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A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery / Bo Yang in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
[article]
Titre : A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery Type de document : Article/Communication Auteurs : Bo Yang, Auteur ; Lin Liu, Auteur ; Minxuan Lan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1740 - 1764 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] coefficient de corrélation
[Termes IGN] criminalité
[Termes IGN] données spatiotemporelles
[Termes IGN] géostatistique
[Termes IGN] historique des données
[Termes IGN] image NPP-VIIRS
[Termes IGN] krigeage
[Termes IGN] modèle dynamique
[Termes IGN] nuit
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] prédiction
[Termes IGN] prévention des risques
[Termes IGN] prise de vue nocturne
[Termes IGN] test statistique
[Termes IGN] zone urbaineRésumé : (auteur) Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test. Numéro de notice : A2020-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1737701 Date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1737701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95622
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1740 - 1764[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020091 RAB Revue Centre de documentation En réserve L003 Disponible X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
[article]
Titre : X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data Type de document : Article/Communication Auteurs : Danfeng Hong, Auteur ; Naoto Yokoya, Auteur ; Gui-Song Sia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 12 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] bruit blanc
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] scène urbaine
[Termes IGN] transmission de donnéesRésumé : (auteur) This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods. Numéro de notice : A2020-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.014 Date de publication en ligne : 11/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95770
in ISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 12 - 23[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt SemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)
[article]
Titre : SemCity Toulouse: a benchmark for building instance segmentation in satellite images Type de document : Article/Communication Auteurs : Ribana Roscher, Auteur ; Michele Volpi, Auteur ; Clément Mallet , Auteur ; Lukas Drees, Auteur ; Jan Dirk Wegner, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 5, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 5 Article en page(s) : pp 109 - 116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] instance
[Termes IGN] Toulouse
[Termes IGN] zone urbaine denseRésumé : (auteur) In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger. The data set addresses interested researchers from various communities such as photogrammetry and remote sensing, but also computer vision and machine learning. Numéro de notice : A2020-503 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-5-2020-109-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-5-2020-109-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95639
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-5-2020 (August 2020) . - pp 109 - 116[article]Extraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])
[article]
Titre : Extraction of urban built-up areas from nighttime lights using artificial neural network Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Giovanni Coco, Auteur ; Jay Gao, Auteur Année de publication : 2020 Article en page(s) : pp 1049 - 1066 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aménagement du territoire
[Termes IGN] bati
[Termes IGN] cartographie urbaine
[Termes IGN] classification dirigée
[Termes IGN] développement durable
[Termes IGN] échantillonnage
[Termes IGN] éclairage public
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] rayonnement lumineux
[Termes IGN] réseau neuronal artificiel
[Termes IGN] seuillage
[Termes IGN] température au sol
[Termes IGN] zone urbaineRésumé : (auteur) The spatial distribution of urban areas at the national and regional scales is critical for urban planners and governments to design sustainable and environment-friendly future development plans. The nighttime lights (NTL) data provide an effective way to monitor the urban at different scales however is usually achieved by using empirical threshold-based algorithms. This study proposed a novel Artificial Neural Network (ANN) approach, using moderate resolution imageries as NTL, MODIS NDVI and land surface temperature data, to map urban areas. Both random and maximum dissimilarity distance algorithm sampling methods were considered and compared. The validation of the urban areas extracted from MDA-based ANN against the 2011 US national land cover data showed a reasonable quality (overall accuracy = 97.84; Kappa = 0.74) and achieved more accurate result than the threshold method. This study demonstrates that ANN can provide an effective, rapid, and accurate alternative in extracting urban built-up areas from NTL. Numéro de notice : A2020-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1559887 Date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2018.1559887 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95488
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1049 - 1066[article]Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
[article]
Titre : Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction Type de document : Article/Communication Auteurs : Qing Zhu, Auteur ; Zhendong Wang, Auteur ; Han Hu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 26 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] appariement de points
[Termes IGN] éclairage
[Termes IGN] image aérienne
[Termes IGN] image terrestre
[Termes IGN] maillage
[Termes IGN] milieu urbain
[Termes IGN] modèle stéréoscopique
[Termes IGN] séparateur à vaste marge
[Termes IGN] valeur aberranteRésumé : (auteur) Integration of aerial and ground images has been proved as an efficient approach to enhance the surface reconstruction in urban environments. However, as the first step, the feature point matching between aerial and ground images is remarkably difficult, due to the large differences in viewpoint and illumination conditions. Previous studies based on geometry-aware image rectification have alleviated this problem, but the performance and convenience of this strategy are still limited by several flaws, e.g. quadratic image pairs, segregated extraction of descriptors and occlusions. To address these problems, we propose a novel approach: leveraging photogrammetric mesh models for aerial-ground image matching. The methods have linear time complexity with regard to the number of images. It explicitly handles low overlap using multi-view images. The proposed methods can be directly injected into off-the-shelf structure-from-motion (SFM) and multi-view stereo (MVS) solutions. First, aerial and ground images are reconstructed separately and initially co-registered through weak georeferencing data. Second, aerial models are rendered to the initial ground views, in which color, depth and normal images are obtained. Then, feature matching between synthesized and ground images are conducted through descriptor searching and geometry-constrained outlier removal. Finally, oriented 3D patches are formulated using the synthesized depth and normal images and the correspondences are propagated to the aerial views through patch-based matching. Experimental evaluations using five datasets reveal satisfactory performance of the proposed methods in aerial-ground image matching, which succeeds in all of the ten challenging pairs compared to only three for the second best. In addition, incorporation of existing SFM and MVS solutions enables more complete reconstruction results, with better internal stability. Numéro de notice : A2020-351 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.024 Date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95234
in ISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 26 - 40[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)PermalinkCyclists' exposure to air pollution and noise in Mexico City : contribution of real-time traffic density indicators integrated into GIS / Philippe Apparicio in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)PermalinkDense stereo matching strategy for oblique images that considers the plane directions in urban areas / Jianchen Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkInteroperable information model for geovisualization and interaction in XR environments / Daeil Seo in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)PermalinkMoGUS, un outil de modélisation et d'analyse comparative des trames urbaines / Dominique Badariotti in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)PermalinkMutualiser la donnée pour une information utile / Jean-Marie Séïté in Géomètre, n° 2182 (juillet - août 2020)PermalinkSemi-automatic identification of submarine pipelines with synthetic aperture sonar Images / Victor Hugo Fernandes in Marine geodesy, Vol 43 n° 4 (July 2020)PermalinkA simple distributed water balance model for an urbanized river basin using remote sensing and GIS techniques / Olutoyin Adeola Fashae in Geocarto international, vol 35 n° 9 ([01/07/2020])PermalinkSimulating urban land use change by integrating a convolutional neural network with vector-based cellular automata / Yaqian Zhai in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)PermalinkTemporal and spatial variations of monsoonal upwelling along the South West and East coasts of India / Shailee Patel in Marine geodesy, Vol 43 n° 4 (July 2020)Permalink