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A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101807 Titre : A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Bo Zhou, Auteur ; Shuai Jin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] arbre aléatoire minimum [Termes IGN] distribution spatiale [Termes IGN] noeud [Termes IGN] Pékin (Chine) [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] réseau neuronal de graphes [Termes IGN] taxinomie [Termes IGN] trafic routier [Termes IGN] triangulation de Delaunay [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) Land-use classification plays an important role in urban planning and resource allocation and had contributed to a wide range of urban studies and investigations. With the development of crowdsourcing technology and map services, points of interest (POIs) have been widely used for recognizing urban land-use types. However, current research methods for land-use classifications have been limited to extracting the spatial relationship of POIs in research units. To close this gap, this study uses a graph-based data structure to describe the POIs in research units, with graph convolutional networks (GCNs) being introduced to extract the spatial context and urban land-use classification. First, urban scenes are built by considering the spatial context of POIs. Second, a graph structure is used to express the scenes, where POIs are treated as graph nodes. The spatial distribution relationship of POIs is considered to be the graph's edges. Third, a GCN model is designed to extract the spatial context of the scene by aggregating the information of adjacent nodes within the graph and urban land-use classification. Thus, the land-use classification can be treated as a classification on a graphic level through deep learning. Moreover, the POI spatial context can be effectively extracted during classification. Experimental results and comparative experiments confirm the effectiveness of the proposed method. Numéro de notice : A2022-375 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101807 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100622 [article]

Detecting land use and land cover change on Barbuda before and after the Hurricane Irma with respect to potential land grabbing: A combined volunteered geographic information and multi sensor approach / Andreas Rienow in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102732 Titre : Detecting land use and land cover change on Barbuda before and after the Hurricane Irma with respect to potential land grabbing: A combined volunteered geographic information and multi sensor approach Type de document : Article/Communication Auteurs : Andreas Rienow, Auteur ; Jan Schweighöfer, Auteur ; Torben Dedring, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102732 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] anthropisation [Termes IGN] Antilles (îles des) [Termes IGN] carte thématique [Termes IGN] changement d'occupation du sol [Termes IGN] détection de changement [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] éclairage public [Termes IGN] image Sentinel [Termes IGN] image Terra-MODIS [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] tempête [Termes IGN] utilisation du sol Résumé : (auteur) Two months after the hurricanes Irma and Maria hit Barbuda, the construction of a new international airport led to accusations of degrading the Codrington Lagoon National Park and contravening the conventions of the Ramsar Program. Scientists have analyzed the aftermath with respect to historical legacies, disaster capitalism, manifestation of climate injustices and green gentrification. The main objective of this study was to quantify and allocate land use and land cover change (LULCC) in Barbuda before and after the 2017 Hurricane disasters. Remote sensing data and volunteered geographic information were analyzed to detect the potential changes in natural LULC so that human activities and the emergence of artificial surfaces could be detected. Human-induced LULCC occurred at different sites on the island, with decreased activities in Codrington, but increased and continued activities at Coco and Palmetto Points. With an accuracy of 97.1 %, we estimated a total increase of vegetated areas by 6.56 km2, and a simultaneous slight increase in roads and buildings with a total length of 249.67 km and a total area of 1.43 km2. The vegetation condition itself depict a steady decrease since 2017. New hotspots of human activity emerged on the island in the Codrington Lagoon National Park. Numéro de notice : A2022-233 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102732 Date de publication en ligne : 02/03/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102732 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100123 [article]

Urban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 4 (April 2022) . - pp 243 - 253 Titre : Urban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI Type de document : Article/Communication Auteurs : Clement E. Akumu, Auteur ; Sam Dennis, Auteur Année de publication : 2022 Article en page(s) : pp 243 - 253 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] carte d'occupation du sol [Termes IGN] changement d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection de changement [Termes IGN] données multitemporelles [Termes IGN] données topographiques [Termes IGN] image Landsat-OLI [Termes IGN] milieu urbain [Termes IGN] MNS lidar [Termes IGN] Tennessee (Etats-Unis) [Termes IGN] utilisation du sol Résumé : (auteur) The mapping and change detection of land cover and land use are essential for urban management. The aim of this study was to map and monitor the spatial and temporal change in urban land cover and land use in Davidson County, Tennessee in the periods of 2013, 2016, and 2020. The urban land cover and land use categories were classified and mapped using Random Forest algorithm. A combination of Landsat Operational Land Imager (OLI) satellite data with Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and derived Topographic Position Index (TPI) were used in the classification and monitoring of urban land cover and land use change. The urban land cover and land use types were mapped with average overall accuracies of about 87% in 2020, 85% in 2016 and 2013. The overall accuracy increased by around 8%, 9%, and 6% in 2020, 2016, and 2013 classifications respectively when lidarDEMand derived TPIwere added to Landsat OLIsatellite data in the classification relative to standalone Landsat OLI. Total change occurred in about 63% of Davidson County between 2016 and 2020 with significant net gains and losses among land cover and land use types. This information could support land use planning. Numéro de notice : A2022-286 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00042R3 Date de publication en ligne : 04/04/2022 En ligne : https://doi.org/10.14358/PERS.21-00042R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100320 [article]

Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata / Aditya Tafta Nugraha in Computers, Environment and Urban Systems, vol 92 (March 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 92 (March 2022) Titre : Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata Type de document : Article/Communication Auteurs : Aditya Tafta Nugraha, Auteur ; Ben J. Waterson, Auteur ; Simon P. Blainey, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] dynamique spatiale [Termes IGN] Grande-Bretagne [Termes IGN] interaction spatiale [Termes IGN] modèle orienté agent [Termes IGN] morphologie urbaine [Termes IGN] planification urbaine [Termes IGN] port [Termes IGN] transport urbain [Termes IGN] utilisation du sol Résumé : (auteur) The urban morphology is characterised by self-organisation where interactions of multiple agents produce emerging patterns on the urban form. Port-urban relationship added to the complexity of port cities' urban form. Most urban cellular automata (CA) models simulate land-use evolution through transition rules representing multi-factored local interactions. However, calibration of CA-based urban land use and transport interaction (LUTI) models often utilise manual methods due to complexity of the process. This limits insights on urban interactions to a few explored settlements and prevents applications for planning and assessment of transport policies in other contexts. This paper, therefore, addresses three main points. The paper (i) demonstrates an improved method for the calibration of CA-based LUTI models, (ii) contributes to a better understanding of the urban dynamics in port city systems by quantifying generalizable interactions from a wide range of port-urban settlements, and (iii) illustrates how the use of these interactions in a simulation model can allow long-term impact predictions of planning interventions. These were done by formulating a model in a similar structure as a neural network model to enable automatic calibration using an application of the gradient-descent algorithm. The model was then used to quantify the dynamics between land-use, geographic, and transport factors in 46 port-based and 10 non-port settlements across Great Britain, thus enabling cross-sectional analysis. Cluster analysis of the calibrated interactions in the study areas was conducted to examine the variations of these interactions. This produced two main groups. In the first group, consisting larger settlements, connections between ports and other urban activities were weaker than in the second group which consisted of smaller port-settlements. Overall, the findings of the research are consistent with existing evidence in the port-cities literature but go further in quantifying the interaction between urban agents within port-urban systems of various sizes and types. These quantified interactions will enable planners to better predict the longer-term consequences of their interventions. Numéro de notice : A2022-084 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101733 Date de publication en ligne : 25/11/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101733 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99489 [article]

Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria / Maninder Singh Dhillon in Remote sensing, vol 14 n° 3 (February-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 3 (February-1 2022) . - n° 677 Titre : Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria Type de document : Article/Communication Auteurs : Maninder Singh Dhillon, Auteur ; Thorsten Dahms, Auteur ; Carina Kübert-Flock, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 677 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Bavière (Allemagne) [Termes IGN] carte d'occupation du sol [Termes IGN] fusion de données [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] pouvoir de résolution géométrique [Termes IGN] réflectance [Termes IGN] surveillance de la végétation [Termes IGN] utilisation du sol Résumé : (auteur) The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R2 = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R2 = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R2 = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R2 = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R2 = 0.60, RMSE = 0.05) and S-MOD13Q1 (R2 = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. Numéro de notice : A2022-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14030677 Date de publication en ligne : 31/01/2022 En ligne : https://doi.org/10.3390/rs14030677 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99687 [article]

Attributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)  

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The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data / Ana-Maria Olteanu-Raimond (2022)  

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A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)  

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GIS-based study on the environmental sensitivity to pollution and susceptibility to eutrophication in Burullus Lake, Egypt / Muhammad A. El-Alfy in Marine geodesy, vol 44 n° 6 (November 2021)  

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Urban land-use analysis using proximate sensing imagery: a survey / Zhinan Qiao in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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Spatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)  

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Land degradation assessment in an African dryland context based on the Composite Land Degradation Index and mapping method / Felicia Akinyemi in Geocarto international, vol 36 n° 16 ([01/09/2021])  

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Towards culture-aware smart and sustainable cities: Integrating historical sources in spatial information infrastructures / Bénédicte Bucher in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

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A high-efficiency global model of optimization design of impervious surfaces for alleviating urban waterlogging in urban renewal / Huafei Yu in Transactions in GIS, Vol 25 n° 4 (August 2021)  

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GIS in soil survey and soil mapping / Perparim Ameti in Geodesy and cartography, vol 47 n° 2 (July 2021)  

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