Descripteur
Documents disponibles dans cette catégorie (94)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])
[article]
Titre : Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics Type de document : Article/Communication Auteurs : E. Banzhaf, Auteur ; H. Kollai, Auteur ; A. Kindler, Auteur Année de publication : 2020 Article en page(s) : pp 623 - 640 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse d'image orientée objet
[Termes IGN] base de données orientée objet
[Termes IGN] base de données urbaines
[Termes IGN] bati
[Termes IGN] bien-être collectif
[Termes IGN] cartographie urbaine
[Termes IGN] développement durable
[Termes IGN] données lidar
[Termes IGN] écosystème urbain
[Termes IGN] gestion urbaine
[Termes IGN] orthophotographie
[Termes IGN] population urbaine
[Termes IGN] santé
[Termes IGN] télédétectionRésumé : (auteur) Mapping urban structures is a vital prerequisite for urban planners to enhance their database for a liveable city dedicated to sustainable development. Therefore, it is significant to measure urban grey and green structures at the scale of local districts to understand the urban structure and residential needs for urban ecosystem services. For a detailed analysis we exploit digital orthophotos (DOP), LiDAR data, and vital statistics. We use remote sensing techniques to create an Object-based Image Analysis (OBIA) that differentiates grey and green structures with high precision and at refined scale. This spatial information is linked with allocated population and health-related indicators to identify built-up types with highest population densities and local districts with deficits in the provision of different green structures. Our results show the share of built-up structures and the contribution of green structures to urban ecosystem services, human health and well-being at local district level. Numéro de notice : A2020-202 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1524514 Date de publication en ligne : 23/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1524514 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94877
in Geocarto international > vol 35 n° 6 [01/05/2020] . - pp 623 - 640[article]A method for urban population density prediction at 30m resolution / Krishnachandran Balakrishnan in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)
[article]
Titre : A method for urban population density prediction at 30m resolution Type de document : Article/Communication Auteurs : Krishnachandran Balakrishnan, Auteur Année de publication : 2020 Article en page(s) : pp 193 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] densité de population
[Termes IGN] gestion urbaine
[Termes IGN] hauteur du bâti
[Termes IGN] image Cartosat-1
[Termes IGN] Inde
[Termes IGN] logiciel de traitement d'image
[Termes IGN] modèle de simulation
[Termes IGN] modélisation du bâti
[Termes IGN] système d'information géographique
[Termes IGN] véhicule automobileRésumé : (auteur) This paper proposes a new method for urban population density prediction at 30 m resolution. Using data for Bangalore, the paper demonstrates that population within each 30 m residential built-up cell can be modeled as a function of cell-level data on street density and building heights and ward-level data on car ownership. Building-height data were generated from Cartosat-1 stereo imagery using an open-source satellite stereo image processing software. Using this building-height data in conjunction with the other datasets, the paper demonstrates that a 30 m resolution population density surface can be generated such that, when summed to the ward level, the median absolute percentage error between predicted population and known census population at the ward level is 8.29%. The paper also shows that the relationship between population density, street density, building height, and ward level car ownership is spatially non-stationary. A fine-grained understanding of urban population densities, as enabled by the proposed method, can be beneficial to research, policy, and practice related to cities. Numéro de notice : A2020-168 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1687014 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/15230406.2019.1687014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94839
in Cartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 193 - 213[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2020031 RAB Revue Centre de documentation En réserve L003 Disponible A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation / Kevin Sparks in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)
[article]
Titre : A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation Type de document : Article/Communication Auteurs : Kevin Sparks, Auteur ; Gautam Thakur, Auteur ; Amol Pasarkar, Auteur ; Marie Urban, Auteur Année de publication : 2020 Article en page(s) : pp 759 - 776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] analyse spatio-temporelle
[Termes IGN] climat urbain
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] coutume
[Termes IGN] déformation temporelle dynamique (algorithme)
[Termes IGN] démographie
[Termes IGN] données géophysiques
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] estimation quantitative
[Termes IGN] ethnologie
[Termes IGN] géographie sociale
[Termes IGN] gestion urbaine
[Termes IGN] milieu urbain
[Termes IGN] modèle dynamique
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] point d'intérêt
[Termes IGN] réseau social
[Termes IGN] trace numériqueRésumé : (auteur) The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish. Numéro de notice : A2020-294 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615069 Date de publication en ligne : 04/06/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615069 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95126
in International journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 759 - 776[article]A deep learning architecture for semantic address matching / Yue Lin in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)
[article]
Titre : A deep learning architecture for semantic address matching Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Mengjun Kang, Auteur ; Yuyang Wu, Auteur Année de publication : 2020 Article en page(s) : pp 559 - 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] appariement d'adresses
[Termes IGN] appariement sémantique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] géocodage par adresse postale
[Termes IGN] gestion urbaine
[Termes IGN] inférence sémantique
[Termes IGN] représentation vectorielle
[Termes IGN] réseau neuronal profond
[Termes IGN] Shenzhen
[Termes IGN] similitude sémantique
[Termes IGN] traitement du langage naturelRésumé : (auteur) Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set. Numéro de notice : A2020-106 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1681431 Date de publication en ligne : 24/10/2019 En ligne : https://doi.org/10.1080/13658816.2019.1681431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94702
in International journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 559 - 576[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020031 RAB Revue Centre de documentation En réserve L003 Disponible A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data / Sheng Hu in Computers, Environment and Urban Systems, vol 80 (March 2020)
[article]
Titre : A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data Type de document : Article/Communication Auteurs : Sheng Hu, Auteur ; Zhanjun He, Auteur ; Liang Wu, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] données massives
[Termes IGN] espace urbain
[Termes IGN] extraction de données
[Termes IGN] gestion urbaine
[Termes IGN] image à haute résolution
[Termes IGN] point d'intérêt
[Termes IGN] regroupement de données
[Termes IGN] télédétection spatiale
[Termes IGN] traitement du langage naturel
[Termes IGN] Wuhan (Chine)
[Termes IGN] zone urbaineRésumé : (auteur) Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geo-data. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a center-context pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed. Numéro de notice : A2020-191 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2019.101442 Date de publication en ligne : 15/11/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101442 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94853
in Computers, Environment and Urban Systems > vol 80 (March 2020)[article]A novel method of spatiotemporal dynamic geo-visualization of criminal data, applied to command and control centers for public safety / Mayra Salcedo-Gonzalez in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkRoad network structure and ride-sharing accessibility: A network science perspective / Mingshu Wang in Computers, Environment and Urban Systems, vol 80 (March 2020)PermalinkPermalinkDevelopment of a GIS and model-based method for optimizing the selection of locations for drinking water extraction by means of riverbank filtration / Yan Zhou (2020)PermalinkPermalinkPermalinkDevelopment of a Protocol to Convert and Manage Underground Infrastructure Maps into Geographic Information Systems (GIS) Format / Guillemette Fonteix (2018)PermalinkPermalinkMise en place d’un outil de classification et d’utilisation des données LiDAR pour l’étude du couvert arboré à Florence / Florian Thill (2018)PermalinkL'île de Ré, un SIG très aérien / Anonyme in Géomatique expert, n° 115 (mars - avril 2017)Permalink