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Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 125 - 136 Titre : Geocoding of trees from street addresses and street-level images Type de document : Article/Communication Auteurs : Daniel Laumer, Auteur ; Nico Lang, Auteur ; Natalie Van Doorn, Auteur Année de publication : 2020 Article en page(s) : pp 125 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse des correspondances [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] image panoramique [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] inventaire [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] zone urbaine Résumé : (auteur) We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale. Numéro de notice : A2020-124 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.001 date de publication en ligne : 21/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94749 [article]

Street-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 681- 707 Titre : Street-Frontage-Net: urban image classification using deep convolutional neural networks Type de document : Article/Communication Auteurs : Stephen Law, Auteur ; Chanuki Illushka Seresinhe, Auteur ; Yao Shen, Auteur Année de publication : 2020 Article en page(s) : pp 681- 707 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] espace public [Termes descripteurs IGN] évaluation foncière [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] façade [Termes descripteurs IGN] habitat urbain [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] immobilier (secteur) [Termes descripteurs IGN] information géographique [Termes descripteurs IGN] Londres [Termes descripteurs IGN] matrice de confusion [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] paysage urbain [Termes descripteurs IGN] urbanisme [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) Quantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and doors) or blank (frontage containing walls, fences and garages). Small-scale studies have indicated that the more active the frontage, the livelier and safer a street feels. However, collecting the city-level data necessary to evaluate street frontage quality is costly. The SFN model uses a deep CNN to classify the frontage of a street. This study expands on the previous research via five experiments. We find robust results in classifying frontage quality for an out-of-sample test set that achieves an accuracy of up to 92.0%. We also find active frontages in a neighbourhood has a significant link with increased house prices. Lastly, we find that active frontage is associated with more scenicness compared to blank frontage. While further research is needed, the results indicate the great potential for using deep learning methods in geographic information extraction and urban design. Numéro de notice : A2020-110 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1555832 date de publication en ligne : 26/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1555832 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94712 [article]

Comparison of spatial modelling approaches to simulate urban growth: a case study on Udaipur city, India / Biswajit Mondal in Geocarto international, vol 35 n° 4 ([15/03/2020])  

Public [article]  inGeocarto international > vol 35 n° 4 [15/03/2020] . - pp 411 - 433 Titre : Comparison of spatial modelling approaches to simulate urban growth: a case study on Udaipur city, India Type de document : Article/Communication Auteurs : Biswajit Mondal, Auteur ; Suman Chakraborti, Auteur ; Dipendra Nath Das, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 411 - 433 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] étalement urbain [Termes descripteurs IGN] Inde [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] pente [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Assessment of past and future urban growth processes helps the decision makers to evaluate and formulate the policy documents. In an attempt to make such assessments, this study compares three commonly used urban growth models: Multicriteria Cellular Automata-Markov Chain (MCCA-MC), Multi-Layer Perception Markov Chain (MLP-MC), and the Slope, Land use, Exclusion, Urban Extent, Transportation and Hillshade (SLEUTH). This study has taken into account the land use and land cover data for the years, 1977, 1992, 2000, 2008, 2016 and prepared driving variables for urban growth. The KAPPA index of agreement indicates that the MCCA-MC, MLP-MC and SLEUTH models avoid errors by 94%, 93%, and 92% respectively. Models forecast that about 156.96 km2, 157.43 km2 and 142.43 km2 built-up areas will emerge through the process of urbanization by 2031 in the city of Udaipur. However, this assessment identified that all the models are embodied with their own advantages and disadvantages while serving specific purposes. While the MCCA-MC and MLP-MC provides a good account of the urban spread, the SLEUTH identifies the new isolated growth centres more accurately. Numéro de notice : A2020-100 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520922 date de publication en ligne : 03/01/2019 En ligne : https://doi.org/10.1080/10106049.2018.1520922 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94691 [article]

Analysing performance of SLEUTH model calibration using brute force and genetic algorithm–based methods / Ankita Saxena in Geocarto international, vol 35 n° 3 ([01/03/2020])  

Public [article]  inGeocarto international > vol 35 n° 3 [01/03/2020] . - pp 256 - 279 Titre : Analysing performance of SLEUTH model calibration using brute force and genetic algorithm–based methods Type de document : Article/Communication Auteurs : Ankita Saxena, Auteur ; Mahesh Kumar Jat, Auteur Année de publication : 2020 Article en page(s) : pp 256 - 279 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] algorithme génétique [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] Inde [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] OpenStreetMap Résumé : (auteur) Present study is aimed to compare the performance of SLEUTH model from two different calibration methods, that is, brute force and GA in term of computational efficiency of calibration processes, capturing urban growth, a form of growth or growth pattern and its spatial distribution. SLEUTH has been parameterized for Ajmer city (India) and its performance has been compared in term of eight parameters/methods, that is, computational efficiency, model fitness that is, OSM, urban shape index, best fit coefficient values, hit-miss-false alarm method, kappa statistics, accuracy percentage and visual analysis. GA-based calibration has been found to be computationally more efficient and relatively better in capturing urban growth and form of growth as compared to brute force. Brute force calibration seems to be slightly better considering urban hits as compared to GA, however, GA is better with respect to lesser false alarms. Numéro de notice : A2020-056 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1516242 date de publication en ligne : 29/11/2018 En ligne : https://doi.org/10.1080/10106049.2018.1516242 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94569 [article]

City-descriptive input data for urban climate models: Model requirements, data sources and challenges / Valéry Masson in Urban climate, vol 31 (March 2020)  

Public [article]  inUrban climate > vol 31 (March 2020) Titre : City-descriptive input data for urban climate models: Model requirements, data sources and challenges Type de document : Article/Communication Auteurs : Valéry Masson, Auteur ; Wieke Heldens, Auteur ; Erwan Bocher, Auteur ; Marion Bonhomme, Auteur ; Bénédicte Bucher , Auteur Année de publication : 2020 Projets : URCLIM / Bucher, Bénédicte Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] données localisées numériques [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] flore urbaine [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] ville [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Cities are particularly vulnerable to meteorological hazards because of the concentration of population, goods, capital stock and infrastructure. Urban climate services require multi-disciplinary and multi-sectorial approaches and new paradigms in urban climate modelling. This paper classifies the required urban input data for both mesoscale state-of-the-art Urban Canopy Models (UCMs) and microscale Obstacle Resolving Models (ORM) into five categories and reviews the ways in which they can be obtained. The first two categories are (1) land cover, and (2) building morphology. These govern the main interactions between the city and the urban climate and the Urban Heat Island. Interdependence between morphological parameters and UCM geometric hypotheses are discussed. Building height, plan and wall area densities are recommended as the main input variables for UCMs, whereas ORMs require 3D building data. Recently, three other categories of urban data became relevant for finer urban studies and adaptation to climate change: (3) building design and architecture, (4) building use, anthropogenic heat and socio-economic data, and (5) urban vegetation data. Several methods for acquiring spatial information are reviewed, including remote sensing, geographic information system (GIS) processing from administrative cadasters, expert knowledge and crowdsourcing. Data availability, data harmonization, costs/efficiency trade-offs and future challenges are then discussed. Numéro de notice : A2020-003 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.uclim.2019.100536 date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1016/j.uclim.2019.100536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94290 [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)  

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Spectral–spatial–temporal MAP-based sub-pixel mapping for land-cover change detection / Da He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)  

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An OD flow clustering method based on vector constraints: a case study for Beijing taxi origin-destination data / Xiaogang Guo in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)  

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Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data / Sukhjit Singh Sehra in Transactions in GIS, Vol 24 n° 1 (February 2020)  

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Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process / Biswajit Nath in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)  

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Object‐oriented tracking of thematic and spatial behaviors of urban heat islands / Rui Zhu in Transactions in GIS, Vol 24 n° 1 (February 2020)  

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Tree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

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A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods / Yongjiu Feng in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)  

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Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data / Yi Shi in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)  

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