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Статті в журналах з теми "GEE (Google Earth Engine)"
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Mutanga, Onisimo, and Lalit Kumar. "Google Earth Engine Applications." Remote Sensing 11, no. 5 (March 12, 2019): 591. http://dx.doi.org/10.3390/rs11050591.
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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 (September 21, 2021): 3778. http://dx.doi.org/10.3390/rs13183778.
Повний текст джерелаАнотація:
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 reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.
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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.
Повний текст джерелаАнотація:
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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Siska, Widia, Widiatmaka Widiatmaka, Yudi Setiawan, and Setyono Hari Adi. "Pemetaan Perubahan Lahan Sawah Kabupaten Sukabumi Menggunakan Google Earth Engine." TATALOKA 24, no. 1 (April 6, 2022): 74–83. http://dx.doi.org/10.14710/tataloka.24.1.74-83.
Повний текст джерелаАнотація:
Google Earth Engine (GEE) merupakan layanan pemrosesan geospasial yang telah banyak digunakan di berbagai bidang pemetaan. Tujuan penelitian ini adalah identifikasi perubahan lahan sawah Kabupaten Sukabumi menggunakan GEE. Data citra landsat 5 dan landsat 8 yang digunakan di GEE merupakan data citra yang telah di pre-process dan terkoreksi. Klasifikasi penggunaan/tutupan lahan dibedakan menjadi 6 kelas yaitu sawah, badan air, pemukiman, bervegetasi, hutan dan tanah terbuka. Sampel acak penggunaan lahan dibuat sebanyak 394 titik di GEE menggunakan poin dan rectangular. Klasifikasi penggunaan lahan dianilisis menggunakan metode Random Forest (RF). Penilaian akurasi dihitung menggunakan confusion Matrix, sedangkan validasi lapang dilakukan dengan menggunakan metode stratified random sampling. Uji akurasi analisis tutupan lahan tahun 2020 dengan confusion Matrix menghasilkan nilai Overall Accuracy (OA) 0,94 dan nilai kappa 0,91; tahun 2015 dengan nilai OA 0,93 dan nilai Kappa 0,91; sedangkan tahun 2010 memiliki nilai OA 0.96 dan nilai Kappa 0.94. Hasil analisis ini menunjukkan bahwa luas lahan sawah Kabupaten Sukabumi mengalami penyusutan seluas 10,317.27 ha dalam kurun waktu sepuluh tahun (2010-2020). Klasifikasi penggunaan lahan menggunakan GEE dapat menghasilkan peta dengan akurasi tinggi dengan OA >85%, serta dapat mempersingkat waktu analisis.
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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 (July 1, 2020): 4574. http://dx.doi.org/10.3390/app10134574.
Повний текст джерелаАнотація:
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 remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments.
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Rajandran, Arvinth, Mou Leong Tan, Narimah Samat, and Ngai Weng Chan. "A review of Google Earth Engine application in mapping aquaculture ponds." IOP Conference Series: Earth and Environmental Science 1064, no. 1 (July 1, 2022): 012011. http://dx.doi.org/10.1088/1755-1315/1064/1/012011.
Повний текст джерелаАнотація:
Abstract Google Earth Engine (GEE) can effectively monitor aquaculture ponds, but it is underutilized. This paper aims to review the application of GEE in mapping aquaculture ponds around the world using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. A total of 16 journal articles have been identified since 2019 from the Scopus and Science Direct databases. Most of the studies were conducted in China and United States using the Sentinel-2, Sentinel-1 and Landsat 8 images. Random Forest and Decision Tree are commonly used machine learning classifiers in GEE-based aquaculture ponds mapping studies. In general, some studies reported that GEE can extract the spatial distribution of aquaculture ponds with great overall accuracies, which are more than 90%. Difficult to detect small ponds and misclassification due to similar spectral reflectance are among the limitations reported in previous studies. Future research directions include incorporation of more aquaculture pond extraction techniques and different types of satellite images in GEE.
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Wang, Shujian, Ming Xu, Xunhe Zhang, and Yuting Wang. "Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine." Remote Sensing 14, no. 9 (April 25, 2022): 2055. http://dx.doi.org/10.3390/rs14092055.
Повний текст джерелаАнотація:
Google Earth Engine (GEE) has been widely used to process geospatial data in recent years. Although the current GEE platform includes functions for fitting linear regression models, it does not have the function to fit nonlinear models, limiting the GEE platform’s capacity and application. To circumvent this limitation, this work proposes a general adaptation of the Levenberg–Marquardt (LM) method for fitting nonlinear models to a parallel processing framework and its integration into GEE. We compared two commonly used nonlinear fitting methods, the LM and nonlinear least square (NLS) methods. We found that the LM method was superior to the NLS method when we compared the convergence speed, initial value stability, and the accuracy of fitted parameters; therefore, we then applied the LM method to develop a nonlinear fitting function for the GEE platform. We further tested this function by fitting a double-logistic equation with the global leaf area index (LAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data to the GEE platform. We concluded that the nonlinear fitting function we developed for the GEE platform was fast, stable, and accurate in fitting double-logistic models with remote sensing data. Given the generality of the LM algorithm, we believe that the nonlinear function can also be used to fit other types of nonlinear equations with other sorts of datasets on the GEE platform.
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Wang, Shujian, Ming Xu, Xunhe Zhang, and Yuting Wang. "Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine." Remote Sensing 14, no. 9 (April 25, 2022): 2055. http://dx.doi.org/10.3390/rs14092055.
Повний текст джерелаАнотація:
Google Earth Engine (GEE) has been widely used to process geospatial data in recent years. Although the current GEE platform includes functions for fitting linear regression models, it does not have the function to fit nonlinear models, limiting the GEE platform’s capacity and application. To circumvent this limitation, this work proposes a general adaptation of the Levenberg–Marquardt (LM) method for fitting nonlinear models to a parallel processing framework and its integration into GEE. We compared two commonly used nonlinear fitting methods, the LM and nonlinear least square (NLS) methods. We found that the LM method was superior to the NLS method when we compared the convergence speed, initial value stability, and the accuracy of fitted parameters; therefore, we then applied the LM method to develop a nonlinear fitting function for the GEE platform. We further tested this function by fitting a double-logistic equation with the global leaf area index (LAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data to the GEE platform. We concluded that the nonlinear fitting function we developed for the GEE platform was fast, stable, and accurate in fitting double-logistic models with remote sensing data. Given the generality of the LM algorithm, we believe that the nonlinear function can also be used to fit other types of nonlinear equations with other sorts of datasets on the GEE platform.
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Campos-Taberner, Manuel, Álvaro Moreno-Martínez, Francisco García-Haro, Gustau Camps-Valls, Nathaniel Robinson, Jens Kattge, and Steven Running. "Global Estimation of Biophysical Variables from Google Earth Engine Platform." Remote Sensing 10, no. 8 (July 24, 2018): 1167. http://dx.doi.org/10.3390/rs10081167.
Повний текст джерелаАнотація:
This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R2 = 0.87, RMSE = 0.54 m2/m2 and ME = 0.03 m2/m2 in the case of LAI, and R2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 = 0.42 and R2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.
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YILDIZ, Mitat Can, and Mustafa YİLMAZ. "Yer Yüzeyi Sıcaklığının Google Earth Engine Kullanılarak Elde Edilmesi ve Değerlendirilmesi." Afyon Kocatepe University Journal of Sciences and Engineering 22, no. 6 (December 28, 2022): 1380–87. http://dx.doi.org/10.35414/akufemubid.1181347.
Повний текст джерелаАнотація:
Günümüz temel problemlerinden biri olan küresel ısınma beraberinde iklim değişikliğini de getirmektedir. Atmosfer ve dünya arasındaki enerji değişimini etkilediği için Yer Yüzeyi Sıcaklığı (YYS) iklimin en önemli parametrelerinden birisidir. Bu nedenle büyük ve küçük ölçekli çalışmalar yapılırken YYS, göz önünde bulundurulması gerekmektedir. Uzaktan algılama verilerinin işlenmesi, analiz edilmesi ve değerlendirilmesi için birçok sistem geliştirilmiştir. Bunlardan birisi web tabanlı sistem olan Google Earth Engine (GEE)’dir. GEE arayüzü, farklı çözünürlüklere sahip uydu verilerinin hızlı bir biçimde değerlendirilmesini ve analiz edilmesini sağlar. Bu çalışmada 7 farklı istasyona ait toplamda 14 Landsat-8 uydu görüntüsü kullanılarak GEE platformunda kodlar yardımıyla 4 farklı metot ile yüzey sıcaklıkları elde edilmiştir. Elde edilen sıcaklıklar istasyondan ölçülen yakın hava sıcaklığı ile karşılaştırılarak tüm metotlar için Karesel Ortalama Hata (KOH) ve korelasyon değerleri hesaplanmıştır. Son dönemde çalışma konusu olarak artış gösteren YYS, GEE gibi kullanımı oldukça basit bir platformda ayrıca uydu görüntüsü indirmeye gerek kalmadan hızlı, kolay ve kısa bir sürede elde edilebileceği ortaya konulmuştur.
Дисертації з теми "GEE (Google Earth Engine)"
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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.
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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.
Знайти повний текст джерелаАнотація:
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 remote sensing technologies helped us to improve the monitoring of wetlands; however, detecting the presence of water under vegetation is still a challenge for correctly delineating the water extent. To address this issue and better detect the presence of water below vegetation, we employ different polarization of SAR sentinel-1 data in combination with optical sentinel-2. After preprocessing the images, we use the K-means clustering algorithm provided in the cloud computing platform of Google Earth Engine, to detect the increased backscatter coming from flooded vegetation duo to the double-bounce of the radar signal. We also take advantage of the high-resolution national land cover of Sweden as an ancillary layer to extract only the relevant information in our study area. Finally, we compare our results with hydroclimatic and field data gathered from the study area. Our workflow improves water-extent delineation in Swedish wetlands by 20% on average by detecting hidden water below the vegetation, which is generally not recognized by optical methods. The proposed method can be extended to monitor and study wetlands’ water availability and changes, contributing to the increase of their resilience to anthropogenic pressures and climate change.
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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.
Повний текст джерелаАнотація:
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 interpretazioni soprattutto per quanto concerne le lunghe serie temporali
Sorgono nuove sfide come l’elaborazione automatica, robusta ed efficace di tale quantità di dati. La definizione di nuovi approcci di CD è necessaria per sostituire le tecniche che erano efficaci sulla precedente generazione di immagini ma non su quelle di ultima generazione. Altre sfide consistono nella definizione di tecniche robuste ai dati di input con qualità variabile nel tempo. Le immagini di queste lunghe e dense serie temporali hanno informazioni ridondanti. Quindi, è plausibile che le mappe di cambio rilevate tra coppie di immagini estratte da una serie temporale siano correlate tra loro. Quindi diventa necessario, ma non sufficiente, eliminare nozioni ridondanti ai fini di ottenere risultati “puri” e validi.
Questa tesi esplora i meccanismi per sfruttare la correlazione temporale delle immagini per migliorare l’estrazione di informazioni da lunghe serie temporali. Vengono proposte nuove tecniche per migliorare i risultati CD ottenuti per una coppia di immagini o più dati telerilevati utilizzando classificazioni non supervisionate, regressioni logistiche per comprendere i trend, estrapolazioni di valori per pixel per analizzare i cambi di vegetazione all’interno delle serie storiche.
In questo contesto, è necessario il mascheramento delle nuvole e il ripristino dei pixel coperti da nuvole in lunghe serie temporali acquisite dai sensori ottici. In letteratura, le soluzioni a questo problema spesso non sfruttano la disponibilità di un gran numero di immagini disponibili per l’area di studio ma si basano solo sulla discriminazione monotemporale delle nuvole. Questo richiede lo sviluppo di nuovi paradigmi e nuove tecniche in grado di sfruttare pienamente tutti i dati e di elaborare in modo efficiente lunghe serie temporali di immagini.
Gli obiettivi principali di questo lavoro sono lo sviluppo di tecniche automatiche di CD e lo studio di meccanismi non supervisionati per la valutazione e il miglioramento dei risultati di CD che sfruttano lunghe serie temporali.
La tesi studia approcci multitemporali in grado di sfruttare appieno le informazioni acquisite sulla scena. In particolare, gli obiettivi e i nuovi contributi della tesi sono:
1. la proposta di un nuovo paradigma per l’estrazione di informazioni da lunghe serie temporali di immagini. Vengono proposti approcci per sfruttare strumenti open-source come Google Earth Engine e dati satellitari a medio-alta risoluzione geometrica su differenti scale spaziali riducendo i tempi di elaborazione delle immagini e di restituzione di output finali.
2. la definizione di modello che permette di monitorare la vegetazione post-incendio attraverso tecniche satellitari non invasive, riuscendo ad estrapolare trend sui massimi annuali degli indici di vegetazione consentendo l’analisi dei valori di singoli pixel ai fini di monitorare eventuali anomalie come: cambi di destinazione d’uso in aree naturali protette, eventi calamitosi innescati dall’incendio come frane, o di una non adeguata salvaguardia del territorio come eventuali tagli di aree forestali non controllate per scopi agricoli o di allevamento.
3. lo sviluppo di approcci per la valutazione e la stima del danno ecosistemico dovuto agli incendi. La comparazione delle mappe delle percorse dal fuoco con i piani urbanistici vigenti italiani, ai fini di perimetrare aree vulnerabili per la salvaguardia del Capitale Naturale.The 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.
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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.
Повний текст джерелаАнотація:
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.
Результатом виконання роботи є створений алгоритм виконання класифікації знімків супутників Landsat 7 і Sentinel-2 за допомогою платформи Google Earth Engine, отримані зображення масштабів забудови території міста Києва за 2000 - 2019 роки та наочний аналіз ориманих результатів, обрахований відсоток зменшення площі зелених зон, і рослинності загалом, у місті Києві за період 2000-2019 років.
Дані, отримані у результаті виконання роботи, можна використовувати для аналізу забудови міста Києва та для впровадження необхідних заходів щодо запобігання винекнення непоправних наслідків безконтрольного містобудування.
Застосовані методи та технології рекомендується використовувати в наукових та організаційно-правових цілях, а також, для отримання більш точних результатів, визначений алгоритм класифікації слід виконати за допомогою супутникових знімків з більшою роздільною здатністю.
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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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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.
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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 integrarsi al successivo utilizzo del satellite attraverso il software Google Earth Engine. Secondariamente, le GTs accoppiate al dato satellitare hanno permesso di identificare alcune fasi fenologiche (maturazione e fioritura) nel mais, girasole, soia e sorgo. Gli indici NDVI e NDWI descrivono bene la curva di crescita delle piante e in particolare la loro deviazione standard appare utile per identificare alcune variazioni di fenologia legata ad un non omogeneo accrescimento delle piante. L’applicazione di una tecnica di Machine Learning, attraverso il Random Forest Classifier, per creare un modello predittivo delle colture ha permesso di tentare l’identificazione delle colture nell’intera provincia di Ravenna. Le colture sono risultate ben riconosciute nell’area di raccolta delle GTs, mentre l’assenza di verità a terra nell’area vasta non ha permesso di validare i risultati della classificazione. I risultati ottenuti pongono le basi per integrare la conoscenza a terra ottenuta con metodi di rilievo a basso costo con l’informazione del programma Copernicus, per proseguire verso una precoce identificazione delle colture agricole in un’ottica di gestione sostenibile delle produzioni e dei loro sottoprodotti.
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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 frane e il loro monitoraggio.
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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 this Master’s thesis degree project, I use the computational power of Google Earth Engine and Google Cloud Platform to generate an oversized feature set in which I explore feature importance and analyse the influence of dimensionality reduction methods. I use Support Vector Machines (SVMs) for object-based classification of satellite images - a commonly used method. A large feature set is evaluated to find the most relevant features to discriminate the classes and thereby contribute most to high clas-sification accuracy. In doing so, one can bypass the sensitive knowledge-based but sometimes arbi-trary selection of input features.Two kinds of dimensionality reduction methods are investigated. The feature extraction methods, Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA), which transform the original feature space into a projected space of lower dimensionality. And the filter-based feature selection methods, chi-squared test, mutual information and Fisher-criterion, which rank and filter the features according to a chosen statistic. I compare these methods against the default SVM in terms of classification accuracy and computational performance. The classification accuracy is measured in overall accuracy, prediction stability, inter-rater agreement and the sensitivity to training set sizes. The computational performance is measured in the decrease in training and prediction times and the compression factor of the input data. I conclude on the best performing classifier with the most effec-tive feature set based on this analysis.In a case study of mapping urban land cover in Stockholm, Sweden, based on multitemporal stacks of Sentinel-1 and Sentinel-2 imagery, I demonstrate the integration of Google Earth Engine and Google Cloud Platform for an optimised supervised land cover classification. I use dimensionality reduction methods provided in the open source scikit-learn library and show how they can improve classification accuracy and reduce the data load. At the same time, this project gives an indication of how the exploitation of big earth observation data can be approached in a cloud computing environ-ment.The preliminary results highlighted the effectiveness and necessity of dimensionality reduction methods but also strengthened the need for inter-comparable object-based land cover classification benchmarks to fully assess the quality of the derived products. To facilitate this need and encourage further research, I plan to publish the datasets (i.e. imagery, training and test data) and provide access to the developed Google Earth Engine and Python scripts as Free and Open Source Software (FOSS).Kartläggning 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.
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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). The goal is to present a new way of collecting, compiling and visualizing geographical data in order to communicate, as an authority, with the public during a crisis. This has been done using a case study with focus on wildfires. Therefore, most of the work consisted of the creation of a prototype, CMAP – Crisis Management and Planning, that visualizes fire-related data. The basic work of the prototype consisted of determining what data that exists and is necessary for the information to be complete and easily understood together with how the data is best implemented. The existing data was retrieved online or via a scheduled API request. Eventrelated data, which is often created in connection with the event itself, was given a common structure and an automatic implementation into the prototype using Google Fusion Tables. In the prototype, data was visualized in two interactive map-based sections. These sections focused on providing the user with the information that might be needed if one fears that they are within an affected location or providing the user with general preparatory information in different counties. Finally, a non-map-based section was created that allowed the public to help authorities and each other via crowdsource data. This was collected in a digital form which was then directly visualized in the prototype’s map-based sections. The result of this showed, among other things, that automatic data flows are a good alternative for avoiding manual data handling and thus enabling a more frequent update of the data. Furthermore, it also showed the importance of having a common structure for which data to be included and collected in order to create a communication platform. Finally, by visualizing of dynamic polygon data in an interactive environment a development in crisis communication that can benefit the public’s understanding of the situation is achieved. This thesis is limited to the functionality and layout provided by the Google platform, including Google Earth Engine, Google Forms, Google Fusion Tables etcI 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.
Книги з теми "GEE (Google Earth Engine)"
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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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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.
Знайти повний текст джерелаЧастини книг з теми "GEE (Google Earth Engine)"
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Khan, Yahya Ali, Yuwei Wang, and Zongyao Sha. "Land Cover Change Analysis in Wuhan, China Using Google Earth Engine Platform and Ancillary Knowledge." In 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.
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Jahromi, Mojtaba Naghdyzadegan, Maryam Naghdizadegan Jahromi, Babak Zolghadr-Asli, Hamid Reza Pourghasemi, and Seyed Kazem Alavipanah. "Google Earth Engine and Its Application in Forest Sciences." In 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.
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Olusola, Adeyemi O., Oluwatola Adedeji, Lawrence Akpoterai, Samuel T. Ogunjo, Christiana F. Olusegun, and Samuel Adelabu. "Flood Assessment Along Lower Niger River Using Google Earth Engine." In 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.
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Capolupo, Alessandra, Cristina Monterisi, Alberico Sonnessa, Giacomo Caporusso, and Eufemia Tarantino. "Modeling Land Cover Impact on Albedo Changes in Google Earth Engine Environment." In 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.
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Hastings, Florencia, Ignacio Fuentes, Mario Perez-Bidegain, Rafael Navas, and Angela Gorgoglione. "Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine." In 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.
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Neeti, Neeti, Ayushi Pandey, and V. M. Chowdary. "Delineation of Waterlogged Areas Using Geospatial Technologies and Google Earth Engine Cloud Platform." In 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.
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Phuc, Vu Hoang Dinh, and Dao Nguyen Khoi. "Monitoring the Spatio-Temporal Changes of Can Gio Mangrove Forest Using Google Earth Engine." In Lecture Notes in Civil Engineering, 941–50. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5144-4_90.
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Danese, Maria, Dario Gioia, and Marilisa Biscione. "Integrated Methods for Cultural Heritage Risk Assessment: Google Earth Engine, Spatial Analysis, Machine Learning." In 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.
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Raju, Doggali, Adhugiri Laxmi Sanjana, and Rambabu Palaka. "Comparative Study on Rainfall and Water Availability in Irrigation Tanks Using Google Earth Engine." In Lecture Notes in Civil Engineering, 97–107. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7509-6_8.
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Kaplan, Gordana, and Mateo Gašparović. "Large-Scale Mapping and Monitoring Inland Waters by Google Earth Engine and Remote Sensing Techniques." In Geospatial Information Handbook for Water Resources and Watershed Management, 17–31. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003175018-3.
Повний текст джерелаТези доповідей конференцій з теми "GEE (Google Earth Engine)"
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Mota, Fernanda, Matheus Gonçalves, Marilton Aguiar, and Diana Adamatti. "Google Earth Engine e sua aplicabilidade na gestão de recursos hídricos." In 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.
Повний текст джерелаАнотація:
Os recursos e serviços hídricos desempenham um papel crucial no crescimento econômico e na sustentabilidade ambiental. Devido a isso, precisamos melhorar a coleta de dados hidrológicos, sua análise e o entendimento dos processos físicos da água. Este artigo tem como objetivo principal apresentar as funcionalidades da plataforma Google Earth Engine (GEE), tendo como objetivos específicos identificar e avaliar como a plataforma pode auxiliar no contexto de análise de dados em recursos hídricos. O GEE propicia a integração das tecnologias presentes em sistemas de informação geográficas, o que a torna interessante para o desenvolvimento de aplicações no âmbito da área ambiental. Este trabalho tem como estudo de caso o gerenciamento de recursos hídricos da bacia hidrográfica da Lagoa Mirim e Canal São Gonçalo. A análise resultante deste estudo pode auxiliar o Comitê de Gerenciamento das Bacias Hidrográficas na análise de dados das Bacias na região sul do Brasil.
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Suresh Babu, K. V., and V. S. K. Vanama. "Burn area mapping in Google Earth Engine (GEE) cloud platform: 2019 forest fires in eastern Australia." In 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.
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Ditian, R. D. Melinda Meganatha, Widodo Eko Prasetyo, Sanjaya Hartanto, and Rudi Hartono. "Mangrove Extent and Change Mapping of Muaragembong from 1990 to 2020 using Google Earth Engine (GEE)." In 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.
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Awad, Mohamad. "Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM)." In 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET). IEEE, 2021. http://dx.doi.org/10.1109/imcet53404.2021.9665519.
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Mateus, Matheus G., Fernanda P. Mota, Marilton S. Aguiar, and Diana F. Adamatti. "Visualizador de Água e Solo: Uma aplicação voltada para o gerenciamento de recursos naturais desenvolvida na plataforma Google Earth Engine." In 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.
Повний текст джерелаАнотація:
Os recursos e serviços hídricos estão diretamente ligados ao crescimento econômico e à sustentabilidade ambiental. Devido a isso, é importante aperfeiçoarmos a coleta, a análise e a percepção dos processos físicos da água. O objetivo deste trabalho é identificar e avaliar como a plataforma Google Earth Engine pode auxiliar no contexto de análise de dados em recursos hídricos. O GEE integra as tecnologias presentes em sistemas de informação geográficas, tornando-a interessante para o desenvolvimento de aplicações voltadas para os recursos naturais. O estudo de caso desta pesquisa foi aplicado no gerenciamento de recursos hídricos da bacia hidrográfica da Lagoa Mirim e Canal São Gonçalo. Os dados disponibilizados são uma ferramenta poderosa para o Comitê de Gerenciamento das Bacias Hidrográficas, que pode entender e analisar esta região.
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Kannangara, KATT, MB Shoukie, MPA Nayomi, SM Dassanayake, ABN Dassanyake, and CL Jayawardena. "Determining the Invasive Plant Dynamics in Bolgoda Lake Using Open-source Data." In 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.
Повний текст джерелаАнотація:
Identifying invasive plants (IP) and monitoring their dynamics is essential to minimize potential adverse effects on natural resources. Remote sensing (RS) could effectively cater to such requirements by acquiring data in many critical domains. Limitations of spatial resolution, spectral information, and large imagery files usually hinder retrieving, managing, and analyzing remotely sensed data. The cloud-based computational capabilities of Google Earth Engine (GEE) provide the amenities for geospatial data analysis, retrieval, and processing with access to a majority of freely available, public, multi-temporal RS data. Integrating machine learning algorithms into GEE generates a promising path toward operationalizing automated RS-based IP monitoring by overcoming traditional challenges. Use of Classification and Regression Trees (CART) classifier to generate water-vegetation classification over six years (2016-2021) with Landsat 8 and Sentinel 2 images enabled mapping the invasive plants and their dominant component of Water Hyacinth (Pontederia crassipes) across a heterogeneous landscape in Bolgoda Lake, Sri Lanka. Also, the study could develop a relatively accurate classification of the water-vegetation dynamics over the time of interest. The classified time series data indicates the annual variation of the water, vegetation, and non-vegetation classes with rapidly fluctuating seasonal cycles for the vegetation cover. These results could benefit regulatory authorities and institutions to optimize environmental resource management and prioritize eco-preservation attempts. Moreover, the findings reflect the capabilities of deep learning models to identify invasive plant behaviors even with modest spatial and spectral resolution imagery.
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HUACANI, Walquer, Nelson P. MEZA, Darío D. SANCHEZ, and Fernando HUANCA. "Land Use Mapping Using Machine Learning, Apurímac-Peru Region." In Air and Water – Components of the Environment 2022 Conference Proceedings. Casa Cărţii de Ştiinţă, 2022. http://dx.doi.org/10.24193/awc2022_17.
Повний текст джерелаАнотація:
The objective of the research is to develop a global land use / land cover map (LULC) of the Apurímac Region, from ESA Sentinel-2 images with a resolution of 10 m. to predict 10 soil type classes throughout the year in order to generate a representative snapshot of 2020. The methodology used in the analysis is the machine learning model, for the classification it was based on Artificial Intelligence (AI). For the processing, 6 bands of Sentinel-2 surface reflectance data were used: visible blue, green, red, near-infrared and two short-wave infrared bands, to create the final map, the model is run on multiple dates of images throughout the year on the Google Earth Engine (GEE) platform. The results of the study determine the total area is 2 111 415.29 ha, where the water represents 9 392.84 ha. (0.44%), on the other hand, snow/ice occupies 227.89 ha, representing 0.01%, while cultivated land occupies an area of 34 408.09 ha, (1.63%), bushes/shrubs occupy most of 1 740 486.69 ha, which represents 82.435% of the total area.
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Navarro, José A. "First Experiences with Google Earth Engine." In 3rd International Conference on Geographical Information Systems Theory, Applications and Management. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006352702500255.
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Uzhinskiy, Alexander Vladimirovich. "Google Earth Engine and machine learning for Earth monitoring." In The 6th International Workshop on Deep Learning in Computational Physics. Trieste, Italy: Sissa Medialab, 2022. http://dx.doi.org/10.22323/1.429.0021.
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Wang, Dar-Hsiung, Han-Ching Hsieh, Chin-Shien Wu, Tsuyoshi Honjo, Yu-Jen Chiang, and Pin-An Yang. "Visualization with Google Earth and gaming engine." In 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.
Повний текст джерелаЗвіти організацій з теми "GEE (Google Earth Engine)"
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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.], July 2020. http://dx.doi.org/10.31812/123456789/3885.
Повний текст джерелаАнотація:
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 information, drones, balloons, kites and balls) and Internet sources (mainstream sites, sites of scientific-technical organizations and distributors, interactive Internet geoservices, cloud platforms of geospatial analysis). The authors point out that their geospatial analysis platforms (Google Earth Engine, Land Viewer, EOS Platform, etc.), due to their unique features, are the basis for the creation of information thematic databases of ERS. The article presents an example of such a database, covering more than 800 aerospace images and dynamic models, which are combined according to such didactic principles as high information load and clarity.