Gotowe bibliografie tematyczne / Google Engine Earth

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Google Engine Earth”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Google Engine Earth"

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Mutanga, Onisimo, and Lalit Kumar. "Google Earth Engine Applications." Remote Sensing 11, no. 5 (2019): 591. http://dx.doi.org/10.3390/rs11050591.

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VILLAVICENCIO, L. M. M., D. MENDES, L. M. B. ANDRADE, and F. F. MONTEIRO. "Google Earth Engine: Mapping Changes in the Vilcanota-Peru Mountain Range." Anuário do Instituto de Geociências - UFRJ 41, no. 3 (2018): 427–33. http://dx.doi.org/10.11137/2018_3_427_433.

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Рахимов, Г., М. Б. Игемберлина, А. Ж. Жанибек та Ж. М. Батыршаева. "МОНИТОРИНГ ИЗМЕНЕНИЯ ПЛОЩАДИ ОЗЕРА БАЛХАШ С ПОМОЩЬЮ ГЕОПРОСТРАНСТВЕННОЙ ПЛАТФОРМЫ GOOGLE EARTH ENGINE". Горный журнал Казахстана, № 4(240) (2 травня 2025): 25–30. https://doi.org/10.48498/minmag.2025.240.4.008.

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В данной статье представлены результаты исследования изменения площади озера Балхаш за период с 2001 по 2023 годы с использованием спутниковых данных MODIS и платформы Google Earth Engine. Целью исследования являлось изучение динамики площади озера. Для достижения поставленной цели была разработана методология, включающая обработку спутниковых данных MODIS, расчет индекса NDWI для выделения водной поверхности, классификацию изображений, расчет площади озера и создание временного ряда. В результате исследования были получены данные о динамике площади озера Балхаш за 20-летний период. Полученные
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Zhao, Qiang, Le Yu, Xuecao Li, Dailiang Peng, Yongguang Zhang, and Peng Gong. "Progress and Trends in the Application of Google Earth and Google Earth Engine." Remote Sensing 13, no. 18 (2021): 3778. http://dx.doi.org/10.3390/rs13183778.

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Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we re
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Staletović, Aleksandar. "IDENTIFIKACIJA POLJOPRIVREDNIH PARCELA UPOTREBOM GOOGLE EARTH ENGINE." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 35, no. 02 (2020): 364–67. http://dx.doi.org/10.24867/06kg03staletovic.

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U ovom radu ispitana je upotrebljivost Google Earth Engine platforme u daljinskoj detekciji kroz analizu satelitskih snimaka. Primenom satelitskih snima­ka sa Sentinel-1 i Sentinel-2 platforme izvršena je iden­tifikacija poljoprivrednih parcela.
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Yancho, J. Maxwell M., Trevor Gareth Jones, Samir R. Gandhi, Colin Ferster, Alice Lin, and Leah Glass. "The Google Earth Engine Mangrove Mapping Methodology (GEEMMM)." Remote Sensing 12, no. 22 (2020): 3758. http://dx.doi.org/10.3390/rs12223758.

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Mangroves are found globally throughout tropical and sub-tropical inter-tidal coastlines. These highly biodiverse and carbon-dense ecosystems have multi-faceted value, providing critical goods and services to millions living in coastal communities and making significant contributions to global climate change mitigation through carbon sequestration and storage. Despite their many values, mangrove loss continues to be widespread in many regions due primarily to anthropogenic activities. Accessible, intuitive tools that enable coastal managers to map and monitor mangrove cover are needed to stem
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Ghaffarian, Saman, Ali Rezaie Farhadabad, and Norman Kerle. "Post-Disaster Recovery Monitoring with Google Earth Engine." Applied Sciences 10, no. 13 (2020): 4574. http://dx.doi.org/10.3390/app10134574.

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Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of r
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Fedotova, Elena, and Anna Gosteva. "Using of Google Earth Engine in monitoring systems." E3S Web of Conferences 333 (2021): 01013. http://dx.doi.org/10.1051/e3sconf/202133301013.

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Google Earth Engine (GEE) cloud service is a powerful tool for environmental research. An example of using GEE to solve a typical research problem is shown. The following data extraction and analysis operations were used: filtering data from sets, constructing functions, building graphs, selecting data using vector and raster masks. GEE interface in the form of JavaScript code was used. Correlation between surface runoff and precipitation and snow depth in areas with forest dieback was analysed for Krasnoyarsk region in Russia (r = 0.30 for precipitation and r = 0.57 for snow depth).
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Brooke, Sam A. S., Mitch D’Arcy, Philippa J. Mason, and Alexander C. Whittaker. "Rapid multispectral data sampling using Google Earth Engine." Computers & Geosciences 135 (February 2020): 104366. http://dx.doi.org/10.1016/j.cageo.2019.104366.

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Mateo-García, Gonzalo, Luis Gómez-Chova, Julia Amorós-López, Jordi Muñoz-Marí, and Gustau Camps-Valls. "Multitemporal Cloud Masking in the Google Earth Engine." Remote Sensing 10, no. 7 (2018): 1079. http://dx.doi.org/10.3390/rs10071079.

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Rozprawy doktorskie na temat "Google Engine Earth"

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Vahidi, Mayamey Farzad. "Improving the water-extent monitoring of Swedish wetlands with open-source satellite data and Google Earth Engine." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Wetlands are essential for controlling the global climate, sustaining the global hydrological cycle, conserving ecological variety, and ensuring human wellbeing. As wetlands are one of the most endangered environments due to land conversion, infrastructure development, and overexploitation, they require constant monitoring. In Sweden, there are 68 sites recognized as wetlands with international importance. The inundated area and the connectivity of the wetlands are affected by climate change. For this reason, we need to better delineate water bodies in these valuable environments. Advances in
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FATTORE, CARMEN. "Verso un approccio multi-scala, multi-temporale e multi-sensore per il monitoraggio del Capitale Naturale nell’era dei Big Earth Data. Metodi, tecniche e sviluppi futuri per la valutazione del danno post-evento." Doctoral thesis, Università degli studi della Basilicata, 2022. http://hdl.handle.net/11563/158586.

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La crescente disponibilità di nuove tecnologie, il miglioramento e la miniaturizzazione di quelle attuali, la maggiore accessibilità ai dati con particolare riferimento all’ accesso gratuito a numerosi (NASA, ESA) archivi di immagini telerilevate, la crescita delle potenzialità offerte dalle tecnologie di Earth Observation (EO), possono offrire una grande quantità di informazioni (EO) destinate alla comunità scientifica e non. Tuttavia, per estrarre utili informazioni dalla immensa quantità di dati oggi disponibili occorre definire, progettare ed implementare nuovi approcci di analisi ed inte
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Ільченко, Катерина Володимирівна. "Космічний моніторинг забудови території міста Києва". Thesis, Національний авіаційний університет, 2020. http://er.nau.edu.ua/handle/NAU/41605.

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Об’єкт дослідження: територія міста Києва. Предметом є її зміна за рахунок забудови, в тому числі неспланованої та незаконної. Мета дипломної роботи: визначення проблем та встановлення масштабів забудови території міста Києва за допомогою методів космічного моніторингу. Методи дослідження: обробка літературних джерел, застосування методів класифікації для візуалізації масштабів забудови території міста Києва, використання платформи Google Earth Engine для виконання класифікації та порівняння її результатів, аналіз отриманих даних, виконання певних розрахунків на основі отриманих даних. Рез
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Ganem, Khalil Ali. "Classificação da cobertura do solo na Caatinga a partir de imagens do Landsat-8 e da ferramenta Google Earth Engine : uma comparação entre dados com e sem correção atmosférica." reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/23501.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Geociências, Programa de Pós-Graduação em Geociências Aplicadas, 2017.<br>Submitted by Raquel Almeida (raquel.df13@gmail.com) on 2017-05-12T19:49:30Z No. of bitstreams: 1 2017_KhalilAliGanem.pdf: 7800118 bytes, checksum: 7922fe6795a865561d79dc0353d14fe4 (MD5)<br>Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2017-05-16T17:02:32Z (GMT) No. of bitstreams: 1 2017_KhalilAliGanem.pdf: 7800118 bytes, checksum: 7922fe6795a865561d79dc0353d14fe4 (MD5)<br>Made available in DSpace on 2017-05-16T17:02:32Z (GMT). No.
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Schirinzi, Mattia. "Action cam per la raccolta di verità a terra propedeutica all'identificazione di colture agricole attraverso immagini ottiche del satellite Sentinel-2." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22341/.

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Il telerilevamento ha reso possibile l’applicazione di tecnologie in grado di ridurre gli impatti sull’ambiente. Tra queste, la missione Sentinel-2 di ESA è in grado di fornire informazioni per contrastare il cambiamento climatico. In questo elaborato, l’utilizzo di una action cam per il monitoraggio di appezzamenti agricoli correlata all’utilizzo di bande Sentinel-2 e degli indici NDVI e NDWI hanno permesso di valutare l’andamento fenologico in una serie di appezzamenti della Provincia di Ravenna. Il primo risultato è stato quello di ottenere ground truth (GTs) a basso costo che potessero int
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Martino, Filippo. "Analisi multitemporale del dissesto da immagini satellitari Sentinel (provincia di Forlì-Cesena)." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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La tesi ha l'obbiettivo di testare le capacità del software Google Earth Engine nel telerilevamento satellitare del dissesto e più precisamente nell'individuazione di frane. L'analisi svolta considera frane recenti note avvenute nella provincia di Forlì-Cesena e cerca di riconoscerne l'attivazione attraverso il confronto di immagini satellitari Sentinel nel tempo. Lo scopo è definire le caratteristiche (tipologia, dimensioni) dei fenomeni effettivamente riconoscibili con questa tecnologia, nella prospettiva che essa possa trovare applicazione in ambiti quali lo studio dell'occorrenza delle f
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Sanchez, Luna Maria M. "MAPPING SMALL SCALE FARMING IN HETEROGENEOUS LANDSCAPES: A CASE STUDY OF SMALLHOLDER SHADE COFFEE AND PLASTIC AGRICULTURE FARMERS IN THE CHIAPAS HIGHLANDS." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1564228778095931.

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Stromann, Oliver. "Feature Extraction and FeatureSelection for Object-based LandCover Classification : Optimisation of Support Vector Machines in aCloud Computing Environment." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-238727.

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Mapping the Earth’s surface and its rapid changes with remotely sensed data is a crucial tool to un-derstand the impact of an increasingly urban world population on the environment. However, the impressive amount of freely available Copernicus data is only marginally exploited in common clas-sifications. One of the reasons is that measuring the properties of training samples, the so-called ‘fea-tures’, is costly and tedious. Furthermore, handling large feature sets is not easy in most image clas-sification software. This often leads to the manual choice of few, allegedly promising features. In
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Althén, Bergman Felix, and Evelina Östblom. "GIS-based crisis communication : A platform for authorities to communicate with the public during wildfire." Thesis, KTH, Geoinformatik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253072.

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Today, people are used to having technology as a constant aid. This also sets expectations that information should always be available. This, together with ongoing climate change that has led to more natural disasters, has laid the foundation for the need to change the methodology for how geographical data is collected, compiled and visualized when used for crisis communication. This study explores how authorities, at present, communicate with the public during a crisis and how this can be done in an easier and more comprehensible way, with the help of Geographical Information Systems (GIS). T
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Guo, Qi. "Bangladesh Shoreline Changes During the Last Four Decades Using Satellite Remote Sensing Data." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503258115717912.

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Książki na temat "Google Engine Earth"

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Cardille, Jeffrey A., Morgan A. Crowley, David Saah, and Nicholas E. Clinton, eds. Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-26588-4.

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Google Earth Engine Applications. MDPI, 2019. http://dx.doi.org/10.3390/books978-3-03897-885-5.

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Introductory course to Google Earth Engine. FAO, 2022. http://dx.doi.org/10.4060/cb9049en.

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Cardille, Jeffrey A., Morgan A. Crowley, David Saah, and Nicholas E. Clinton. Cloud-Based Remote Sensing with Google Earth Engine: Fundamentals and Applications. Springer International Publishing AG, 2023.

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Cardille, Jeffrey A., Morgan A. Crowley, David Saah, and Nicholas E. Clinton. Cloud-Based Remote Sensing with Google Earth Engine: Fundamentals and Applications. Springer International Publishing AG, 2023.

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Farhan, Muhammad, Noor Zaman Jhanjhi, Muhammad Umer, Rana M. Amir Latif, Mamoona Humayun, and Syed Jawad Hussain. A Smart Agriculture Land Suitability Detection Model Using Machine Learning with Google Earth Engine. Eliva Press, 2020.

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Kurbucz, Marcell T., and Bo Pieter Johannes Andrée. Building and Managing Local Databases from Google Earth Engine with the geeLite R Package. Washington, DC: World Bank, 2025. https://doi.org/10.1596/1813-9450-11115.

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Horton, Zach. Ant and Empire: Myrmetic Writing, Simulation and the Problem of Reciprocal Becomings. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474422734.003.0006.

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This chapter considers the ant as a limit case of becoming-animal in order to problematize a troubling reciprocity of becoming. For the ant, already multiple, already molecularized, adapts to every niche on earth, constitutes its territory through a limitless processural colonization of the other that involves endless becomings, endless deterritorializations and reconstitutions as a species body at multiple scales. In short, the ant is the perfect Deleuzean animal, and yet as H. G. Wells captures so astutely in his story, “The Empire of the Ants,” it is also the most imperial and hierarchized.
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Części książek na temat "Google Engine Earth"

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Deines, Jillian M., Stefania Di Tommaso, Nicholas Clinton, and Noel Gorelick. "Scaling up in Earth Engine." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_29.

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AbstractCommonly, when Earth Engine users move from tutorials to developing their own processing scripts, they encounter the dreaded error messages, “computation timed out” or “user memory limit exceeded”. Computational resources are never unlimited, and the team at Earth Engine has designed a robust system with built-in checks to ensure that server capacity is available to everyone. This chapter will introduce general tips for creating efficient Earth Engine workflows that accomplish users’ ambitious research objectives within the constraints of the Earth Engine ecosystem. We use two example
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Aybar, Cesar, David Montero, Antony Barja, Fernando Herrera, Andrea Gonzales, and Wendy Espinoza. "Combining R and Earth Engine." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_31.

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AbstractThe purpose of this chapter is to introduce rgee, a non-official API for Earth Engine. You will explore the main features available in rgee and how to set up an environment that integrates rgee with third-party R and Python packages. After this chapter, you will be able to combine R, Python, and JavaScript in the same workflow.
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Gandhi, Ujaval. "JavaScript and the Earth Engine API." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_1.

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Nekorchuk, Dawn. "Health Applications." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_37.

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AbstractThe purpose of this chapter is to demonstrate how Google Earth Engine may be used to support modeling and forecasting of vector-borne infectious diseases such as malaria. In doing so, the chapter will also show how Earth Engine may be used to gather data for subsequent analyses outside of Earth Engine, the results of which can then also be brought back into Earth Engine.
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Wu, Qiusheng. "Sharing Work in Earth Engine: Basic UI and Apps." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_30.

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AbstractThe purpose of this chapter is to demonstrate how to design and publish Earth Engine Apps using both JavaScript and Python. You will be introduced to the Earth Engine User Interface JavaScript API and the geemap Python package. Upon completion of this chapter, you will be able to publish an Earth Engine App with a split-panel map for visualizing land cover change.
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Ramdani, Fatwa. "Integration of QGIS with Google Earth Engine (GEE)." In Exploring the Earth with QGIS. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46042-5_9.

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Srivastava, Anubhava, and Himanshu Sharma. "AI-Driven Environmental Monitoring Using Google Earth Engine." In Smart Sensors, Measurement and Instrumentation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68602-3_19.

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Engelstad, Peder, Daniel Carver, and Nicholas E. Young. "Working with GPS and Weather Data." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_51.

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AbstractThe purpose of this chapter is to demonstrate how to use Google Earth Engine as a means of associating remotely sensed data (weather observations) with open-source GPS point locations. These methods will provide a quick and easy way to access and analyze large amounts of information relative to your own research and efficiently move your data outside Earth Engine.
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Szeto, Sabrina H. "Collaborating in Earth Engine with Scripts and Assets." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_28.

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AbstractMany users find themselves needing to collaborate with others in Earth Engine at some point. Students may need to work on a group project, people from different organizations might want to collaborate on research together, or people may want to share a script or an asset they created with others. This chapter will show you how to collaborate with others and share your work.
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Van Den Hoek, Jamon, and Hannah K. Friedrich. "Humanitarian Applications." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_38.

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AbstractDespite the global refugee population's continued growth and the long-term habitation of many refugee settlements, there has been little overall attention from the Earth observation community toward environmental conditions and change in these settlements and their surrounding landscapes. However, considering the persistent concerns regarding the sustainability of environmental resource usage by refugees, potential environmental conflicts with nearby communities, and the impact of environmental hazards on refugee populations, it is crucial to gain a better understanding of the diverse
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Streszczenia konferencji na temat "Google Engine Earth"

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Chakraborty, Rhydam, Rwitayan Das, Bappaditya Sarkar, and Anindita Das Bhattacharjee. "Study of Mangrove Forests Using Google Earth Engine." In 2025 8th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2025. https://doi.org/10.1109/iementech65115.2025.10959483.

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Chowdhury, Anuva, Surajit Ghosh, and Richa Sharma. "Earth Observation-based drought assessment of Odisha, India, using Google Earth Engine." In 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS). IEEE, 2024. https://doi.org/10.1109/ingarss61818.2024.10984083.

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Desai, Geeta T., and Abhay N. Gaikwad. "Crop Classification Using Sentinel1 Data and Google Earth Engine." In 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS). IEEE, 2024. https://doi.org/10.1109/ingarss61818.2024.10984384.

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Garima, Kalyan Acharjya, and Karthikeyan M. P. "Google Earth Engine for Remote Sensing Big Data: A Comprehensive Review." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10867007.

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Masood, Raziqa, Sheeba Praveen, and Anubhava Srivastava. "AI-driven Real-Time Monitoring of Earthquakes using Google Earth Engine." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882382.

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Alírio, João, Neftalí P. Sillero, Nuno Garcia, et al. "Montrends: a Google Earth Engine application for analysing species habitat suitability over time." In Earth Resources and Environmental Remote Sensing/GIS Applications XV, edited by Karsten Schulz, Konstantinos G. Nikolakopoulos, and Ulrich Michel. SPIE, 2024. http://dx.doi.org/10.1117/12.3030967.

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Gaikwad, sandeep V., Amol D. Vibhute, Suraj V. Shah, Jatinderkumar R. Saini, and Karbhari V. Kale. "Vegetation health analysis of Vaijapur using Google Earth Engine and vegetation indices." In 2nd International Conference on Current Trends in Physics and Photonics (ICCTPP 2024), edited by Debabrata Saha and Aavishkar Katti. SPIE, 2024. http://dx.doi.org/10.1117/12.3044615.

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Varan, Andrei, Adriana Coroiu, and Liviu-Mihai Iacob. "Analyzing Deforestation Dynamics in Romania Using Random Forest Algorithm and Google Earth Engine." In 20th International Conference on Evaluation of Novel Approaches to Software Engineering. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013480400003928.

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Prasuna, Vempati Lakshmi, Tanimiki Sujith Kumar, and Puvvada Thanu Sree. "Shoreline and Land Cover Change Monitoring in Uppada Region with Google Earth Engine." In 2024 First International Conference for Women in Computing (InCoWoCo). IEEE, 2024. https://doi.org/10.1109/incowoco64194.2024.10863423.

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Yang, Yun, Martha Anderson, Kyle Knipper, et al. "Decreased Latency in Landsat Derived Evapotranspiration Products Using Machine Learning on Google Earth Engine." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641995.

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Raporty organizacyjne na temat "Google Engine Earth"

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Johansen, Richard, Molly Reif, Christina Saltus, and Kaytee Pokrzywinski. A broadscale assessment of Sentinel-2 imagery and the Google Earth Engine for the nationwide mapping of chlorophyll a. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48784.

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Harmful algal blooms degrade water quality and can result in adverse health impacts to humans and wildlife. Monitoring these at scale is difficult due to the lack of coincident data. Additionally, traditional field collection methods are labor- and cost-prohibitive, resulting in disparate data collection not capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to alleviate this by leveraging large, public, water quality databases coupled with open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usabili
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Kholoshyn, Ihor V., Olga V. Bondarenko, Olena V. Hanchuk, and Iryna M. Varfolomyeyeva. Cloud technologies as a tool of creating Earth Remote Sensing educational resources. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3885.

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This article is dedicated to the Earth Remote Sensing (ERS), which the authors believe is a great way to teach geography and allows forming an idea of the actual geographic features and phenomena. One of the major problems that now constrains the active introduction of remote sensing data in the educational process is the low availability of training aerospace pictures, which meet didactic requirements. The article analyzes the main sources of ERS as a basis for educational resources formation with aerospace images: paper, various individual sources (personal stations receiving satellite infor
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Djamai, N., R. A. Fernandes, L. Sun, F. Canisius, and G. Hong. Python version of Simplified Level 2 Prototype Processor for retrieving canopy biophysical variables from Sentinel-2 multispectral data. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/p8stuehwyc.

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La mission Sentinel-2 de Copernicus est conçue pour fournir des données pouvant être utilisées pour cartographier les variables biophysiques de la végétation a une échelle globale. Les estimations des variables biophysiques de la végétation ne sont pas encore produites de manière opérationnelle par le segment au sol de Sentinel-2. Plutôt, un algorithme de prédiction, appelé Simplified Level 2 Prototype Processor (SL2P), a été défini par l'Agence Spatiale Européenne. SL2P utilise deux réseaux neuronaux à rétropropagation, un pour estimer le variable biophysique de la végétation et l’autre pour
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Enhancing the MRC Land Use and Land Cover 2020 Mapping Products. Mekong River Commission Secretariat, 2023. http://dx.doi.org/10.52107/mrc.aqrsbr.

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This report summarizes the process starting from the background of the project, field data collection, and most importantly, the process of applying the Earth Observation technology via machine learning using the Google Earth Engine platform and the approach agreed by the expert group from the Member Countries to update the land cover and land use map of 2020 for the Lower Mekong Basin region.
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