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A machine learning approach for detecting rescue requests from social media / Zheye Wang in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 11 (November 2022) . - n° 570 Titre : A machine learning approach for detecting rescue requests from social media Type de document : Article/Communication Auteurs : Zheye Wang, Auteur ; Nina S.N. Lam, Auteur ; Mingxuan Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 570 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage automatique [Termes IGN] code postal [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données issues des réseaux sociaux [Termes IGN] Etats-Unis [Termes IGN] filtrage d'information [Termes IGN] secours d'urgence [Termes IGN] tempête [Termes IGN] terminologie [Termes IGN] Twitter Résumé : (auteur) Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different machine learning models. We investigate how the outcomes of our ZIP code filtering differ from those of a recent, comparable study in terms of generating training data for machine learning models. Following this, experiments are conducted to test how the existence of ZIP codes would affect the performance of machine learning models by simulating different percentages of ZIP-code-tagged positive samples. The findings show that (1) all machine learning classifiers except K-nearest neighbors and Naïve Bayes achieve state-of-the-art performance in detecting rescue requests from social media; (2) using ZIP code filtering could increase the effectiveness of gathering rescue requests for training machine learning models; (3) machine learning models are better able to identify rescue requests that are associated with ZIP codes. We thereby encourage every rescue-seeking victim to include ZIP codes when posting messages on social media. This study is a useful addition to the literature and can be helpful for first responders to rescue disaster victims more efficiently. Numéro de notice : A2022-846 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11110570 Date de publication en ligne : 16/11/2022 En ligne : https://doi.org/10.3390/ijgi11110570 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102081 [article]

Mapping and assessing coastal resilience in the Caribbean region / Nina S.N. Lam in Cartography and Geographic Information Science, Vol 42 n° 4 (September 2015)  

Public [article]  inCartography and Geographic Information Science > Vol 42 n° 4 (September 2015) . - pp 315 - 322 Titre : Mapping and assessing coastal resilience in the Caribbean region Type de document : Article/Communication Auteurs : Nina S.N. Lam, Auteur ; Yi Qiang, Auteur ; Helbert Arenas, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 315 - 322 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Caraïbes [Termes IGN] cyclone [Termes IGN] estimation statistique [Termes IGN] outil d'aide à la décision [Termes IGN] représentation cartographique [Termes IGN] résilience écologique [Termes IGN] risque naturel [Termes IGN] surveillance du littoral [Termes IGN] système d'information géographique [Termes IGN] tempête [Termes IGN] vulnérabilité Résumé : (Auteur) Assessing the vulnerability and resilience to coastal hazards is a critical worldwide issue, especially for hurricane-prone coastal regions such as the Caribbean. However, the development of a useful metric for vulnerability and resilience assessment has a lot of challenges. Cartography and GIS analysis can contribute effectively to the solution of the issue by integrating natural and human data layers for assessment, mapping, and visualization. This paper uses the new Resilience Inference Measurement (RIM) model to assess the resilience of 25 countries in the Caribbean region to hurricanes. The RIM indices of the countries were computed using three variables representing three dimensions: exposure, damage, and recovery, and eight variables representing social-environmental capacity. The RIM resilience indices were mapped and compared with the vulnerability indices computed in a previous study. The results show that Turks & Caicos Islands had the highest resilience, whereas Montserrat had the lowest. This paper contributes to the hazard literature by demonstrating new vulnerability and resilience assessment methodologies that include validation and enable inference. The paper also contributes to the cartography and GIS literature by demonstrating the need to integrate data and perspectives from multiple disciplines and regions, as well as the ability of geospatial technology, in producing useful decision-making tools for a very pressing societal problem. Numéro de notice : A2015-514 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1040999 En ligne : https://doi.org/10.1080/15230406.2015.1040999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77714 [article]

Exemplaires(1)

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032-2015041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Wavelet for urban spatial feature discrimination: comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches / Nina S.N. Lam in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 7 (July 2004)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 7 (July 2004) . - pp 803 - 812 Titre : Wavelet for urban spatial feature discrimination: comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches Type de document : Article/Communication Auteurs : Nina S.N. Lam, Auteur ; S.W. Myint, Auteur ; J.M. Tyler, Auteur Année de publication : 2004 Article en page(s) : pp 803 - 812 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse fractale [Termes IGN] autocorrélation spatiale [Termes IGN] classification dirigée [Termes IGN] image multibande [Termes IGN] matrice [Termes IGN] milieu urbain [Termes IGN] précision de la classification [Termes IGN] transformation en ondelettes Résumé : (Auteur) Traditional image processing techniques have proven inadequate for urban mapping using high spatial resolution remote-sensing images. This study examined and evaluated wavelet transforms for urban texture analysis and image classification using high spatial resolution ATLAS imagery. For the purpose of comparison and to evaluate the effectiveness of the wavelet approaches, two different fractal approaches (isarithm and triangular prism), spatial autocorrelation (Moran's I and Geary's C), and spatial co-occurrence matrix of the selected urban classes were examined using 65 X 65, 33 X 33, and 17 X 17 samples with a pixel size of 2.5 m. Results from this study suggest that a multi-band and multi-level wavelet approach can be used to drastically increase the classification accuracy. The fractal techniques did not provide satisfactory classification accuracy. Spatial autocorrelation and spatial co-occurrence techniques were found to be relatively effective when compared to the fractal approaches. It can be concluded that the wavelet transform approach is the most accurate of all four approaches. Numéro de notice : A2004-273 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.7.803 En ligne : https://doi.org/10.14358/PERS.70.7.803 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26800 [article]

An elevation of fractal methods for characterizing image complexity / Nina S.N. Lam in Cartography and Geographic Information Science, vol 29 n° 1 (January 2002)  

Public [article]  inCartography and Geographic Information Science > vol 29 n° 1 (January 2002) . - pp 25 - 35 Titre : An elevation of fractal methods for characterizing image complexity Type de document : Article/Communication Auteurs : Nina S.N. Lam, Auteur ; H.L. Qiu, Auteur ; et al., Auteur Année de publication : 2002 Article en page(s) : pp 25 - 35 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] autocorrélation spatiale [Termes IGN] complexité [Termes IGN] dimension fractale [Termes IGN] exploration de données [Termes IGN] objet fractal [Termes IGN] simulation de surface Résumé : (Auteur) Previously, we developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remote sensing data. This paper evaluates three fractal dimension measurement methods that have been implemented in ICAMS: isarithm, variogram, and a modified version of triangular prism. To provide insights into how the fractal methods compare with conventional spatial techniques in measuring landscape complexity, the performance of two spatial autocorrelation methods, Moran's 1 and Geary's C, is also evaluated. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of surfaces having higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all surfaces, particularly those with high tractor dimensions. As with the fractal techniques, spatial autocorrelation techniques have been found to be useful for measuring complex images, but not images with low dimensionality. Fractal measurement methods, as well as spatial autocorrelation techniques, can be applied directly to unclassified images and could serve as a tool for change detection and data mining. Numéro de notice : A2002-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1559/152304002782064600 En ligne : https://doi.org/10.1559/152304002782064600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21965 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-02011	RAB	Revue	Centre de documentation	En réserve L003	Disponible