Relevant bibliographies by topics / Google earth image

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Google earth image'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Google earth image"

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Carolina, Sparavigna Amelia. "The Shrinking Toshka Lakes in the Google Earth Images." International Journal of Sciences Volume 2, no. 2013-08 (2013): 92–94. https://doi.org/10.5281/zenodo.3348418.

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Toshka Lakes are lakes artificially created in the Sahara Desert of Egypt, by the water of the Nile, conveyed from the Nasser Lake through a canal in the Toshka Depression. From space, astronauts noticed the growing of a first lake, the easternmost one, in 1998. Then additional lakes grew in succession due west, the westernmost one between 2000 and 2001. The pictures of the Toshka Lakes taken by the crews of space missions and the satellite imagery can show the evolution of them. From 2006, the lakes started shrinking rapidly. The recent images, among them those of Google Earth, display that t
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Najaf, M., H. Arefi, H. Amini Amirkolaee, and B. Farajelahi. "MONOCULAR DEPTH ESTIMATION OF GOOGLE EARTH IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 14, 2023): 589–94. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-589-2023.

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Abstract. Depth estimation from images is an important task using scene understanding and reconstruction. Recently, encoder-decoder type fully convolutional architectures have gained great success in the area of depth estimation. Depth extraction from aerial and satellite images is one of the important topics in photogrammetry and remote sensing. This is usually done using image pairs, or more than two images. Solving this problem using a single image is still a challenging problem and has not been completely solved. Several convolutional neural networks have been proposed to extract depth fro
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Wang, Wenliang. "Automatic extraction of coastline based on Google Earth engine." International Journal of Computer Science and Information Technology 1, no. 1 (2023): 102–11. http://dx.doi.org/10.62051/ijcsit.v1n1.14.

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Traditional remote sensing image-based coastline extraction is limited by data volume and processing speed, and the extracted coastline is susceptible to noise. Therefore, this paper proposes a method based on the Google Earth Engine geospatial platform, which combines threshold segmentation, the Otsu method, and morphological algorithms. First, remote sensing images are preprocessed on the platform, and the Normalized Difference Water Index (NDWI) is calculated. Then, the Otsu method is used to calculate the NDWI threshold for water-land segmentation, resulting in a binary water-land image. N
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Mr., Sadashiv D. Lavange* 1. Prof. A. D. Vishwakarma 2. Prof. H. T. Ingale 3. &. Prof. V. D. Chaudhari 4. "IMAGE LOCALIZATION USING GOOGLE MAPS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 423–26. https://doi.org/10.5281/zenodo.828673.

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Now days, government of India is making awareness about green energy. So, various analyses have to make for promotion of green energy like solar energy over the country. It is required to know about the rooftop area useful for holding solar panels. Image localization and image registration techniques are the mostly used by the researchers for searching image in specified place. Google earth image searching is required for localization and analysis of specific area over the areal image. This type of searching is useful for calculating the area of solar rooftop in any specific location. Within t
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Mr., Sadashiv D. Lavange* 1. Prof. A. D. Vishwakarma 2. Prof. H. T. Ingale 3. &. Prof. V. D. Chaudhari 4. "IMAGE LOCALIZATION USING GOOGLE MAPS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 585–88. https://doi.org/10.5281/zenodo.829780.

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Now days, government of India is making awareness about green energy. So, various analyses have to make for promotion of green energy like solar energy over the country. It is required to know about the rooftop area useful for holding solar panels. Image localization and image registration techniques are the mostly used by the researchers for searching image in specified place. Google earth image searching is required for localization and analysis of specific area over the areal image. This type of searching is useful for calculating the area of solar rooftop in any specific location. Within t
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Guerriero, Luigi, Diego Di Martire, Domenico Calcaterra, and Mirko Francioni. "Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation." Remote Sensing 12, no. 21 (2020): 3518. http://dx.doi.org/10.3390/rs12213518.

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An increasing number of satellite platforms provide daily images of the Earth’s surface that can be used in quantitative monitoring applications. However, their cost and the need for specific processing software make such products not often suitable for rapid mapping and deformation tracking. Google Earth images have been used in a number of mapping applications and, due to their free and rapid accessibility, they have contributed to partially overcome this issue. However, their potential in Earth’s surface displacement tracking has not yet been explored. In this paper, that aspect is analyzed
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Schmitt, M., L. H. Hughes, C. Qiu, and X. X. Zhu. "AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 145–52. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-145-2019.

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<p><strong>Abstract.</strong> Cloud coverage is one of the biggest concerns in spaceborne optical remote sensing, because it hampers a continuous monitoring of the Earth’s surface. Based on Google Earth Engine, a web- and cloud-based platform for the analysis and visualization of large-scale geospatial data, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for user-defined areas of interest and time periods, which can be significantly shorter than the one-year time frames that are commonly used in other multi-temporal image aggregation approache
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Prayogo, Luhur Moekti. "Mangrove Vegetation Mapping Using Sentinel-2A Imagery Based on Google Earth Engine Cloud Computing Platform." International Journal of Science, Engineering and Information Technology 6, no. 1 (2021): 249–55. http://dx.doi.org/10.21107/ijseit.v6i1.12175.

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Mangroves are trees whose habitat is affected by tides, and their presence has decreased from year to year. Today, mapping technology has undergone many developments, including the availability of images of various resolutions and cloud-based image processing. One of the popular platforms today is the Google Earth Engine. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for extensive processing. The advantage of using Google Earth Engine is that users do not have to be IT experts without experts in application development, WEB prog
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Qian, Junhao, Min Xia, Yonghong Zhang, Jia Liu, and Yiqing Xu. "TCDNet: Trilateral Change Detection Network for Google Earth Image." Remote Sensing 12, no. 17 (2020): 2669. http://dx.doi.org/10.3390/rs12172669.

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Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has t
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Chen, Yao, and Xiaoning Chen. "Image interpolation of gas concentration based on Google Earth." Journal of Physics: Conference Series 1176 (March 2019): 022018. http://dx.doi.org/10.1088/1742-6596/1176/2/022018.

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Dissertations / Theses on the topic "Google earth image"

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Zhang, Ruibo, and Manni Chen. "Extraction of street from google earth imagery." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9399.

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Extraction of streets from Google earth imagery is a hot research topic. The main purpose of this paper is to create a method to extract streets information from satellite image automatically. It is exceedingly difficult to achieve, because every road has different characters and there are a lot of noises (e.g. shadow, building, and vehicle) in the image. By using generic color model and the image analysis techniques, we build up the automatic road extraction system. It extracted road successfully from mid-size city image with a very high extraction rate. Some interesting discoveries and uniqu
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Becklinger, Nicole Lynn. "Design and test of a multi-camera based orthorectified airborne imaging system." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/461.

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Airborne imaging platforms have been applied to such diverse areas as surveillance, natural disaster monitoring, cartography and environmental research. However, airborne imaging data can be expensive, out of date, or difficult to interpret. This work introduces an Orthorectified Airborne Imaging (OAI) system designed to provide near real time images in Google Earth. The OAI system consists of a six camera airborne image collection system and a ground based image processing system. Images and position data are transmitted from the air to the ground station using a point to point (PTP) data lin
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Griffiths, Thomas Richard. "An Enhanced Data Model and Tools for Analysis and Visualization of Levee Simulations." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3477.pdf.

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Felipe, Alexandre Luis da Silva [UNESP]. "Topografia convencional na aferição de áreas obtidas por georreferenciamento e Google Earth." Universidade Estadual Paulista (UNESP), 2015. http://hdl.handle.net/11449/132115.

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Made available in DSpace on 2015-12-10T14:23:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-08-07. Added 1 bitstream(s) on 2015-12-10T14:30:04Z : No. of bitstreams: 1 000853186.pdf: 790204 bytes, checksum: 5290f2778fd056916d241a6d1218d3db (MD5)<br>O presente trabalho objetivou comparar distâncias horizontais e áreas de um polígono considerando pontos homólogos obtidos através de levantamento topográfico convencional realizado por Estação Total Nikon Nivo 322d, levantamento georreferenciado por receptor GNSS AshTech Pro Mark 200 e imagem do Google Earth. O processamento do levantam
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Felipe, Alexandre Luis da Silva 1978. "Topografia convencional na aferição de áreas obtidas por georreferenciamento e Google Earth /." Botucatu, 2015. http://hdl.handle.net/11449/132115.

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Orientador: Lincoln Gehring Cardoso<br>Banca: Luciano Nardini Gomes<br>Banca: Bruna Soares Xavier de Barros<br>Resumo: O presente trabalho objetivou comparar distâncias horizontais e áreas de um polígono considerando pontos homólogos obtidos através de levantamento topográfico convencional realizado por Estação Total Nikon Nivo 322d, levantamento georreferenciado por receptor GNSS AshTech Pro Mark 200 e imagem do Google Earth. O processamento do levantamento topográfico foi realizado através programa computacional DataGeosis versão Office que acusou elevada precisão, constituindo-se em referên
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Strey, Fábio. "A contradição entre a importância e o uso do google earth como recurso didático." Universidade Estadual do Oeste do Parana, 2014. http://tede.unioeste.br:8080/tede/handle/tede/49.

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Made available in DSpace on 2017-05-12T14:42:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-08-26<br>The main objective of this research is to search for answers, trough the use of Google Earth images, to teach the subject of Geography in Paraná's public schools, utilizing for sampling the schools belonging to the Regional Education Nucleus of Francisco Beltrão. As well as reflect about the teaching of Geography looking into the didactic-pedagogical process in the domain of categories and geotechnological information techniques in some aspects of the pedagogical relations, especia
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Vale, Thiago Souza. "O Google Earth como procedimento metodológico na prática pedagógica da Geografia no Ensino Fundamental II." Pontifícia Universidade Católica de São Paulo, 2014. https://tede2.pucsp.br/handle/handle/12314.

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Made available in DSpace on 2016-04-27T18:15:44Z (GMT). No. of bitstreams: 1 Thiago Souza Vale.pdf: 3481122 bytes, checksum: 3870ff16b1d0a8120dd5019f7b9aec9b (MD5) Previous issue date: 2014-03-11<br>Secretaria da Educação do Estado de São Paulo<br>This thesis discusses the use of Google Earth as methodological procedure in the teaching and learning of geographical education to the students of Escola Estadual Caetano de Campo, held in the 1st half of 2013. The popularization and dissemination of geographical information targeted to the geographical information are increasingly present in braz
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ALMEIDA, CASSIO FREITAS PEREIRA DE. "POPULATION DISTRIBUTION MAPPING THROUGH THE DETECTION OF BUILDING AREAS IN GOOGLE EARTH IMAGES OF HETEROGENEOUS REGIONS USING DEEP LEARNING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=32969@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Informações precisas sobre a distribuição da população são reconhecidamente importantes. A fonte de informação mais completa sobre a população é o censo, cujos os dados são disponibilizados de forma agregada em setores censitários. Esses setores são unidades operacionais de tamanho e formas irregulares, que dificulta a análise espacial dos dados associados. Assim, a mudança de setores censitários para um conjunto de células regulares com esti
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Frini, Marouane. "Diagnostic des engrenages à base des indicateurs géométriques des signaux électriques triphasés." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES052.

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Bien qu’ils soient largement utilisés dans le domaine, les mesures vibratoires classiques présentent plusieurs limites. A la base, l’analyse vibratoire ne peut identifier qu’environ 60% des défauts qui peuvent survenir dans les machines. Cependant, les principaux inconvénients des mesures de la vibration sont l’accès difficile au système de transmission afin d’y placer le capteur ainsi que le coût conséquent de la mise en œuvre. Ceci résulte en des problèmes de sensibilité relatifs à la position de l’installation et ceux de difficulté pour distinguer la source de vibration à cause de la divers
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LIN, SHIN-WEI, and 林炘緯. "Research on the Shoreline Change of Cijin Coast Using Google Earth Image." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/58344279442970177119.

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碩士<br>國立高雄海洋科技大學<br>海事資訊科技研究所<br>105<br>Recent years it is found that coastline erosion has long been a serious problem in Cijin City, Kaohsiung. In order to mitigate coastal erosion, Kaohsiung City Government was conducted a “Cijin Coastline Protection Project” using detached breakwater, offshore submerged breakwater, artificial headland and beach nourishment. The project was finished in August, 2013. Traditionally, a coastline measurement relies on manpower. However, the measurement is hard to proceed precisely because the measured errors of coastal lines might be contributed by repeatab
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Books on the topic "Google earth image"

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Crowder, David A. Google Earth For Dummies. John Wiley & Sons, Ltd., 2007.

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Crowder, David A. Google Earth for Dummies. Wiley & Sons, Incorporated, John, 2011.

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Crowder, David A. Google Earth for Dummies. Wiley & Sons, Incorporated, John, 2011.

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Book chapters on the topic "Google earth image"

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Donchyts, Gennadii. "Exploring Image Collections." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_13.

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AbstractThis chapter teaches how to explore image collections, including their spatiotemporal extent, resolution, and values stored in images and image properties. You will learn how to map and inspect image collections using maps, charts, and interactive tools and how to compute different statistics of values stored in image collections using reducers.
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Dyson, Karen, Andréa Puzzi Nicolau, David Saah, and Nicholas Clinton. "Neighborhood-Based Image Transformation." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_10.

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Crowley, Morgan A., Jeffrey A. Cardille, and Noel Gorelick. "Object-Based Image Analysis." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_11.

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Dyson, Karen, Andréa Puzzi Nicolau, David Saah, and Nicholas Clinton. "Interpreting an Image: Regression." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_8.

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AbstractThis chapter introduces the use of regression to interpret imagery. Regression is one of the fundamental tools you can use to move from viewing imagery to analyzing it. In the present context, regression means predicting a numeric variable for a pixel instead of a categorical variable, such as a class label.
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Hermosilla, Txomin, Saverio Francini, Andréa P. Nicolau, et al. "Clouds and Image Compositing." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_15.

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AbstractThe purpose of this chapter is to provide necessary context and demonstrate different approaches for image composite generation when using data quality flags, using an initial example of removing cloud cover. We will examine different filtering options, demonstrate an approach for cloud masking, and provide additional opportunities for image composite development. Pixel selection for composite development can exclude unwanted pixels—such as those impacted by cloud, shadow, and smoke or haze—and can also preferentially select pixels based upon proximity to a target date or a preferred s
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Nicolau, Andréa Puzzi, Karen Dyson, David Saah, and Nicholas Clinton. "Interpreting an Image: Classification." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_6.

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Donchyts, Gennadii, and Fedor Baart. "Advanced Raster Visualization." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_27.

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AbstractThis chapter should help users of Earth Engine to better understand raster data by applying visualization algorithms such as hillshading, hill shadows, and custom colormaps. We will also learn how image collection datasets can be explored by animating them as well as by annotating with text labels, using, e.g., attributes of images or values queried from images.
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Nicolau, Andréa Puzzi, Karen Dyson, David Saah, and Nicholas Clinton. "Survey of Raster Datasets." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_3.

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AbstractThe purpose of this chapter is to introduce you to the many types of collections of images available in Google Earth Engine. These include sets of individual satellite images, pre-made composites (which merge multiple individual satellite images into one composite image), classified land use and land cover (LULC) maps, weather data, and other types of datasets. If you are new to JavaScript or programming, work through Chaps. F1.0 and F1.1 first.
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Dyson, Karen, Andréa Puzzi Nicolau, Nicholas Clinton, and David Saah. "Advanced Pixel-Based Image Transformations." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_9.

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Dyson, Karen, Andréa Puzzi Nicolau, David Saah, and Nicholas Clinton. "The Remote Sensing Vocabulary." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_4.

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AbstractThe purpose of this chapter is to introduce some of the principal characteristics of remotely sensed images and how they can be examined in Earth Engine. We discuss spatial resolution, temporal resolution, and spectral resolution, along with how to access important image metadata. You will be introduced to image data from several sensors aboard various satellite platforms. At the completion of the chapter, you will be able to understand the difference between remotely sensed datasets based on these characteristics and how to choose an appropriate dataset for your analysis based on thes
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Conference papers on the topic "Google earth image"

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Kaur, Navneet, Rishi Prakash, and Manoj Diwakar. "LULC Classification of Sentinel-2 Satellite Image Using Random Forest Algorithm in Google Earth Engine." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10840515.

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Loo, Chu Kiong, and Huang Han Wang. "Satellite Image and Tree Canopy Height Analysis Using Machine Learning on Google Earth Engine with Carbon Stock Estimation." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831413.

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Ragavendiran, S. D. Prabu, NR Gowthami, Gayathri C, D. Ramya, D. Padmapriya, and S. V. Suji Aparna. "A Novel Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer for Satellite Image Classification with Google Earth Engine." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866210.

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Rodriguez, Adonnis, Daniel Henriquez, Lizardo Arias, and Carlos Pocasangre. "Identification of Thermal Anomalies in Active Volcanoes Using Google Earth Engine and Multi-Spectral Satellite Images." In 2024 IEEE Biennial Congress of Argentina (ARGENCON). IEEE, 2024. http://dx.doi.org/10.1109/argencon62399.2024.10735897.

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Wu, Qianqian, Xianping Ma, Jialu Sui, and Man-On Pun. "A Sam-Empowered Dual-Stream Framework for Scene-Level Local Climate Zone Classification Using Google Earth and Sentinel Images." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642854.

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Gasparovic, Mateo, Ivana Metic, and Radan Vujnovic. "LONG-TERM VEGETATION DYNAMICS IN THE SPACVAN BASIN (1984-2023): REMOTE SENSING INSIGHTS INTO CLIMATIC IMPACTS AND EXTREME WEATHER EVENTS." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024v/4.2/s19.43.

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This research focuses on the long-term assessment of vegetation quality dynamics in the Spacvan Basin from 1984 to 2023. The objective is to evaluate how specific climatic factors influence vegetation health. The study employed remote sensing techniques, analyzing satellite images from the LANDSAT and Sentinel missions. The Spacvan Basin, covering 43,519 ha, served as the area of interest. Data collection, processing, and analysis were conducted using the Google Earth Engine platform. NDVI values were measured over the period from 1984 to 2023, revealing an overall increase in the index despit
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Li Shaomei, Kan Yinghong, Liu Haiyan, and An Xiaoya. "Image-based mapping using Google Earth images." In 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5487246.

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Crowley, Matthew David, William Chen, Eric J. Sukalac, Xiuhong Sun, Patrick L. Coronado, and Guo-Qiang Zhang. "Visualization of Remote Hyperspectral Image Data Using Google Earth." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.233.

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Ding, Weili, Feng Zhu, and Yingming Hao. "Interactive 3D City Modeling using Google Earth and Ground Images." In Fourth International Conference on Image and Graphics (ICIG 2007). IEEE, 2007. http://dx.doi.org/10.1109/icig.2007.5.

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Pan, Hong-jun, and Xiaoqiu Yao. "Marine Salvage Management Information System Based on Google Earth/GPS Technology." In 2010 International Conference on Optoelectronics and Image Processing (ICOIP). IEEE, 2010. http://dx.doi.org/10.1109/icoip.2010.31.

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Reports on the topic "Google earth image"

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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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Fedoseev, O. N. Calculation of the coverage area based on GOOGLE EARTH satellite images (MAP PIXEL-decryption V1.1 ). Ailamazyan Program Systems Institute of Russian Academy of Sciences, 2024. http://dx.doi.org/10.12731/ofernio.2023.25277.

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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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