Literatura científica selecionada sobre o tema "Time series of satellite images"
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Artigos de revistas sobre o assunto "Time series of satellite images"
Ghosh, Tilottama, Kimberly E. Baugh, Christopher D. Elvidge, Mikhail Zhizhin, Alexey Poyda e Feng-Chi Hsu. "Extending the DMSP Nighttime Lights Time Series beyond 2013". Remote Sensing 13, n.º 24 (9 de dezembro de 2021): 5004. http://dx.doi.org/10.3390/rs13245004.
Texto completo da fonteWang, Ruifu, Dongdong Teng, Wenqing Yu, Xi Zhang e Jinshan Zhu. "Improvement and Application of a GAN Model for Time Series Image Prediction—A Case Study of Time Series Satellite Cloud Images". Remote Sensing 14, n.º 21 (2 de novembro de 2022): 5518. http://dx.doi.org/10.3390/rs14215518.
Texto completo da fonteLiu, Yu, Wenqing Li, Li Li e Naiqun Zhang. "Extraction of Long Time-Series Vegetation Indices from Combined Multisource Satellite Imagery". Computational Intelligence and Neuroscience 2022 (30 de maio de 2022): 1–8. http://dx.doi.org/10.1155/2022/3901372.
Texto completo da fonteErena, Manuel, José A. Domínguez, Joaquín F. Atenza, Sandra García-Galiano, Juan Soria e Ángel Pérez-Ruzafa. "Bathymetry Time Series Using High Spatial Resolution Satellite Images". Water 12, n.º 2 (14 de fevereiro de 2020): 531. http://dx.doi.org/10.3390/w12020531.
Texto completo da fonteGuyet, Thomas, e Hervé Nicolas. "Long term analysis of time series of satellite images". Pattern Recognition Letters 70 (janeiro de 2016): 17–23. http://dx.doi.org/10.1016/j.patrec.2015.11.005.
Texto completo da fonteLi, Jianzhou, Jinji Ma e Xiaojiao Ye. "A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images". Remote Sensing 14, n.º 17 (29 de agosto de 2022): 4252. http://dx.doi.org/10.3390/rs14174252.
Texto completo da fonteSilva, B. L. C., F. C. Souza, K. R. Ferreira, G. R. Queiroz e L. A. Santos. "SPATIOTEMPORAL SEGMENTATION OF SATELLITE IMAGE TIME SERIES USING SELF-ORGANIZING MAP". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (17 de maio de 2022): 255–61. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-255-2022.
Texto completo da fontePETITJEAN, FRANÇOIS, FLORENT MASSEGLIA, PIERRE GANÇARSKI e GERMAIN FORESTIER. "DISCOVERING SIGNIFICANT EVOLUTION PATTERNS FROM SATELLITE IMAGE TIME SERIES". International Journal of Neural Systems 21, n.º 06 (dezembro de 2011): 475–89. http://dx.doi.org/10.1142/s0129065711003024.
Texto completo da fonteVitkovskaya, I. S. "SATELLITE DATA PROCESSING ALGORITHM IN THE PROCESS OF FORMATION OF THE TIME SERIES OF VEGETATION INDEXES". Eurasian Physical Technical Journal 18, n.º 2 (11 de junho de 2021): 90–95. http://dx.doi.org/10.31489/2021no2/90-95.
Texto completo da fonteZhou, Z. G., P. Tang e M. Zhou. "DETECTING ANOMALY REGIONS IN SATELLITE IMAGE TIME SERIES BASED ON SESAONAL AUTOCORRELATION ANALYSIS". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (6 de junho de 2016): 303–10. http://dx.doi.org/10.5194/isprsannals-iii-3-303-2016.
Texto completo da fonteTeses / dissertações sobre o assunto "Time series of satellite images"
Vázquez, Navarro Margarita R. "Life cycle of contrails from a time series of geostationary satellite images". kostenfrei, 2009. http://edoc.ub.uni-muenchen.de/10913/.
Texto completo da fonteVazquez, Navarro Margarita R. "Life cycle of contrails from a time series of geostationary satellite images". Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-109135.
Texto completo da fonteKalinicheva, Ekaterina. "Unsupervised satellite image time series analysis using deep learning techniques". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS335.
Texto completo da fonteThis thesis presents a set of unsupervised algorithms for satellite image time series (SITS) analysis. Our methods exploit machine learning algorithms and, in particular, neural networks to detect different spatio-temporal entities and their eventual changes in the time.In our thesis, we aim to identify three different types of temporal behavior: no change areas, seasonal changes (vegetation and other phenomena that have seasonal recurrence) and non-trivial changes (permanent changes such as constructions or demolishment, crop rotation, etc). Therefore, we propose two frameworks: one for detection and clustering of non-trivial changes and another for clustering of “stable” areas (seasonal changes and no change areas). The first framework is composed of two steps which are bi-temporal change detection and the interpretation of detected changes in a multi-temporal context with graph-based approaches. The bi-temporal change detection is performed for each pair of consecutive images of the SITS and is based on feature translation with autoencoders (AEs). At the next step, the changes from different timestamps that belong to the same geographic area form evolution change graphs. The graphs are then clustered using a recurrent neural networks AE model to identify different types of change behavior. For the second framework, we propose an approach for object-based SITS clustering. First, we encode SITS with a multi-view 3D convolutional AE in a single image. Second, we perform a two steps SITS segmentation using the encoded SITS and original images. Finally, the obtained segments are clustered exploiting their encoded descriptors
Wegner, Maus Victor, Gilberto Camara, Marius Appel e Edzer Pebesma. "dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R". Foundation for Open Access Statistics, 2019. http://epub.wu.ac.at/6808/1/v88i05.pdf.
Texto completo da fonteLI, YUANXUN. "SVM Object Based Classification Using Dense Satellite Imagery Time Series". Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233340.
Texto completo da fonteSanchez, Eduardo Hugo. "Learning disentangled representations of satellite image time series in a weakly supervised manner". Thesis, Toulouse 3, 2021. http://www.theses.fr/2021TOU30032.
Texto completo da fonteThis work focuses on learning data representations of satellite image time series via an unsupervised learning approach. The main goal is to enforce the data representation to capture the relevant information from the time series to perform other applications of satellite imagery. However, extracting information from satellite data involves many challenges since models need to deal with massive amounts of images provided by Earth observation satellites. Additionally, it is impossible for human operators to label such amount of images manually for each individual task (e.g. classification, segmentation, change detection, etc.). Therefore, we cannot use the supervised learning framework which achieves state-of-the-art results in many tasks.To address this problem, unsupervised learning algorithms have been proposed to learn the data structure instead of performing a specific task. Unsupervised learning is a powerful approach since no labels are required during training and the knowledge acquired can be transferred to other tasks enabling faster learning with few labels.In this work, we investigate the problem of learning disentangled representations of satellite image time series where a shared representation captures the spatial information across the images of the time series and an exclusive representation captures the temporal information which is specific to each image. We present the benefits of disentangling the spatio-temporal information of time series, e.g. the spatial information is useful to perform time-invariant image classification or segmentation while the knowledge about the temporal information is useful for change detection. To accomplish this, we analyze some of the most prevalent unsupervised learning models such as the variational autoencoder (VAE) and the generative adversarial networks (GANs) as well as the extensions of these models to perform representation disentanglement. Encouraged by the successful results achieved by generative and reconstructive models, we propose a novel framework to learn spatio-temporal representations of satellite data. We prove that the learned disentangled representations can be used to perform several computer vision tasks such as classification, segmentation, information retrieval and change detection outperforming other state-of-the-art models. Nevertheless, our experiments suggest that generative and reconstructive models present some drawbacks related to the dimensionality of the data representation, architecture complexity and the lack of disentanglement guarantees. In order to overcome these limitations, we explore a recent method based on mutual information estimation and maximization for representation learning without relying on image reconstruction or image generation. We propose a new model that extends the mutual information maximization principle to disentangle the representation domain into two parts. In addition to the experiments performed on satellite data, we show that our model is able to deal with different kinds of datasets outperforming the state-of-the-art methods based on GANs and VAEs. Furthermore, we show that our mutual information based model is less computationally demanding yet more effective. Finally, we show that our model is useful to create a data representation that only captures the class information between two images belonging to the same category. Disentangling the class or category of an image from other factors of variation provides a powerful tool to compute the similarity between pixels and perform image segmentation in a weakly-supervised manner
Wang, Zhihao. "Land Cover Classification on Satellite Image Time Series Using Deep Learning Models". The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159559249009195.
Texto completo da fonteKarasiak, Nicolas. "Cartographie des essences forestières à partir de séries temporelles d’images satellitaires à hautes résolutions : stabilité des prédictions, autocorrélation spatiale et cohérence avec la phénologie observée in situ". Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0115.
Texto completo da fonteForests have a key role on earth, whether to store carbon and so reducing the global warming or to provide a place for many species. However, the composition of the forest (the location of the tree species or their diversity) has an influence on the ecological services provided. In this context, it seems critical to map tree species that make it up the forest. Remote sensing, especially from satellite images, appears to be the most appropriate way to map large areas. Thanks to the satellite constellations such as Sentinel-2 or Landsat-8 and their free acquisition for the user, the use of time series of satellite images with high spatial, spectral and temporal resolution using automatic learning algorithms is now easy to access. While many works have studied the potential of satellite images to identify tree species, few use time series (several images per year) with high spatial resolution and taking into account the spatial autocorrelation of references, i.e. the spectral similarity of spatially close samples. However, by not taking this phenomenon into account, evaluation biases may occur and thus overestimate the quality of the learning models. It is also a question of better identifying the methodological barriers in order to understand why it can be easy or complicated for an algorithm to identify one species from another. The general objective of the thesis is to study the potential and the obstacles concerning the idenficiation of forest tree species from satellite images time series with high spatial, spectral and temporal resolution. The first objective is to study the temporal stability of predictions from a nine-year archive of the Formosat-2 satellite. More specifically, the work focuses on the implementation of a validation method that is as faithful as possible to the observed quality of the maps. The second objective focuses on the link between in situ phenological events (leaf growth at the beginning of the season, or leaf loss and coloration at the end of the season) and what can be observed by remote sensing. In addition to the ability to detect these events, it will be studied whether what allows the algorithms to identify tree species from each other is related to species-specific behaviors
Petitjean, François. "Dynamic time warping : apports théoriques pour l'analyse de données temporelles : application à la classification de séries temporelles d'images satellites". Thesis, Strasbourg, 2012. http://www.theses.fr/2012STRAD023.
Texto completo da fonteSatellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions, which aim at providing a coverage of the Earth every few days with high spatial resolution (ESA’s Sentinel program). In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling. In order to consistently handle the huge amount of information that will be produced (for instance, Sentinel-2 will cover the entire Earth’s surface every five days, with 10m to 60m spatial resolution and 13 spectral bands), new methods have to be developed. This Ph.D. thesis focuses on the “Dynamic Time Warping” similarity measure, which is able to take the most of the temporal structure of the data, in order to provide an efficient and relevant analysis of the remotely observed phenomena
Shen, Meicheng. "Statistical Estimation of Vegetation Production in the Northern High Latitude Region based on Satellite Image Time Series". The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563552594966495.
Texto completo da fonteLivros sobre o assunto "Time series of satellite images"
Nunes Kehl, Thiago, Viviane Todt, Maurício Roberto Veronez e Silvio Cesar Cazella. Real time deforestation detection using ANN and Satellite images. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15741-2.
Texto completo da fonteComputing brain activity maps from fMRI time-series images. Cambridge: Cambridge University Press, 2007.
Encontre o texto completo da fonteRemsberg, Ellis E. Time series comparisons of satellite and rocketsonde temperatures in 1978-79. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.
Encontre o texto completo da fonteJoel, Katz Eli, e United States. National Aeronautics and Space Administration., eds. A comparison of coincidental time series of the ocean surface height by satellite altimeter, mooring, and inverted echo sounder: Final technical report. Palisades, NY: Lamont-Doherty Earth Observatory of Columbia University, 1994.
Encontre o texto completo da fonteComiso, Josefino C. Polar microwave brightness temperatures from Nimbus-7 SMMR: Time series of daily and monthly maps from 1978 to 1987. Washington, D.C: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.
Encontre o texto completo da fonteJay, Zwally H., e United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Polar microwave brightness temperatures from Nimbus-7 SMMR: Time series of daily and monthly maps from 1978 to 1987. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.
Encontre o texto completo da fonteT, DeLand Matthew, Hilsenrath Ernest e United States. National Aeronautics and Space Administration., eds. Analysis of solar spectral irradiance measurements from the SBUV/2-series and the SSBUV instruments: Semi-annual report ... 1 March 1996 to 31 August 1996. [Washington, DC: National Aeronautics and Space Administration, 1996.
Encontre o texto completo da fonteT, DeLand Matthew, Hilsenrath Ernest e United States. National Aeronautics and Space Administration., eds. Analysis of solar spectral irradiance measurements from the SBUV/2-series and the SSBUV instruments: Semi-annual report, period of performance: 1 March 1997 to 31 August 1997; contract number: NASW-4864. [Washington, DC: National Aeronautics and Space Administration, 1997.
Encontre o texto completo da fonteT, DeLand Matthew, Hilsenrath Ernest e United States. National Aeronautics and Space Administration., eds. Analysis of solar spectral irradiance measurements from the SBUV/2-series and the SSBUV instruments: Semi-annual report, period of performance: 1 March 1997 to 31 August 1997; contract number: NASW-4864. [Washington, DC: National Aeronautics and Space Administration, 1997.
Encontre o texto completo da fonteT, DeLand Matthew, Hilsenrath Ernest e United States. National Aeronautics and Space Administration., eds. Analysis of solar spectral irradiance measurements from the SBUV/2-series and the SSBUV instruments: Semi-annual report, period of performance: 31 August 1996 to 28 February 1997, contract number-- NASW-4864. [Washington, DC: National Aeronautics and Space Administration, 1997.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Time series of satellite images"
Al-Obed, Meshari, Sief Uddin e Ashraf Ramadhan. "Dust Storm Satellite Images". In Atlas of Fallen Dust in Kuwait, 1–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66977-5_1.
Texto completo da fonteArya, K. V., e Suggula Jagadeesh. "Time Series Forecasting of Soil Moisture Using Satellite Images". In Communications in Computer and Information Science, 385–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07005-1_33.
Texto completo da fonteGarnot, Vivien Sainte Fare, e Loic Landrieu. "Lightweight Temporal Self-attention for Classifying Satellite Images Time Series". In Advanced Analytics and Learning on Temporal Data, 171–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65742-0_12.
Texto completo da fonteSanchez, Eduardo H., Mathieu Serrurier e Mathias Ortner. "Learning Disentangled Representations of Satellite Image Time Series". In Machine Learning and Knowledge Discovery in Databases, 306–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46133-1_19.
Texto completo da fonteGomes da Silva, Paula, Anne-Laure Beck, Jara Martinez Sanchez, Raúl Medina Santanmaria, Martin Jones e Amine Taji. "Advances on coastal erosion assessment from satellite earth observations: exploring the use of Sentinel products along with very high resolution sensors". In Proceedings e report, 412–21. Florence: Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-147-1.41.
Texto completo da fonteHonda, Rie, e Osamu Konishi. "Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB". In Principles of Data Mining and Knowledge Discovery, 204–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44794-6_17.
Texto completo da fonteChakroun, Hedia. "Drought Assessment in Tunisia by Time-Series Satellite Images: An Ecohydrologic Approach". In Springer Water, 233–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63668-5_12.
Texto completo da fonteda Silva Adeu, Rodrigo de Sales, Karine Reis Ferreira, Pedro R. Andrade e Lorena Santos. "Assessing Satellite Image Time Series Clustering Using Growing SOM". In Computational Science and Its Applications – ICCSA 2020, 270–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58814-4_19.
Texto completo da fontePetitjean, François, Pierre Gançarski, Florent Masseglia e Germain Forestier. "Analysing Satellite Image Time Series by Means of Pattern Mining". In Intelligent Data Engineering and Automated Learning – IDEAL 2010, 45–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15381-5_6.
Texto completo da fonteNguyen, Tuan, Nicolas Méger, Christophe Rigotti, Catherine Pothier e Rémi Andreoli. "SITS-P2miner: Pattern-Based Mining of Satellite Image Time Series". In Machine Learning and Knowledge Discovery in Databases, 63–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46131-1_14.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Time series of satellite images"
Keswani, Mahesh, Sanket Mahale, Rahul Kanwal e Shalu Chopra. "Land Cover Classification from Time Series Satellite Images". In 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021. http://dx.doi.org/10.1109/incet51464.2021.9456315.
Texto completo da fontePetitjean, Francois, Jordi Inglada e Pierre Gancarskv. "Clustering of satellite image time series under Time Warping". In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005050.
Texto completo da fonteRÖDER, A., T. KÜMMERLE e J. HILL. "EXTENDING TIME-SERIES OF SATELLITE IMAGES BY RADIOMETRIC INTERCALIBRATION". In Proceedings of the Second International Workshop on the Multitemp 2003. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702630_0003.
Texto completo da fonteRadoi, Anamaria, e Mihai Datcu. "Spatio-temporal characterization in satellite image time series". In 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp). IEEE, 2015. http://dx.doi.org/10.1109/multi-temp.2015.7245805.
Texto completo da fonteGiros, A. "Comparison of Partitions of Two Images for Satellite Image Time Series Segmentation". In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.670.
Texto completo da fonteNorth, Heather, D. Pairman, S. E. Belliss e J. Cuff. "Classifying agricultural land uses with time series of satellite images". In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352319.
Texto completo da fonteLafabregue, Baptiste, Anne Puissant, Jonathan Weber e Germain Forestier. "Deep Clustering Methods Study Applied to Satellite Images Time Series". In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884322.
Texto completo da fonteLodge, Felicity, Nicolas Meger, Christophe Rigotti, Catherine Pothier e Marie-Pierre Doin. "Iterative summarization of satellite image time series". In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6946703.
Texto completo da fonteGriparis, Andreea, Anamaria Rădoi, Daniela Faur e Mihai Datcu. "Visual Exploration of Satellite Image Time Series". In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023. http://dx.doi.org/10.1109/igarss52108.2023.10282850.
Texto completo da fonteTuna, Caglayan, Francois Merciol e Sebastien Lefevre. "Attribute Profiles For Satellite Image Time Series". In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898493.
Texto completo da fonteRelatórios de organizações sobre o assunto "Time series of satellite images"
Harris, Brian, Kathleen Harris, Navid Jafari, Jasmine Bekkaye, Elizabeth Murray e Safra Altman. Selection of a time series of beneficial use wetland creation sites in the Sabine National Wildlife Refuge for use in restoration trajectory development. Engineer Research and Development Center (U.S.), setembro de 2023. http://dx.doi.org/10.21079/11681/47579.
Texto completo da fonteDeschamps, Henschel e Robert. PR-420-123712-R01 Lateral Ground Movement Detection Capabilities Derived from Synthetic Aperture Radar. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), novembro de 2014. http://dx.doi.org/10.55274/r0010831.
Texto completo da fonteSwanson, David. Stability of ice wedges in Alaska's Arctic National Parks, 1951-2019. National Park Service, maio de 2022. http://dx.doi.org/10.36967/nrr-2293324.
Texto completo da fonteSwanson, David, e Celia Hampton-Miller. Drained lakes in Bering Land Bridge National Preserve: Vegetation succession and impacts on loon habitat. National Park Service, janeiro de 2023. http://dx.doi.org/10.36967/2296593.
Texto completo da fonteThompson, Anna, Michael Loso, Sydney Mooneyham, Brandon Tober, Christopher Larsen e John Holt. Surficial geology and proglacial lake change at S?t? Tlein (Malaspina Glacier), Wrangell-St. Elias National Park and Preserve, Alaska. National Park Service, 2024. http://dx.doi.org/10.36967/2301689.
Texto completo da fonteJääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, setembro de 2023. http://dx.doi.org/10.35614/isbn.9789523361782.
Texto completo da fonteMelrose, Rachel, Jeff Kingwell, Leo Lymburner e Rohan Coghlan. Murray-Darling Basin vegetation monitoring project : using time series Landsat Satellite data for the assessment of vegetation control. Geoscience Australia, 2013. http://dx.doi.org/10.11636/record.2013.037.
Texto completo da fonteSalazar, Lina, Ana Claudia Palacios, Michael Selvaraj e Frank Montenegro. Using Satellite Images to Measure Crop Productivity: Long-Term Impact Assessment of a Randomized Technology Adoption Program in the Dominican Republic. Inter-American Development Bank, setembro de 2021. http://dx.doi.org/10.18235/0003604.
Texto completo da fonteRosinska, Olena. Образи батьків у молодіжних серіалах: наратив протистояння. Ivan Franko National University of Lviv, março de 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11748.
Texto completo da fonteTemple, Dorota S., Jason S. Polly, Meghan Hegarty-Craver, James I. Rineer, Daniel Lapidus, Kemen Austin, Katherine P. Woodward e Robert H. Beach III. The View From Above: Satellites Inform Decision-Making for Food Security. RTI Press, agosto de 2019. http://dx.doi.org/10.3768/rtipress.2019.rb.0021.1908.
Texto completo da fonte