Google Earth Engine Applications

The Google Earth Engine (GEE) is a cloud computing platform designed to store and process huge data sets (at petabyte-scale) for analysis and ultimate decision making [1]. Following the free availability of Landsat series in 2008, Google archived all the data sets and linked them to the cloud computing engine for open source use. The current archive of data includes those from other satellites, as well as Geographic Information Systems (GIS) based vector data sets, social, demographic, weather, digital elevation models, and climate data layers

Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine

This article provides an angular-based radiometric slope correction routine for Sentinel-1 SAR imagery on the Google Earth Engine platform. Two established physical reference models are implemented. The first model is optimised for vegetation applications by assuming volume scattering on the ground. The second model is optimised for surface scattering, and therefore targeted at urban environments or analysis of soil characteristics. The framework of both models is extended to simultaneously generate masks of invalid data in active layover and shadow affected areas. A case study, using openly available and reproducible code, exemplarily demonstrates the improvement of the backscatter signal in a mountainous area of the Austrian Alps. Furthermore, suggestions for specific use cases are discussed and drawbacks of the method with respect to pixel-area based methods are highlighted. The radiometrically corrected radar backscatter products are overcoming current limitations and are compliant with recent CEOS specifications for SAR backscatter over land. This improves a wide range of potential usage scenarios of the Google Earth Engine platform in mapping various land surface parameters with Sentinel-1 on a large scale and in a rapid manner. The provision of an openly accessible Earth Engine module allows users a smooth integration of the routine into their own workflows

Google Earth Engine capabilities for learning satellite image processing

Bakalářská práce se zaměřuje na využití Google Earth Engine pro výuku zpracování družicových snímků. V teoretické části práce jsou popsány základy digitálního zpracování družicových dat a je představena platforma Google Earth Engine spolu s přehledem dostupných studijních materiálu týkajících se Google Earth Engine. Praktická část práce zahrnuje tvorbu čtyř studijních materiálů, které pokrývají řadu témat z oblasti dálkového průzkumu Země. Tyto materiály obsahují ukázkové skripty pro Earth Engine JavaScript API a pracují s daty z družic Sentinel-2, Sentinel-5P a Landsat 9. Veškerá použitá data jsou dostupná v datovém katalogu Google Earth Engine. Cílem těchto materiálů je rozšířit programové možnosti pro výuku zpracování družicových snímků pro studenty geoinformatiky. Studijní materiály zahrnují témata jako spektrální a ohniskové zvýraznění obrazu, klasifikace, maskování oblak a tvorba webových aplikací pomocí JavaScript API. Vytvořené materiály jsou k dispozici ve formátu PDF na stránkách vedoucí bakalářské práce, a jsou určeny k použití pro výuku předmětu Dálkový průzkum Země na Katedře geoinformatiky VŠB-TUO. Praktická část práce také popisuje pilotní projekty, které předcházely tvorbě studijních materiálů. Na závěr práce je provedeno vyhodnocení vhodnosti platformy Google Earth Engine pro výuku studentů. Na základě získaných poznatků byla platforma shledána jako efektivní a vhodná pro výuku zpracování družicových snímků.The bachelor thesis is focused on the utilization of Google Earth Engine for teaching satellite image processing. The theoretical section describes the basics of digital image processing and introduces the Google Earth Engine platform, along with an overview of available study materials related to it. The practical part of the thesis involves the development of four study materials covering a range of topics from remote sensing. These materials contain sample scripts for the Earth Engine JavaScript API and work with data from Sentinel-2, Sentinel-5P and Landsat 9 satellites. The goal of these materials is to extend the software capabilities for teaching satellite image processing to geoinformatics students. Topics covered in the learning materials include spectral and focal image enhancement, classification, cloud masking, and creating web applications using JavaScript API. The created materials are available in PDF format on the website of the bachelor's thesis supervisor and are intended for teaching remote sensing at the Department of Geoinformatics of VSB – Technical University of Ostrava. The practical section also describes pilot projects that preceded the creation of the study materials. Finally, an evaluation of the suitability of the Google Earth Engine platform for teaching students is provided. Based on the obtained knowledge, the platform was found to be effective and suitable for teaching satellite image processing.548 - Katedra geoinformatikyvýborn

Deteksi Alih Fungsi Lahan Padi Sawah Menggunakan Sentinel-2 dan Google Earth Engine di Kota Serang, Provinsi Banten

Land is one of the main factors in rice production. However, the transfer of agricultural land functions to other sectors continues and becomes a challenge in the food supply in Indonesia. Serang City is one of the rice-producing areas in Banten Province. This study aims to analyze changes in the transfer of rice field functions to other sectors by mapping rice field cover using Sentinel-2 satellite imagery in 2021 compared to 2019 with the Random Forest method by using Google Earth Engine (GEE) applications and cloud computing support. The study results showed that the cover of rice fields in Serang City in 2021 decreased by 602.87 ha (-7.20%) compared to 2019 from the total land cover. Land cover in other vegetation was also reduced by 242 ha (-2.45%), while urban land cover in 2021 increased by 781.82 ha (10.89%). This study shows that there has been a change in land transfer in Serang City due to urban expansion in 3 years, as well as that the use of GEE can streamline monitoring of changes in land transfer and land use cover.   Keywords: rice field, Google Earth Engine, Sentinel-

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent

Free global DSM assessment on large scale areas exploiting the potentialities of the innovative google earth engine platform

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto-Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters. In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale

The Google Similarity Distance

Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of `society' is `database,' and the equivalent of `use' is `way to search the database.' We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts we use the world-wide-web as database, and Google as search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the world-wide-web using Google page counts. The world-wide-web is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies, and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87% with the expert crafted WordNet categories.Comment: 15 pages, 10 figures; changed some text/figures/notation/part of theorem. Incorporated referees comments. This is the final published version up to some minor changes in the galley proof

Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version