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Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101628 Titre : Flood depth mapping in street photos with image processing and deep neural networks Type de document : Article/Communication Auteurs : Bahareh Alizadeh Kharazi, Auteur ; Amir H. Behzadan, Auteur Année de publication : 2021 Article en page(s) : n° 101628 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] centre urbain [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] crue [Termes IGN] détection de contours [Termes IGN] Etats-Unis [Termes IGN] image Streetview [Termes IGN] inondation [Termes IGN] profondeur [Termes IGN] signalisation routière [Termes IGN] système d'aide à la décision [Termes IGN] traitement d'image [Termes IGN] transformation de Hough [Termes IGN] zone urbaine Résumé : (auteur) Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings. Numéro de notice : A2021-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101628 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97620 [article]

An area merging method in map generalization considering typical characteristics of structured geographic objects / Chengming Li in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 3 (May 2021) . - pp 210 - 224 Titre : An area merging method in map generalization considering typical characteristics of structured geographic objects Type de document : Article/Communication Auteurs : Chengming Li, Auteur ; Yong Yin, Auteur ; Pengda Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 210 - 224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Chine [Termes IGN] conflit d'intégration [Termes IGN] détection de contours [Termes IGN] fusion de données [Termes IGN] généralisation automatique de données [Termes IGN] objet géographique zonal [Termes IGN] occupation du sol [Termes IGN] programmation adaptée à l'objet [Termes IGN] structure spatiale [Termes IGN] tessellation [Termes IGN] ville [Termes IGN] zone tampon [Vedettes matières IGN] Généralisation Résumé : (auteur) Merging is an important operation in the map generalization of land-cover and other coverages. We define structured geographic objects as collections of adjacent areas with homogeneous semantics that are regularly arranged as spatial structures. Existing studies have concentrated on unstructured objects, which will lead to the structured ones losing part or even most of the typical characteristics during merging. Therefore, as a supplement to the existing mature merging method, a targeted method was proposed in this paper to address the merging problem of structured geographic objects. First, structured geographic objects were classified into four typical patterns, and they were identified automatically according to seven spatial structure parameters. Second, a Miter-type buffer transformation was introduced to extract the overall boundary of structured geographic objects, and areas inside the overall boundary were processed with the most appropriate merging operations for their pattern. Finally, the corresponding merged results of structured geographic objects were inserted back into the merged result of the original land-cover data by using the NOT operation, and the spatial conflicts near the boundary were adjusted. We test our method for a dataset of geographical census data for a city in China. The experimental results revealed that compared with state-of-the-art method, the proposed method produces more reasonable generalization result by effectively identifying and maintaining the typical spatial structures; moreover, the proposed method also preserves the planar tessellation characteristic of land-cover data and the balance of area variation in each land-cover class. Numéro de notice : A2021-489 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1863862 date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1080/15230406.2020.1863862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97530 [article]

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Delineation of cities based on scaling properties of urban patterns: a comparison of three methods / Gaëtan Montero in International journal of geographical information science IJGIS, vol 35 n° 5 (May 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 5 (May 2021) . - pp 919 - 947 Titre : Delineation of cities based on scaling properties of urban patterns: a comparison of three methods Type de document : Article/Communication Auteurs : Gaëtan Montero, Auteur ; Cécile Tannier, Auteur ; Isabelle Thomas, Auteur Année de publication : 2021 Article en page(s) : pp 919 - 947 Note générale : Bibliothèque Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] Bruxelles [Termes IGN] délimitation [Termes IGN] intersection spatiale [Termes IGN] morphologie urbaine [Termes IGN] reconstruction 2D du bâti [Termes IGN] site urbain [Termes IGN] ville Résumé : (Auteur) Identifying urban boundaries involves analysing both the functional and the morphological aspects of urban systems. In this paper, we adopt a purely morphological approach and compare three methods for the morphological delineation of cities. Each method avoids using any predefined quantified threshold (size, distance, built density, etc.) to detect crucial discontinuities in space. The first method identifies Natural Cities by clustering points. The other two are the fractal-based MorphoLim method and the Hierarchical Percolation; both involve transforming the data using a step-by-step dilation process. The three methods are critically compared and illustrated by applications to theoretical urban patterns. We further apply each method to the urban agglomeration of Brussels, the Belgian capital, using different data (building footprints, building centroids and street nodes) and considering two study areas, namely the former province of Brabant and the entire country of Belgium. The results show that it is impossible to draw an unambiguous morphological boundary for an urban agglomeration. Consequently, it is crucial to relate the data used, the size of the study area and the method chosen to the objectives of the delineation. Numéro de notice : A2021-335 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1817462 date de publication en ligne : 12/01/2021 En ligne : https://doi.org/10.1080/13658816.2020.1817462 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97554 [article]

Urban agglomeration worsens spatial disparities in climate adaptation / Seung-Kyum Kim in Scientific reports, vol 11 (2021)  

Public [article]  inScientific reports > vol 11 (2021) . - n° 8446 Titre : Urban agglomeration worsens spatial disparities in climate adaptation Type de document : Article/Communication Auteurs : Seung-Kyum Kim, Auteur ; Mia M. Bennett, Auteur ; Terry van Gevelt, Auteur ; Paul Joosse, Auteur Année de publication : 2021 Article en page(s) : n° 8446 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agglomération [Termes IGN] changement climatique [Termes IGN] disparité [Termes IGN] espace vert [Termes IGN] Kouangtoung (Chine) [Termes IGN] prise en compte du contexte [Termes IGN] vulnérabilité Résumé : (auteur) Many countries promote urban agglomeration to enhance economic competitiveness, but the impacts of this strategy on local climate adaptation remain poorly understood. Here, we use variation in greenspaces to test the effectiveness of climate adaptation policy across climate impacts and vulnerability dimensions. Using satellite imagery and logistic regression, we analyze spatiotemporal correlation between greenspace and climate vulnerability in the Guangdong-Hong Kong-Macau Greater Bay Area, an area comprising ~ 70 million people and 11 cities, making it a useful natural experiment for our study. We find that while greenspace increases proportionally with climate exposure and sensitivity, many cities exhibit discrepancies between greenspace variation and climate vulnerability. Green adaptation funnels into wealthier, less vulnerable areas while bypassing more vulnerable ones, increasing their climate vulnerability and undermining the benefits of urban agglomeration. The results suggest that centrally-planned climate adaptation policy must accommodate local heterogeneity to improve urban sustainability. By neglecting local heterogeneity, urban agglomeration policy risks exacerbating spatial inequalities in climate adaptation. Numéro de notice : A2021-307 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Actes DOI : 10.1038/s41598-021-87739-1 date de publication en ligne : 19/04/2021 En ligne : https://doi.org/10.1038/s41598-021-87739-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97662 [article]

Learning from urban form to predict building heights / Nikola Milojevic-Dupont in Plos one, vol 15 n° 12 (December 2020)  

Public [article]  inPlos one > vol 15 n° 12 (December 2020) . - n° 0242010 Titre : Learning from urban form to predict building heights Type de document : Article/Communication Auteurs : Nikola Milojevic-Dupont, Auteur ; Nicolai Hans, Auteur ; Lynn H. Kaack, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0242010 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Allemagne [Termes IGN] apprentissage automatique [Termes IGN] base de connaissances [Termes IGN] données localisées des bénévoles [Termes IGN] France (administrative) [Termes IGN] hauteur du bâti [Termes IGN] Italie [Termes IGN] morphologie urbaine [Termes IGN] OpenStreetMap [Termes IGN] Pays-Bas [Termes IGN] ville Résumé : (auteur) Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change. Numéro de notice : A2020-830 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1371/journal.pone.0242010 date de publication en ligne : 09/12/2020 En ligne : https://doi.org/10.1371/journal.pone.0242010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97658 [article]

The Urban Climate Services URCLIM project / Valéry Masson in Climate Services, vol 20 (December 2020)  

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Monitoring population dynamics in the Pearl River Delta from 2000 to 2010 / Sisi Yu in Geocarto international, vol 35 n° 14 ([15/10/2020])  

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Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)  

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Urban climate services: climate impact projections and their uncertainties at city scale / Bert Van Schaeybroeck in FMI's climate bulletin research letters, vol 2020 n° 1 (Spring 2020)    

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

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Estimation of metabolic flows of urban environment based on fuzzy expert knowledge / Igor Patrakeyev in Geodesy and cartography, vol 46 n° 1 (January 2020)  

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La modélisation en géographie / Denise Pumain (2020)

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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)  

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Nouvelle donne aérienne / Marielle Mayo in Géomètre, n° 2175 (décembre 2019)

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Cartographic delimitation of the city centre using mental sketches / Kamil Nieścioruk in Cartographic journal (the), Vol 56 n° 4 (November 2019)  

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