Literatura académica sobre el tema "GEE (Google Earth Engine)"
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Artículos de revistas sobre el tema "GEE (Google Earth Engine)"
Mutanga, Onisimo y Lalit Kumar. "Google Earth Engine Applications". Remote Sensing 11, n.º 5 (12 de marzo de 2019): 591. http://dx.doi.org/10.3390/rs11050591.
Texto completoZhao, Qiang, Le Yu, Xuecao Li, Dailiang Peng, Yongguang Zhang y Peng Gong. "Progress and Trends in the Application of Google Earth and Google Earth Engine". Remote Sensing 13, n.º 18 (21 de septiembre de 2021): 3778. http://dx.doi.org/10.3390/rs13183778.
Texto completoFedotova, Elena y 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.
Texto completoSiska, Widia, Widiatmaka Widiatmaka, Yudi Setiawan y Setyono Hari Adi. "Pemetaan Perubahan Lahan Sawah Kabupaten Sukabumi Menggunakan Google Earth Engine". TATALOKA 24, n.º 1 (6 de abril de 2022): 74–83. http://dx.doi.org/10.14710/tataloka.24.1.74-83.
Texto completoGhaffarian, Saman, Ali Rezaie Farhadabad y Norman Kerle. "Post-Disaster Recovery Monitoring with Google Earth Engine". Applied Sciences 10, n.º 13 (1 de julio de 2020): 4574. http://dx.doi.org/10.3390/app10134574.
Texto completoRajandran, Arvinth, Mou Leong Tan, Narimah Samat y Ngai Weng Chan. "A review of Google Earth Engine application in mapping aquaculture ponds". IOP Conference Series: Earth and Environmental Science 1064, n.º 1 (1 de julio de 2022): 012011. http://dx.doi.org/10.1088/1755-1315/1064/1/012011.
Texto completoWang, Shujian, Ming Xu, Xunhe Zhang y Yuting Wang. "Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine". Remote Sensing 14, n.º 9 (25 de abril de 2022): 2055. http://dx.doi.org/10.3390/rs14092055.
Texto completoWang, Shujian, Ming Xu, Xunhe Zhang y Yuting Wang. "Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine". Remote Sensing 14, n.º 9 (25 de abril de 2022): 2055. http://dx.doi.org/10.3390/rs14092055.
Texto completoCampos-Taberner, Manuel, Álvaro Moreno-Martínez, Francisco García-Haro, Gustau Camps-Valls, Nathaniel Robinson, Jens Kattge y Steven Running. "Global Estimation of Biophysical Variables from Google Earth Engine Platform". Remote Sensing 10, n.º 8 (24 de julio de 2018): 1167. http://dx.doi.org/10.3390/rs10081167.
Texto completoYILDIZ, Mitat Can y Mustafa YİLMAZ. "Yer Yüzeyi Sıcaklığının Google Earth Engine Kullanılarak Elde Edilmesi ve Değerlendirilmesi". Afyon Kocatepe University Journal of Sciences and Engineering 22, n.º 6 (28 de diciembre de 2022): 1380–87. http://dx.doi.org/10.35414/akufemubid.1181347.
Texto completoTesis sobre el tema "GEE (Google Earth Engine)"
BELCORE, ELENA. "Generation of a Land Cover Atlas of environmental critic zones using unconventional tools". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2907028.
Texto completoVahidi, 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.
Buscar texto completoFATTORE, 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.
Texto completoThe increasing availability of new technologies, the improvement and miniaturization of current technologies, the greater accessibility of data with particular reference to free access to numerous (NASA, ESA) archives of remote sensing images, the growth in potential offered by Earth Observation (EO) technologies, can offer a great deal of information (EO) for the scientific and non-scientific community. However, in order to extract useful information from the immense amount of data available today, new analysis and interpretation approaches need to be defined, designed and implemented, especially with regard to long time series New challenges arise such as automatic, robust and efficient processing of this amount of data. The definition of new CD approaches is needed to replace techniques that were effective on the previous generation of images but not on the latest generation. Other challenges include defining techniques that are robust to input data with time-varying quality. Images from these long, dense time series have redundant information. Therefore, it is plausible that the change maps detected between pairs of images extracted from a time series are correlated with each other. Therefore, it becomes necessary, but not sufficient, to eliminate redundant notions in order to obtain 'pure' and valid results. This thesis explores mechanisms for exploiting the temporal correlation of images to improve the extraction of information from long time series. New techniques are proposed to improve CD results obtained for a pair of images or multiple remote sensing data using unsupervised classifications, logistic regressions to understand trends, and per-pixel value extrapolations to analyse vegetation changes within time series. In this context, cloud masking and restoration of cloud-covered pixels in long time series acquired by optical sensors is required. In the literature, solutions to this problem often do not take advantage of the availability of a large number of images available for the study area but rely only on monotemporal cloud discrimination. This requires the development of new paradigms and techniques capable of fully exploiting all data and efficiently processing long time series of images. The main objectives of this work are the development of automatic CD techniques and the study of unsupervised mechanisms for the evaluation and improvement of CD results exploiting long time series. The thesis studies multitemporal approaches capable of fully exploiting the information acquired on the scene. Specifically, the aims and new contributions of the thesis are: 1. the proposal of a new paradigm for extracting information from long time series of images. Approaches are proposed to exploit open-source tools such as Google Earth Engine and medium-high geometric resolution satellite data on different spatial scales, reducing image processing times and returning final outputs. 2. the definition of a model to monitor post-fire vegetation through non-invasive satellite techniques, being able to extrapolate trends on the annual maxima of vegetation indices, allowing the analysis of single pixel values in order to monitor possible anomalies such as: changes of use in protected natural areas, calamitous events triggered by the fire such as landslides, or inadequate land protection such as the cutting of uncontrolled forest areas for agricultural or breeding purposes. 3. the development of approaches for assessing and estimating ecosystem damage due to fires. The comparison of fire maps with current Italian urban plans, in order to delimit vulnerable areas for the safeguarding of Natural Capital.
Ільченко, Катерина Володимирівна. "Космічний моніторинг забудови території міста Києва". Thesis, Національний авіаційний університет, 2020. http://er.nau.edu.ua/handle/NAU/41605.
Texto completoGanem, 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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O projeto intitulado Mapeamento Anual da Cobertura e Uso do Solo no Brasil -MapBiomas - é um projeto atual voltado para a classificação da cobertura do solo a nível nacional. As diversas classes abordadas no âmbito do projeto estão sendo identificadas e mapeadas com base em imagens das séries Landsat. Após a seleção de duas cartas, a SC-24-V-C e a SD-23-X-D, com base em critérios específicos, foi feito um comparativo entre o dado com correção atmosférica obtido por meio dos algoritmos FLAASH e QUAC, e o produto em reflectância no topo da atmosfera (LC8_L1T_TOA), sem correção atmosférica. Este último é o dado atualmente utilizado pelo MapBiomas para o Landsat-8 em classificações resultantes de uma árvore de decisão definida empiricamente pela equipe do projeto. Tal investigação se torna necessária pois o MapBiomas necessita obter um panorama acerca de qual dado apresenta o melhor ajuste às classificações, demonstrando-se mais realista ao contexto da Caatinga, o qual se trata do bioma proporcionalmente menos estudado dentre as regiões naturais brasileiras. As classificações foram geradas pelo Code Editor do Google Earth Engine, uma plataforma capaz de processar imagens de satélite na nuvem de forma distribuída e rápida, permitindo que ferramentas de alto desempenho interpretem e analisem uma gama de informações, as quais são visualizadas em mapas. A partir de análises visuais e da aplicação de testes estatísticos de exatidão global e por classe identificou-se o dado que melhor se ajustou ao contexto da Caatinga, nas cartas selecionadas, mostrando-se mais adequado para proceder com o mapeamento da cobertura do solo no bioma. A carta SC-24-V-C apresentou valores do coeficiente Tau para as classificações oriundas do dado sem correção atmosférica e com dados corrigidos pelo FLAASH e QUAC, de, respectivamente, 54,13%, 39,13% e 58,10%. Já a carta SD-23-X-D apresentou resultados para o mesmo índice de, respectivamente, 55,45%, 68,90% e 64,90%. Isso mostrou que o dado com correção atmosférica, de modo geral, mostrou melhor ajuste ao contexto da Caatinga em comparação com o dado em reflectância no topo da atmosfera. Além disso, dentre os dados utilizados, o FLAASH apresentou maior inconsistência, mostrando-se ora o melhor, ora o pior para cada carta, sendo ainda bem complexo para executar em comparação ao QUAC, que por sua vez é mais rápido no tempo de processamento e apresentou melhor desempenho. O dado sem correção atmosférica não demonstrou diferenças significativas em comparação com os dados corrigidos. E, apesar de ter sido mais baixo, mostrou resultados praticamente idênticos em ambas as análises, o que faz com que o dado não deva ser dispensado, devendo apenas ser feitos ajustes nos parâmetros da árvore de decisão para que seu uso seja mais eficiente. Apesar da variabilidade dos resultados estatísticos em função dos tipos de dados utilizados, constatou-se que a plataforma Google Earth Engine se demonstrou prática, rápida e satisfatória para proceder com a classificação da cobertura do solo na Caatinga.
The project entitled Mapping Annual Coverage and Land Use in Brazil - MapBiomas - is a current project focused on the classification of soil cover at a national level. The various classes addressed in the scope of the project are being identified and mapped based on Landsat images. After the selection of the SC-24-VC and SD-23-XD letters based on specific criteria, a comparison was made between the atmospheric correction data obtained using the FLAASH and QUAC algorithms, and the product of reflectance on the top of atmosphere (LC8_L1T_TOA), without atmospheric correction. The latter data is currently used by MapBiomas for Landsat-8 in the soil cover classifications based on a decision tree empirically defined by the project team. This is a necessary research because the MapBiomas project needs to obtain a panorama about which data represents the best adjustment to the classifications, proving to be more realistic to the context of the Caatinga, which is the biome proportionally less studied among the Brazilian natural regions. The classifications were generated by the Code Editor of Google Earth Engine, a platform capable of processing satellite images in the cloud in a distributed and fast way, allowing high performance tools to interpret and analyze a range of information, which is visualized in the form of maps. Based on the visual analysis and the application of statistical tests of global accuracy and accuracy by class, the data that best fit the context of the Caatinga was identified as the more appropriate to proceed with the mapping of the soil cover in the biome. The letter SC-24-V-C presented Tau values for the classifications from the TOA data and with data corrected by FLAASH and QUAC, of 54.13%, 39.13% and 58.10%, respectively. SD-23-X-D presented results for the same index of, respectively, 55.45%, 68.90% and 64.90%. This showed that the atmospheric correction data, in general, presented a better fit to the context of the Caatinga compared to the top-of-atmosphere reflectance data. In addition, among the data used, FLAASH presented a greater inconsistency, showing to be sometimes the best and in other times the worst for each image. And it is still quite complex to be used if compared to QUAC, which is faster in processing time and had a better performance. The data without atmospheric correction did not show significant differences in comparison with the corrected data. And, even though it had lower values, it showed almost identical results in both analysis, which means that the data should not be dispensed, being necessary only adjustments in the decision tree parameters to achieve more efficient results. In the end, despite the variability of the data, it was found that Google Earth Engine is a highly effective tool to proceed with the classification of the ground cover.
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/.
Texto completoMartino, Filippo. "Analisi multitemporale del dissesto da immagini satellitari Sentinel (provincia di Forlì-Cesena)". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Buscar texto completoSanchez, 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.
Texto completoStromann, 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.
Texto completoKartläggning av jordens yta och dess snabba förändringar med fjärranalyserad data är ett viktigt verktyg för att förstå effekterna av en alltmer urban världsbefolkning har på miljön. Den imponerande mängden jordobservationsdata som är fritt och öppet tillgänglig idag utnyttjas dock endast marginellt i klassifikationer. Att hantera ett set av många variabler är inte lätt i standardprogram för bildklassificering. Detta leder ofta till manuellt val av få, antagligen lovande variabler. I det här arbetet använde jag Google Earth Engines och Google Cloud Platforms beräkningsstyrkan för att skapa ett överdimensionerat set av variabler i vilket jag undersöker variablernas betydelse och analyserar påverkan av dimensionsreducering. Jag använde stödvektormaskiner (SVM) för objektbaserad klassificering av segmenterade satellitbilder – en vanlig metod inom fjärranalys. Ett stort antal variabler utvärderas för att hitta de viktigaste och mest relevanta för att diskriminera klasserna och vilka därigenom mest bidrar till klassifikationens exakthet. Genom detta slipper man det känsliga kunskapsbaserade men ibland godtyckliga urvalet av variabler.Två typer av dimensionsreduceringsmetoder tillämpades. Å ena sidan är det extraktionsmetoder, Linjär diskriminantanalys (LDA) och oberoende komponentanalys (ICA), som omvandlar de ursprungliga variablers rum till ett projicerat rum med färre dimensioner. Å andra sidan är det filterbaserade selektionsmetoder, chi-två-test, ömsesidig information och Fisher-kriterium, som rangordnar och filtrerar variablerna enligt deras förmåga att diskriminera klasserna. Jag utvärderade dessa metoder mot standard SVM när det gäller exakthet och beräkningsmässiga prestanda.I en fallstudie av en marktäckeskarta över Stockholm, baserat på Sentinel-1 och Sentinel-2-bilder, demonstrerade jag integrationen av Google Earth Engine och Google Cloud Platform för en optimerad övervakad marktäckesklassifikation. Jag använde dimensionsreduceringsmetoder som tillhandahålls i open source scikit-learn-biblioteket och visade hur de kan förbättra klassificeringsexaktheten och minska databelastningen. Samtidigt gav detta projekt en indikation på hur utnyttjandet av stora jordobservationsdata kan nås i en molntjänstmiljö.Resultaten visar att dimensionsreducering är effektiv och nödvändig. Men resultaten stärker också behovet av ett jämförbart riktmärke för objektbaserad klassificering av marktäcket för att fullständigt och självständigt bedöma kvaliteten på de härledda produkterna. Som ett första steg för att möta detta behov och för att uppmuntra till ytterligare forskning publicerade jag dataseten och ger tillgång till källkoderna i Google Earth Engine och Python-skript som jag utvecklade i denna avhandling.
Althén, Bergman Felix y 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.
Texto completoI dagens samhälle är människan van vid teknik som ett ständigt hjälpmedel. Detta sätter också förväntningar på att information alltid ska vara tillgänglig och uppdaterad. Detta tillsammans med pågående klimatförändringar som lett till fler och svårare naturkatastrofer har lagt grunden till att det finns ett behov av att förändra hur man samlar in, sammanställer och visualiserar geografiska data som används för kommunikation i en krissituation. Denna studie utforskar hur myndigheter, i dagsläget, kommunicerar med allmänheten vid en krissituation och hur detta kan göras på ett enklare och mer givande sätt med hjälp av GIS. Målet är att visa ett nytt sätt att samla in, sammanställa och visualisera geografiska data för att, som myndighet, kommunicera med allmänheten under en kris. Detta har gjorts som i en fallstudie med fokus på skogs- och gräsbränder. Merparten av arbetet bestod därför av framtagande av en prototyp, CMAP – Crisis Management and Planning som visualiserar brandrelaterade data. Grundarbetet till prototypen bestod av att fastställa vilken data som finns och är nödvändig för att informationen skulle bli lättförstådd och komplett samt hur denna bäst implementeras. Den existerande data som implementerades hämtades online eller via ett schemalagt anrop av APIer. Händelserelaterade data skapas ofta i samband med själva händelsen och därför skapades en gemensam struktur och direktimplementation till prototypen för denna data med hjälp av Google Fusion Tables. I prototypen visualiserades data i två interaktiva kartbaserade sektioner. Dessa sektioner fokuserade kring att förse användaren med den information som kan behövas om man befarar att man befinner sig på en drabbad plats eller att förse användaren med allmän förberedande information inom olika län. Slutligen skapades även en icke kartbaserad sektion som möjliggjorde att allmänheten kan hjälpa myndigheter och varandra genom ”crowdsource” data. Denna samlades in i ett digitalt formulär som sedan direkt visualiserades i prototypens kartbaserade delar. Resultatet av detta visade bland annat att automatiska dataflöden är ett bra alternativ för att slippa manuell hantering av data och därmed möjliggöra en mer frekvent uppdatering. Vidare visade det även på vikten av att ha en gemensam struktur för vilken data som ska inkluderas och samlas in för att skapa en kommunikationsplattform. Slutligen är visualisering av dynamiska polygondata i en interaktiv miljö en utveckling av kriskommunikation som kan gynna förståelsen för situationen hos allmänheten. Studien är begränsad till att skapa en plattform baserad på den inbyggda funktionaliteten och designen som erbjuds i Googles plattform, detta inkluderat Google Earth Engine, Google Formulär, Google Fusion Tables etc.
Libros sobre el tema "GEE (Google Earth Engine)"
Google Earth Engine Applications. MDPI, 2019. http://dx.doi.org/10.3390/books978-3-03897-885-5.
Texto completoIntroductory course to Google Earth Engine. FAO, 2022. http://dx.doi.org/10.4060/cb9049en.
Texto completoFarhan, Muhammad, Noor Zaman Jhanjhi, Muhammad Umer, Rana M. Amir Latif, Mamoona Humayun y Syed Jawad Hussain. A Smart Agriculture Land Suitability Detection Model Using Machine Learning with Google Earth Engine. Eliva Press, 2020.
Buscar texto completoCapítulos de libros sobre el tema "GEE (Google Earth Engine)"
Khan, Yahya Ali, Yuwei Wang y Zongyao Sha. "Land Cover Change Analysis in Wuhan, China Using Google Earth Engine Platform and Ancillary Knowledge". En Geo-informatics in Sustainable Ecosystem and Society, 229–39. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7025-0_24.
Texto completoJahromi, Mojtaba Naghdyzadegan, Maryam Naghdizadegan Jahromi, Babak Zolghadr-Asli, Hamid Reza Pourghasemi y Seyed Kazem Alavipanah. "Google Earth Engine and Its Application in Forest Sciences". En Spatial Modeling in Forest Resources Management, 629–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56542-8_27.
Texto completoOlusola, Adeyemi O., Oluwatola Adedeji, Lawrence Akpoterai, Samuel T. Ogunjo, Christiana F. Olusegun y Samuel Adelabu. "Flood Assessment Along Lower Niger River Using Google Earth Engine". En Soil-Water, Agriculture, and Climate Change, 329–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12059-6_17.
Texto completoCapolupo, Alessandra, Cristina Monterisi, Alberico Sonnessa, Giacomo Caporusso y Eufemia Tarantino. "Modeling Land Cover Impact on Albedo Changes in Google Earth Engine Environment". En Computational Science and Its Applications – ICCSA 2021, 89–101. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87007-2_7.
Texto completoHastings, Florencia, Ignacio Fuentes, Mario Perez-Bidegain, Rafael Navas y Angela Gorgoglione. "Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine". En Computational Science and Its Applications – ICCSA 2020, 721–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58811-3_52.
Texto completoNeeti, Neeti, Ayushi Pandey y V. M. Chowdary. "Delineation of Waterlogged Areas Using Geospatial Technologies and Google Earth Engine Cloud Platform". En Geospatial Technologies for Land and Water Resources Management, 125–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90479-1_8.
Texto completoPhuc, Vu Hoang Dinh y Dao Nguyen Khoi. "Monitoring the Spatio-Temporal Changes of Can Gio Mangrove Forest Using Google Earth Engine". En Lecture Notes in Civil Engineering, 941–50. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5144-4_90.
Texto completoDanese, Maria, Dario Gioia y Marilisa Biscione. "Integrated Methods for Cultural Heritage Risk Assessment: Google Earth Engine, Spatial Analysis, Machine Learning". En Computational Science and Its Applications – ICCSA 2021, 605–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86970-0_42.
Texto completoRaju, Doggali, Adhugiri Laxmi Sanjana y Rambabu Palaka. "Comparative Study on Rainfall and Water Availability in Irrigation Tanks Using Google Earth Engine". En Lecture Notes in Civil Engineering, 97–107. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7509-6_8.
Texto completoKaplan, Gordana y Mateo Gašparović. "Large-Scale Mapping and Monitoring Inland Waters by Google Earth Engine and Remote Sensing Techniques". En Geospatial Information Handbook for Water Resources and Watershed Management, 17–31. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003175018-3.
Texto completoActas de conferencias sobre el tema "GEE (Google Earth Engine)"
Mota, Fernanda, Matheus Gonçalves, Marilton Aguiar y Diana Adamatti. "Google Earth Engine e sua aplicabilidade na gestão de recursos hídricos". En Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wcama.2020.11030.
Texto completoSuresh Babu, K. V. y V. S. K. Vanama. "Burn area mapping in Google Earth Engine (GEE) cloud platform: 2019 forest fires in eastern Australia". En 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC). IEEE, 2020. http://dx.doi.org/10.1109/icsidempc49020.2020.9299625.
Texto completoDitian, R. D. Melinda Meganatha, Widodo Eko Prasetyo, Sanjaya Hartanto y Rudi Hartono. "Mangrove Extent and Change Mapping of Muaragembong from 1990 to 2020 using Google Earth Engine (GEE)". En 2021 IEEE Ocean Engineering Technology and Innovation Conference: Ocean Observation, Technology and Innovation in Support of Ocean Decade of Science (OETIC). IEEE, 2021. http://dx.doi.org/10.1109/oetic53770.2021.9733736.
Texto completoAwad, Mohamad. "Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM)". En 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET). IEEE, 2021. http://dx.doi.org/10.1109/imcet53404.2021.9665519.
Texto completoMateus, Matheus G., Fernanda P. Mota, Marilton S. Aguiar y Diana F. Adamatti. "Visualizador de Água e Solo: Uma aplicação voltada para o gerenciamento de recursos naturais desenvolvida na plataforma Google Earth Engine". En Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/wcama.2021.15747.
Texto completoKannangara, KATT, MB Shoukie, MPA Nayomi, SM Dassanayake, ABN Dassanyake y CL Jayawardena. "Determining the Invasive Plant Dynamics in Bolgoda Lake Using Open-source Data". En International Symposium on Earth Resources Management & Environment. Department of Earth Resources Engineering, University of Moratuwa, Sri Lanka, 2022. http://dx.doi.org/10.31705/iserme.2022.15.
Texto completoHUACANI, Walquer, Nelson P. MEZA, Darío D. SANCHEZ y Fernando HUANCA. "Land Use Mapping Using Machine Learning, Apurímac-Peru Region." En Air and Water – Components of the Environment 2022 Conference Proceedings. Casa Cărţii de Ştiinţă, 2022. http://dx.doi.org/10.24193/awc2022_17.
Texto completoNavarro, José A. "First Experiences with Google Earth Engine". En 3rd International Conference on Geographical Information Systems Theory, Applications and Management. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006352702500255.
Texto completoUzhinskiy, Alexander Vladimirovich. "Google Earth Engine and machine learning for Earth monitoring". En The 6th International Workshop on Deep Learning in Computational Physics. Trieste, Italy: Sissa Medialab, 2022. http://dx.doi.org/10.22323/1.429.0021.
Texto completoWang, Dar-Hsiung, Han-Ching Hsieh, Chin-Shien Wu, Tsuyoshi Honjo, Yu-Jen Chiang y Pin-An Yang. "Visualization with Google Earth and gaming engine". En 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA). IEEE, 2012. http://dx.doi.org/10.1109/pma.2012.6524868.
Texto completoInformes sobre el tema "GEE (Google Earth Engine)"
Kholoshyn, Ihor V., Olga V. Bondarenko, Olena V. Hanchuk y Iryna M. Varfolomyeyeva. Cloud technologies as a tool of creating Earth Remote Sensing educational resources. [б. в.], julio de 2020. http://dx.doi.org/10.31812/123456789/3885.
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