
[Termes IGN] pêche
[Termes IGN] réseau neuronal récurrent
[Termes IGN] série temporelle
[Termes IGN] température de surface de la merRésumé : (auteur) Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry. Numéro de notice : A2020-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213654 Date de publication en ligne : 07/11/2020 En ligne : https://doi.org/10.3390/rs12213654 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96311
in Remote sensing > vol 12 n° 21 (November 2020) . - n° 3654[article]The construction of sound speed field based on back propagation neural network in the global ocean / Junting Wang in Marine geodesy, vol 43 n° 6 (November 2020)
[article]
Titre : The construction of sound speed field based on back propagation neural network in the global ocean Type de document : Article/Communication Auteurs : Junting Wang, Auteur ; Tianhe Xu, Auteur ; Wenfeng Nie, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 621 - 642 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] fonction orthogonale
[Termes IGN] interpolation spatiale
[Termes IGN] milieu marin
[Termes IGN] onde acoustique
[Termes IGN] propagation du son
[Termes IGN] réseau neuronal artificiel
[Termes IGN] salinité
[Termes IGN] sondage acoustique
[Termes IGN] température
[Termes IGN] vitesseRésumé : (auteur) The sound speed is a key parameter that affects the underwater acoustic positioning and navigation. Aiming at the high-precision construction of sound speed field in the complex marine environment, this paper proposes a sound speed field model based on back propagation neural network (BPNN) by considering the correlation of learning samples. The method firstly uses measured ocean parameters to construct the temperature and salinity field. Then the spatial position, the temperature and the salinity information are used to construct the global ocean sound speed field based on the back propagation neural network algorithm. During the processing, the learning samples of back propagation neural network are selected based on the correlation between sound speed and distance. The proposed algorithm is validated by the global Argo data as well as compared with the spatial interpolation and the empirical orthogonal function (EOF) algorithm. The results demonstrate that the average root mean squares of the BPNN considering the correlation of learning samples is 0.352 m/s compared to the 1.527 m/s of EOF construction and the 2.661 m/s of spatial interpolation, with an improvement of 76.9% and 86.8%. Therefore, the proposed algorithm can improve the construction accuracy of sound speed field in the complex marine environment. Numéro de notice : A2020-694 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1815912 Date de publication en ligne : 14/09/2020 En ligne : https://doi.org/10.1080/01490419.2020.1815912 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96242
in Marine geodesy > vol 43 n° 6 (November 2020) . - pp 621 - 642[article]Urban expansion in Auckland, New Zealand: a GIS simulation via an intelligent self-adapting multiscale agent-based model / Tingting Xu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)PermalinkApplication of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data / Mahdi Moalla in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkCompensation of geometric parameter errors for terrestrial laser scanner by integrating intensity correction / Wanli Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkGround-based remote sensing of forests exploiting GNSS signals / Leila Guerriero in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkHeliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkExtraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkClassification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkEstimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study / Mir Reza Ghaffari Razin in GPS solutions, Vol 24 n° 3 (July 2020)PermalinkUsing machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests / Jiaxin Chen in Forest ecology and management, Vol 466 (15 June 2020)Permalink