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Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)
[article]
Titre : Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression Type de document : Article/Communication Auteurs : Haoyu Wang, Auteur ; Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113088 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] apprentissage profond
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie thématique
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] croissance urbaine
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle de régression
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) Global urbanization changes land cover patterns and affects the living environment of humans. However, urbanization and its evolution process, i.e., conversions among diverse land covers, are hard to measure, as existing land cover maps usually have low temporal resolutions; conversely, long-term and temporally dense land cover maps, such as vegetation-impervious-soil decomposition maps base on MODIS, ignore the important land cover of cropland in urban evolution process (UEP). To resolve the issue, this study suggests a novel model named time-extended non-crop vegetation-impervious-cropland (Time V-I-C) to represent and quantify different stages of UEP; then, a normalized multi-objective T-ConvLSTM (NMT) method is proposed to unmix cropland, non-crop vegetation, and impervious based on the intra-annual remotely-sensed time series, and obtain their fractions in each pixel for generating UEP maps. Consequently, UEP maps from 2001 to 2018 are generated for two Chinese urban agglomerations, i.e., Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. The mapping results have high accuracies with a small standard error of regression (SER) of 13.1%, small root mean square error (RMSE) of 12.6%, and small mean absolute error (MAE) of 8.4%, and the maps reveal the different UEP in the two urban agglomerations. Therefore, this study provides a new idea for expressing UEP and contributes to a wide range of urbanization studies and sustainable city development. Numéro de notice : A2022-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.rse.2022.113088 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113088 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101049
in Remote sensing of environment > vol 278 (September 2022) . - n° 113088[article]Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery / Shengyuan Zou in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)
[article]
Titre : Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery Type de document : Article/Communication Auteurs : Shengyuan Zou, Auteur ; Le Wang, Auteur Année de publication : 2022 Article en page(s) : n° 103018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection d'objet
[Termes IGN] image à très haute résolution
[Termes IGN] image Streetview
[Termes IGN] logement
[Termes IGN] New York (Etats-Unis ; état)
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] zone urbaineRésumé : (auteur) Abandoned houses (AH) present an utmost challenge confronting the urban environment in contemporary U.S. shrinking cities. Data accessibility is a major hurdle that prevents the acquisition of large-scale AH information at the individual property level. To this end, the latest revolution of open-access remote sensing platforms has witnessed a plethora of multi-source, multi-perspective fine-spatial-resolution data for urban environments, among which very-high-resolution (VHR) top-down view remote sensing images and horizontal-perspective Google Street View (GSV) images are prominent exemplifiers. In this study, we aim to map individual-level abandoned houses across cities by developing a method that can effectively leverage VHR remote sensing and GSV images. The proposed method is composed of four steps. First, we explored the feasibility of the three most relevant and complementary remote sensing data for individual-level AH detection, i.e., daytime VHR images, nighttime light VHR images, and GSV images. Second, we extracted discriminative features that are indicative of housing abandonment conditions from the three disparate data sources. Third, we applied decision-level fusion with Dempster-Shafer Theory (DST) to better leverage the prior knowledge about data effectiveness. In the last step, a geographical random forests (GRF) model was first implemented to improve the predictions of where houses were occluded on GSV images. We mapped individual AH in two typical U.S. shrinking cities, Buffalo, NY, and Cleveland, OH, which allowed us to further explore the individual-property-level spatial characteristics of AH. Results revealed that the proposed DST fusion and GRF prediction consistently achieved promising performance across the two cities. Given the merits of incorporating open-access and multi-perspective data, our proposed method has the potential to be generalized to understanding regional and national-scale urban environments tackling housing abandonment challenges. Numéro de notice : A2022-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103018 Date de publication en ligne : 18/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101894
in International journal of applied Earth observation and geoinformation > vol 113 (September 2022) . - n° 103018[article]Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices / Najmeh Mozaffaree Pour in Environmental Monitoring and Assessment, vol 194 n° 9 (September 2022)
[article]
Titre : Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices Type de document : Article/Communication Auteurs : Najmeh Mozaffaree Pour, Auteur ; Oleksandr Karasov, Auteur ; Iuliia Burdun, Auteur ; Tõnu Oja, Auteur Année de publication : 2022 Article en page(s) : n° 584 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] chaîne de Markov
[Termes IGN] croissance urbaine
[Termes IGN] Estonie
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat-8
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Over the recent two decades, land use/land cover (LULC) drastically changed in Estonia. Even though the population decreased by 11%, noticeable agricultural and forest land areas were turned into urban land. In this work, we analyzed those LULC changes by mapping the spatial characteristics of LULC and urban expansion in the years 2000–2019 in Estonia. Moreover, using the revealed spatiotemporal transitions of LULC, we simulated LULC and urban expansion for 2030. Landsat 5 and 8 data were used to estimate 147 spectral-textural indices in the Google Earth Engine cloud computing platform. After that, 19 selected indices were used to model LULC changes by applying the hybrid artificial neural network, cellular automata, and Markov chain analysis (ANN-CA-MCA). While determining spectral-textural indices is quite common for LULC classifications, utilization of these continues indices in LULC change detection and examining these indices at the landscape scale is still in infancy. This country-wide modeling approach provided the first comprehensive projection of future LULC utilizing spectral-textural indices. In this work, we utilized the hybrid ANN-CA-MCA model for predicting LULC in Estonia for 2030; we revealed that the predicted changes in LULC from 2019 to 2030 were similar to the observed changes from 2011 to 2019. The predicted change in the area of artificial surfaces was an increased rate of 1.33% to reach 787.04 km2 in total by 2030. Between 2019 and 2030, the other significant changes were the decrease of 34.57 km2 of forest lands and the increase of agricultural lands by 14.90 km2 and wetlands by 9.31 km2. These findings can develop a proper course of action for long-term spatial planning in Estonia. Therefore, a key policy priority should be to plan for the stable care of forest lands to maintain biodiversity. Numéro de notice : A2022-458 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article DOI : 10.1007/s10661-022-10266-7 Date de publication en ligne : 13/07/2022 En ligne : http://dx.doi.org/10.1007/s10661-022-10266-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101258
in Environmental Monitoring and Assessment > vol 194 n° 9 (September 2022) . - n° 584[article]Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping / Sandro Martinis in Remote sensing of environment, vol 278 (September 2022)
[article]
Titre : Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping Type de document : Article/Communication Auteurs : Sandro Martinis, Auteur ; Sandro Groth, Auteur ; Marc Wieland, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Allemagne
[Termes IGN] Australie
[Termes IGN] carte thématique
[Termes IGN] fusion d'images
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Inde
[Termes IGN] inondation
[Termes IGN] Mozambique
[Termes IGN] prévention des risques
[Termes IGN] série temporelle
[Termes IGN] Soudan
[Termes IGN] surveillance hydrologique
[Termes IGN] variation saisonnière
[Termes IGN] zone à risqueRésumé : (auteur) Satellite-based flood mapping has become an important part of disaster response. In order to accurately distinguish flood inundation from normally present conditions, up-to-date, high-resolution information on the seasonal water cover is crucial. This information is usually neglected in disaster management, which may result in a non-reliable representation of the flood extent, mainly in regions with highly dynamic hydrological conditions. In this study, we present a fully automated method to generate a global reference water product specifically designed for the use in global flood mapping applications based on high resolution Earth Observation data. The proposed methodology combines existing processing pipelines for flood detection based on Sentinel-1/2 data and aggregates permanent as well as seasonal water masks over an adjustable reference time period. The water masks are primarily based on the analysis of Sentinel-2 data and are complemented by Sentinel-1-based information in optical data scarce regions. First results are demonstrated in five selected study areas (Australia, Germany, India, Mozambique, and Sudan), distributed across different climate zones and are systematically compared with external products. Further, the proposed product is exemplary applied to three real flood events in order to evaluate the impact of the used reference water mask on the derived flood extent. Results show, that it is possible to generate a consistent reference water product at 10–20 m spatial resolution, that is more suitable for the use in rapid disaster response than previous masks. The proposed multi-sensor approach is capable of producing reasonable results, even if only few or no information from optical data is available. Further it becomes clear, that the consideration of seasonality of water bodies, especially in regions with highly dynamic hydrological and climatic conditions, reduces potential over-estimation of the inundation extent and gives a more reliable picture on flood-affected areas. Numéro de notice : A2022-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113077 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113077 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100801
in Remote sensing of environment > vol 278 (September 2022) . - n° 113077[article]Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])
[article]
Titre : Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data Type de document : Article/Communication Auteurs : Tohid Nouri, Auteur Année de publication : 2022 Article en page(s) : pp 5017 - 5040 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] altération géologique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] appariement d'images
[Termes IGN] diffraction
[Termes IGN] image multibande
[Termes IGN] Iran
[Termes IGN] logique floue
[Termes IGN] mine d'or
[Termes IGN] MNS ASTER
[Termes IGN] pixel
[Termes IGN] prospection minérale
[Termes IGN] sédiment
[Termes IGN] spectrométrieRésumé : (auteur) The northeast area of Ardabil, a city located in northwestern Iran, is one of the potential gold mineralization areas. In this study, ASTER data were used to identify the alteration events in this region. For this purpose, a novel approach was used in which the fuzzy logic was implemented to extract the co-occurrence map of the endmembers. This method revealed alterations more accurately than SID. Stream sediment samples were employed to validate the obtained results. Since these samples are alluvial, their catchment basins were determined and overlaid with the alteration maps. To the best of the authors’ knowledge, this validation approach has not been used in previous studies. The extracted alteration zones were in high conformity to the stream sediment samples. Next, X-ray diffraction (XRD) analysis and field spectrometry were used for delineation of the mineralogical phases present in the anomalous areas. Finally, the potential gold mineralization zones were identified. Numéro de notice : A2022-701 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1903575 Date de publication en ligne : 07/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1903575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101560
in Geocarto international > Vol 37 n° 17 [20/08/2022] . - pp 5017 - 5040[article]Evapotranspiration mapping of cotton fields in Brazil: comparison between SEBAL and FAO-56 method / Juan Vicente Liendro Moncada in Geocarto international, Vol 37 n° 17 ([20/08/2022])PermalinkComparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs / Douglas Stefanello Facco in Geocarto international, vol 37 n° 16 ([15/08/2022])Permalink3D building reconstruction from single street view images using deep learning / Hui En Pang in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)Permalink3D semantic scene completion: A survey / Luis Roldão in International journal of computer vision, vol 130 n° 8 (August 2022)PermalinkAn automatic approach for tree species detection and profile estimation of urban street trees using deep learning and Google street view images / Kwanghun Choi in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)PermalinkAn investigation into heat storage by adopting local climate zones and nocturnal-diurnal urban heat island differences in the Tokyo Prefecture / Christopher O'Malley in Sustainable Cities and Society, vol 83 (August 2022)PermalinkAssessing structural complexity of individual scots pine trees by comparing terrestrial laser scanning and photogrammetric point clouds / Noora Tienaho in Forests, Vol 13 n° 8 (August 2022)PermalinkDeep learning feature representation for image matching under large viewpoint and viewing direction change / Lin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)PermalinkDetection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM / Jiehua Cai in Engineering Geology, vol 305 (August 2022)PermalinkEffective CBIR based on hybrid image features and multilevel approach / D. Latha in Multimedia tools and applications, vol 81 n° 20 (August 2022)Permalink