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A thematic mapping method to assess and analyze potential urban hazards and risks caused by flooding / Mohammad Khalid Hossain in Computers, Environment and Urban Systems, vol 79 (January 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 79 (January 2020) Titre : A thematic mapping method to assess and analyze potential urban hazards and risks caused by flooding Type de document : Article/Communication Auteurs : Mohammad Khalid Hossain, Auteur ; Qingmin Meng, Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Alabama (Etats-Unis) [Termes descripteurs IGN] aléa [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] ethnographie [Termes descripteurs IGN] inondation [Termes descripteurs IGN] risque naturel [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] vulnérabilité [Termes descripteurs IGN] zone inondable [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) About 30% of the total global economic loss inflicted by natural hazards is caused by flooding. Among them, the most serious situation is urban flooding. Urban impervious surface enhances storm runoff and overwhelms the drainage capacity of the storm sewer system, while the urban socioeconomic characteristics most often exacerbate them even more vulnerable to urban flooding impacts. Currently, there is still a significant knowledge gap of comparable assessment and understanding of minority's and non-minority's vulnerability. Therefore, this study designs a quantitative thematic mapping method–location quotient (LQ), using Birmingham, Alabama, USA as the study area. Urban residents' vulnerability to flooding is then analyzed demographically using LQ with census data. Comparing with the widely used social vulnerability index (SVI), LQ is more robust, which not only provides more detailed measurements of both the minority's and the White's vulnerability, but also shows a direct comparison for all populations with finer information about their potential spatial risk assessment. Although SVI showed the Shades Creek is the most vulnerable area with a SVI value above 0.75, only 228 Hispanic people and 2290 African-American live there that is not a significant aggregation of minorities in Birmingham; however, a total White population 12,872 is identified by LQ with a significant aggregation in the Shades Creek. Overall, LQ suggests that the White populations are highly and significantly concentrated in the flood areas, while SVI never considered the White as vulnerable. LQ further indicates that the concentration of minorities (i.e., 88,895) and vulnerable houses (i.e., 26,235) are much higher compared to the numbers of the minorities and houses indicated by SVI, which are only 11,772 and 8323, respectively. The LQ based thematic mapping, as a promising method for vulnerability assessment of urban hazards and risks, can make a significant contribution to hazard management efforts to reduce urban vulnerability and hence enhance urban resilience to hazards in the future. Numéro de notice : A2020-002 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2019.101417 date de publication en ligne : 14/09/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101417 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93621 [article]

An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 50 - 62 Titre : An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data Type de document : Article/Communication Auteurs : Puzhao Zhang, Auteur ; Andrea Nascetti, Auteur ; Yifang Ban, Auteur ; Maoguo Gong, Auteur Année de publication : 2019 Article en page(s) : pp 50 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] incendie [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Short Waves InfraRed Résumé : (auteur) Compared with optical sensors, the all-weather and day-and-night imaging ability of Synthetic Aperture Radar (SAR) makes it competitive for burnt area mapping. This study investigates the potential of Sentinel-1 C-band SAR sensors in burnt area mapping with an implicit Radar Convolutional Burn Index (RCBI). Based on multitemporal Sentinel-1 SAR data, a convolutional networks-based classification framework is proposed to learn the RCBI for highlighting the burnt areas. We explore the mapping accuracy level that can be achieved using SAR intensity and phase information for both VV and VH polarizations. Moreover, we investigate the decorrelation of Interferometric SAR (InSAR) coherence to wildfire events using different temporal baselines. The experimental results on two recent fire events, Thomas Fire (Dec., 2017) and Carr Fire (July, 2018) in California, demonstrate that the learnt RCBI has a better potential than the classical log-ratio operator in highlighting burnt areas. By exploiting both VV and VH information, the developed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the Thomas Fire and Carr Fire, respectively. Numéro de notice : A2019-545 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.013 date de publication en ligne : 04/10/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94189 [article]

Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 155 - 170 Titre : Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images Type de document : Article/Communication Auteurs : Cheolhee Yoo, Auteur ; Daehyeon Han, Auteur ; Jungho Im, Auteur ; Benjamin Bechtel, Auteur Année de publication : 2019 Article en page(s) : pp 155 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Chicago (Illinois) [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] Madrid (Espagne) [Termes descripteurs IGN] Rome [Termes descripteurs IGN] World Urban Database and Access Portal Tools [Termes descripteurs IGN] zone urbaine dense Résumé : (Auteur) The Local Climate Zone (LCZ) scheme is a classification system providing a standardization framework to present the characteristics of urban forms and functions, especially for urban heat island (UHI) research. Landsat-based 100 m resolution LCZ maps have been classified by the World Urban Database and Portal Tool (WUDAPT) method using a random forest (RF) machine learning classifier. Some studies have proposed modified RF and convolutional neural network (CNN) approaches. This study aims to compare CNN with an RF classifier for LCZ mapping in great detail. We designed five schemes (three RF-based schemes (S1–S3) and two CNN-based ones (S4–S5)), which consist of various combinations of input features from bitemporal Landsat 8 data over four global mega cities: Rome, Hong Kong, Madrid, and Chicago. Among the five schemes, the CNN-based one with the incorporation of a larger neighborhood information showed the best classification performance. When compared to the WUDAPT workflow, the overall accuracies for entire land cover classes (OA) and for urban LCZ types (i.e., LCZ1-10; OAurb) increased by about 6–8% and 10–13%, respectively, for the four cities. The transferability of LCZ models for the four cities were evaluated, showing that CNN consistently resulted in higher accuracy (increased by about 7–18% and 18–29% for OA and OAurb, respectively) than RF. This study revealed that the CNN classifier classified particularly well for the specific LCZ classes in which buildings were mixed with trees or buildings or plants were sparsely distributed. The research findings can provide a basis for guidance of future LCZ classification using deep learning. Numéro de notice : A2019-495 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.009 date de publication en ligne : 19/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93728 [article]

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Placial analysis of events: a case study on criminological places / Sunghwan Cho in Cartography and Geographic Information Science, Vol 46 n° 6 (November 2019)  

Public [article]  inCartography and Geographic Information Science > Vol 46 n° 6 (November 2019) . - pp 547-566 Titre : Placial analysis of events: a case study on criminological places Type de document : Article/Communication Auteurs : Sunghwan Cho, Auteur ; May Yuan, Auteur Année de publication : 2019 Article en page(s) : pp 547-566 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] cartographie statistique [Termes descripteurs IGN] criminalité [Termes descripteurs IGN] Dallas (Texas) [Termes descripteurs IGN] détection d'événement [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] interaction humain-espace [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] zone à risque Résumé : (auteur) The contrast of space and place has long been an active topic of scholarly discussions in many disciplines. While spatial analysis enjoys a multitude of quantitative methods, the study of place remains mostly conceptual and descriptive. This paper expands upon the rich concepts of place in the literature to propose a quantitative framework for placial analysis based on events. Central to the proposed framework are three assumptions: (1) human experiences transform space to place; (2) events build human experiences in space; and (3) places emerge organically and may change characters, spatial extent and location over time through the shifts in occurrences and types of events in space and time. The proposed framework consists of three elements: clustering events, decomposing event distributions, and identifying the similarity of event clusters. We applied the framework to identify criminological places in the City of Dallas in the United States and the changes of these places from 1 June 2014 to 30 May 2018. Numéro de notice : A2019-417 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1578265 date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.1080/15230406.2019.1578265 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93544 [article]

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Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (2 October 2019)  

Public [article]  inRemote sensing > Vol 11 n° 20 (2 October 2019) . - 18 p. Titre : Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data Type de document : Article/Communication Auteurs : Emanuele Santi, Auteur ; Mohammed Dabboor, Auteur ; Simone Pettinato, Auteur ; Simonetta Paloscia, Auteur Année de publication : 2019 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande C [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] Manitoba (Canada) [Termes descripteurs IGN] polarimétrie [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surface cultivée Résumé : (auteur) This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (O°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than O°, with correlation coe?cients up to R ' 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of O° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized O°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9. Numéro de notice : A2019-555 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202451 date de publication en ligne : 22/10/2019 En ligne : https://doi.org/10.3390/rs11202451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94210 [article]

Estimating pasture biomass and canopy height in brazilian savanna using UAV photogrammetry / Juliana Batistoti in Remote sensing, Vol 11 n° 20 (2 October 2019)  

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Automatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Landsats 1–5 multispectral scanner system sensors radiometric calibration update / Cibele Teixeira-Pinto in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

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Mapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Multi-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy / Yan Zhou in Transactions in GIS, Vol 23 n°5 (October 2019)  

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sUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar / Paul J. Kinzel in Remote sensing, vol 11 n° 19 (October 2019)  

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Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador / Nguyen-Thanh Son in Geocarto international, vol 34 n° 12 ([15/09/2019])  

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Co-seismic displacement and waveforms of the 2018 Alaska earthquake from high-rate GPS PPP velocity estimation / Shuanggen Jin in Journal of geodesy, vol 93 n° 9 (September 2019)  

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A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena / Guiming Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

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