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Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n°12 (December 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°12 (December 2019) . - pp 2406 - 2423 Titre : Extracting urban landmarks from geographical datasets using a random forests classifier Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Yuyang Cai, Auteur ; Yue Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2406 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] point de repère [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] représentation mentale spatiale [Termes descripteurs IGN] saillance [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] ville Résumé : (auteur) Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods. Numéro de notice : A2019-426 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1620238 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1620238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93559 [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]

Land-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images / Temulun Tangud in Geocarto international, vol 34 n° 11 (November 2019)  

Public [article]  inGeocarto international > vol 34 n° 11 (November 2019) . - pp 1237 - 1251 Titre : Land-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images Type de document : Article/Communication Auteurs : Temulun Tangud, Auteur ; Kenlo Nasahara, Auteur ; Habura Borjigin, Auteur ; Hasi Bagan, Auteur Année de publication : 2019 Article en page(s) : pp 1237 - 1251 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] maillage [Termes descripteurs IGN] Mongolie intérieure (Chine) [Termes descripteurs IGN] prairie [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] steppe [Termes descripteurs IGN] zone aride Résumé : (Auteur) The Inner Mongolian steppe is a vast grassland ecosystem that has long been home to nomadic pastoralists. However, this steppe is experiencing grassland degradation as well as more frequent sand storms. The objective of this study was to detect land-cover changes in the Wulagai grassland of Inner Mongolia using multi-temporal Landsat images from 1986 to 2014, and to determine the factors driving these changes and their impacts. Land-cover maps for 1986, 1995, 2000, 2006 and 2014 were produced using the Support Vector Machine method. Subsequently, 300 m × 300 m grid-cell vector map which covered Wulagai grassland was made to detect land-cover changes and correlations between land-cover classes. The results show degradation trend from 1986 to 2014. Grid-cell-based spatial correlation analysis confirmed a strong negative correlation between grassland and barren, indicating that grassland degradation in this region is due to the regional modernization over the past 28 years. Numéro de notice : A2019-464 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1478457 date de publication en ligne : 01/06/2018 En ligne : https://doi.org/10.1080/10106049.2018.1478457 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93607 [article]

New method for environmental monitoring in armed conflict zones: a case study of Syria / Samira Mobaied in Environmental Monitoring and Assessment, vol 191 n° 11 (November 2019)  

Public [article]  inEnvironmental Monitoring and Assessment > vol 191 n° 11 (November 2019) Titre : New method for environmental monitoring in armed conflict zones: a case study of Syria Type de document : Article/Communication Auteurs : Samira Mobaied, Auteur ; Jean-Paul Rudant , Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] dégradation de l'environnement [Termes descripteurs IGN] guerre [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] protection de la biodiversité [Termes descripteurs IGN] risque environnemental [Termes descripteurs IGN] Syrie [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] zone à risque Résumé : (auteur) Today, armed conflict affects some twenty countries, covering an area making up 11% of the surface area of the Earth. Any degradation of nature in these areas represents a harmful depletion of the world’s natural heritage. Despite this, environmental issues are neglected during these periods of conflict, considered secondary to the urgency of restoring peace and safeguarding human life. Yet their consequences are potentially severe. In these areas, it is future generations who will suffer the effects of the current devastation for a very long time. In this context, the method developed in this study, named (Geographic Information System) for Environmental Monitoring in Wartime, can be used to calculate a risk indicator for environmental degradation, spatial monitoring and risk management. This will make it possible to identify the main threats to protected areas, catalogue the damage caused to the environment by armed conflicts and create a dynamic risk map. In this paper, GIS-EMW has been applied to calculate a risk indicator for environmental degradation in Syria. Numéro de notice : A2019-508 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10661-019-7805-5 date de publication en ligne : 10/10/2019 En ligne : https://doi.org/10.1007/s10661-019-7805-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93794 [article]

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

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 63 - 76 Titre : Automatic canola mapping using time series of Sentinel 2 images Type de document : Article/Communication Auteurs : Davoud Ashourloo, Auteur ; Hamid Salehi Shahrabi, Auteur ; Mohsen Azadbakht, Auteur ; Hossein Aghighi, Auteur ; Hamed Nematollahi, Auteur ; Abbas Alimohammadi, Auteur ; Ali Akbar Matkan, Auteur Année de publication : 2019 Article en page(s) : pp 63 - 76 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] agriculture de précision [Termes descripteurs IGN] Brassica napus subsp. napus [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Iran [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Oklahoma (Etats-Unis) [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] série temporelle Résumé : (Auteur) Different techniques utilized for mapping various crops are mainly based on using training dataset. But, due to difficulties of access to a well-represented training data, development of automatic methods for detection of crops is an important need which has not been considered as it deserves. Therefore, main objective of present study was to propose a new automatic method for canola (Brassica napus L.) mapping based on Sentinel 2 satellite time series data. Time series data of three study sites in Iran (Moghan, Gorgan, Qazvin) and one site in USA: (Oklahoma), were used. Then, spectral reflectance values of canola in various spectral bands were compared with those of the other crops during the growing season. NDVI, Red and Green spectral bands were successfully applied for automatic identification of canola flowering date using the threshold values. Examination of the fisher function indicated that multiplication of the near-infrared (NIR) band by the sum of red and green bands during the flowering date is an efficient index to differentiate canola from the other crops. The Kappa and overall accuracy (OA) for the four study sites were more than 0.75 and 88%, respectively. Results of this research demonstrated the potential of the proposed approach for canola mapping using time series of remotely sensed data. Numéro de notice : A2019-317 Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.08.007 date de publication en ligne : 09/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93355 [article]

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