Centre de documentation
scientifique

Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach / Bowen Niu in Geocarto international, vol 38 n° 1 ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° 1 [01/01/2023] . - n° 2164361 Titre : Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach Type de document : Article/Communication Auteurs : Bowen Niu, Auteur ; Quanlong Feng, Auteur ; Jianyu Yang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2164361 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] déchet [Termes IGN] fusion de données [Termes IGN] image à très haute résolution [Termes IGN] Inde [Termes IGN] Mexique [Termes IGN] urbanisation Résumé : (auteur) The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people’s wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. Numéro de notice : A2023-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2164361 Date de publication en ligne : 04/01/2023 En ligne : https://doi.org/10.1080/10106049.2022.2164361 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102407 [article]

Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, vol 36 n° 15 ([15/08/2021])  

Public [article]  inGeocarto international > vol 36 n° 15 [15/08/2021] . - pp 1768 - 1784 Titre : Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico Type de document : Article/Communication Auteurs : Yao Gao, Auteur ; Alexander Quevedo, Auteur ; Zoltan Szantoi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1768 - 1784 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] détection de changement [Termes IGN] fonction harmonique [Termes IGN] image Terra-MODIS [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Mexique [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] série temporelle [Termes IGN] surveillance forestière [Termes IGN] variation saisonnière Résumé : (auteur) MODIS-based NDVI time series (2000–2016) was applied to monitor sub-annual forest disturbance in the Mexican state of Michoacán, with an algorithm that decomposes the time-series data into a harmonic function and a trend. To detect change, a moving sum of residuals between the observed and predicted NDVI values was compared with that from the reference period. Magnitude of change was computed by subtracting the predicted NDVI from the observed one. By comparing the detected changes with reference data through visual interpretation, a threshold of |0.05| was established as the magnitude of change for forest disturbance detection. The method detected more forest gain than loss for 2013–2016, a result which is supported by recent findings from the national forest inventory. Forest loss decreases yearly for 2013–2016, and forest gain peaks at 2014 and 2015. We verified the findings with data from the global forest cover change project. Numéro de notice : A2021-580 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1661032 Date de publication en ligne : 09/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1661032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98185 [article]

Cluster-based empirical tropospheric corrections applied to InSAR time series analysis / Kyle Dennis Murray in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2204 - 2212 Titre : Cluster-based empirical tropospheric corrections applied to InSAR time series analysis Type de document : Article/Communication Auteurs : Kyle Dennis Murray, Auteur ; Rowena B. Lohman, Auteur ; David P. S. Bekaert, Auteur Année de publication : 2021 Article en page(s) : pp 2204 - 2212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] bruit atmosphérique [Termes IGN] classification par nuées dynamiques [Termes IGN] déformation de la croute terrestre [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] Mexique [Termes IGN] retard troposphérique [Termes IGN] série temporelle Résumé : (Auteur) Interferometric synthetic aperture radar (InSAR) allows for mapping of crustal deformation on land with high spatial resolution and precision in areas with high signal-to-noise ratios. Efforts to obtain precise displacement time series globally, however, are severely limited by radar path delays within the troposphere. The tropospheric delay is integrated along the full path length between the ground and the satellite, resulting in correlations between the interferometric phase and elevation that can vary dramatically in both space and time. We evaluate the performance of spatially variable, empirical removal of phase-elevation dependence within SAR interferograms through the use of the K -means clustering algorithm. We apply this method to both synthetic test data, as well as to C-band Sentinel-1a/b time series acquired over a large area in south-central Mexico along the Pacific coast and inland—an area with a large elevation gradient that is of particular interest to researchers studying tectonic- and anthropogenic-related deformation. We show that the clustering algorithm is able to identify cases where tropospheric properties vary across topographic divides, reducing total root mean square (rms) by an average of 50%, as opposed to a spatially constant phase-elevation correction, which has insignificant error reduction. Our approach also reduces tropospheric noise while preserving test signals in synthetic examples. Finally, we show the average standard deviation of the residuals from the best-fit linear rate decreases from approximately 3 to 1.5 cm, which corresponds to a change in the error on the best-fit linear rate from 0.94 to 0.63 cm/yr. Numéro de notice : A2021-215 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3003271 Date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3003271 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97204 [article]

Artificial intelligence methods applied to urban remote sensing and GIS / Chang-Wook Lee (2021)  

Public Titre : Artificial intelligence methods applied to urban remote sensing and GIS Type de document : Monographie Auteurs : Chang-Wook Lee, Éditeur scientifique ; Hyangsun Han, Éditeur scientifique ; Hoonyol Lee, Éditeur scientifique ; Yu-Chul Park, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 166 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-3-0365-1603-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] carte thématique [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Corée du sud [Termes IGN] effondrement de terrain [Termes IGN] espace vert [Termes IGN] image à très haute résolution [Termes IGN] image radar moirée [Termes IGN] indice de végétation [Termes IGN] intelligence artificielle [Termes IGN] Jakarta (Indonésie) [Termes IGN] méthode de Monte-Carlo [Termes IGN] Mexique [Termes IGN] milieu urbain [Termes IGN] pollution des eaux [Termes IGN] réseau local sans fil [Termes IGN] segmentation sémantique [Termes IGN] séisme [Termes IGN] système d'information géographique Résumé : (éditeur) This book is based on Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS" from early 2020 to 2021. This book includes seven papers related to the application of artificial intelligence, machine learning and deep learning algorithms using remote sensing and GIS techniques in urban areas. Note de contenu : 1- Improvement of earthquake risk awareness and seismic literacy of Korean citizens through earthquake vulnerability map from the 2017 Pohang earthquake, South Korea 2- Land subsidence susceptibility mapping in Jakarta using functional and meta-ensemble machine learning algorithm based on time-series InSAR data 3- Integration of InSAR time-series data and GIS to assess Llnd subsidence along subway lines in the Seoul metropolitan area, South Korea 4- Mapping urban green spaces at the metropolitan level using very high resolution satellite imagery and deep learning techniques for semantic segmentation 5- Susceptibility analysis of the Mt. Umyeon landslide area using a physical slope model and probabilistic method 6- Intelligent WSN system for water quality analysis using machine learning algorithms: A case study (Tahuando River from Ecuador) 7- Groundwater potential mapping using remote sensing and GIS-based machine learning techniques Numéro de notice : 28667 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-1603-5 En ligne : https://doi.org/10.3390/books978-3-0365-1603-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99870

Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)  

Public [article]  inForests > vol 11 n°11 (November 2020) . - n° 1234 Titre : Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery Type de document : Article/Communication Auteurs : Astrid Helena Huechacona-Ruiz, Auteur ; Juan Manuel Dupuy, Auteur ; Naomi B. Schwartz, Auteur Année de publication : 2020 Article en page(s) : n° 1234 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse des mélanges spectraux [Termes IGN] arbre caducifolié [Termes IGN] carte de la végétation [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] distribution spatiale [Termes IGN] forêt tropicale [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] réflectance [Termes IGN] texture d'image [Termes IGN] Yucatan Résumé : (auteur) In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R2 = 0.56 to R2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery. Numéro de notice : A2020-756 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11111234 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/f11111234 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96468 [article]

Topographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])  

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Monitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing / Jonathan B. Thayn in Marine geodesy, Vol 43 n° 5 (September 2020)  

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Cyclists' exposure to air pollution and noise in Mexico City : contribution of real-time traffic density indicators integrated into GIS / Philippe Apparicio in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)  

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Landsats 1–5 multispectral scanner system sensors radiometric calibration update / Cibele Teixeira-Pinto in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

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Using LiDAR to develop high-resolution reference models of forest structure and spatial pattern / Haley L. Wiggins in Forest ecology and management, vol 434 (28 February 2019)  

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Synergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico / Fabiola D. Yepez-Rincon in Geocarto international, vol 34 n° 2 ([01/02/2019])  

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Evolutionary approach for detection of buried remains using hyperspectral images / Leon Dozal in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 7 (juillet 2018)  

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InSAR to support sustainable urbanization over compacting aquifers: The case of Toluca Valley, Mexico / Pascal Castellazzi in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)  

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Tectonic and anthropogenic deformation at the Cerro Prieto geothermal step-over revealed by sentinel-1A InSAR / Xiaohua Xu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

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GIS and integrated water resource management in Mexico / R. Spooner in GEO: Geoconnexion international, vol 12 n° 2 (february 2013)

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