Google Earth Engine: Planetary-scale geospatial analysis for everyone

Gorelick, Noel; Hancher, M.; Dixon, Mike; Ilyushchenko, Simon; Thau, David; Moore, Rebecca

doi:10.1016/j.rse.2017.06.031

Cited by 8,075 publications

(4,870 citation statements)

References 19 publications

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“…However, this limitation is being overcome by the increasing number of users developing algorithms that may be potentially implemented in GEE for a wide range of applications. The functions in GEE utilize several built-in parallelization and data distribution models to achieve high performance [30]. The RFs' implementation in GEE is not an exception to that.…”

Section: Discussionmentioning

confidence: 99%

“…In GEE, the system handles and hides nearly every aspect of how a computation is managed, including resource allocation, parallelism, data distribution, and retries. These decisions are purely administrative; none of them can affect the result of a query, only the speed at which it is produced [30]. Under these circumstances, it is very difficult to give exact computation times because they vary in every run.…”

Section: Discussionmentioning

confidence: 99%

“…This situation also occurs because of the wide variety of free available remote sensing data disseminated by agencies such as the National Aeronautics and Space Administration (NASA) (e.g., MODIS), the United States Geological Survey (USGS) (e.g., Landsat), and the European Space Agency (ESA) (e.g., data from the Sentinel constellation) [29]. Recently, Google (Mountain View, Cal., USA) developed the Google Earth Engine (GEE) [30], a cloud computing platform specifically designed for geospatial analysis at the petabyte scale. The GEE data catalog is composed by widely used geospatial data sets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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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 (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 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 (R 2 = 0.42 and R 2 = 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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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“…For (3) and (4), we used national scale datasets of agricultural productivity and prices combined with global-scale geographic datasets of 2010 urban area, population, and meteorological information. We aggregated and summarized the data in Google Earth Engine (hereafter Earth Engine), a cloud-based geospatial processing platform for analysis of planetary scale geospatial data (Gorelick et al, 2017). This paper is not an estimate of actual UA production, though we do estimate services from existing vegetation, which may or may not be agriculture.…”

Section: Introductionmentioning

confidence: 99%

A Global Geospatial Ecosystem Services Estimate of Urban Agriculture

et al. 2018

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Though urban agriculture (UA), defined here as growing of crops in cities, is increasing in popularity and importance globally, little is known about the aggregate benefits of such natural capital in built‐up areas. Here, we introduce a quantitative framework to assess global aggregate ecosystem services from existing vegetation in cities and an intensive UA adoption scenario based on data‐driven estimates of urban morphology and vacant land. We analyzed global population, urban, meteorological, terrain, and Food and Agriculture Organization (FAO) datasets in Google Earth Engine to derive global scale estimates, aggregated by country, of services provided by UA. We estimate the value of four ecosystem services provided by existing vegetation in urban areas to be on the order of $33 billion annually. We project potential annual food production of 100–180 million tonnes, energy savings ranging from 14 to 15 billion kilowatt hours, nitrogen sequestration between 100,000 and 170,000 tonnes, and avoided storm water runoff between 45 and 57 billion cubic meters annually. In addition, we estimate that food production, nitrogen fixation, energy savings, pollination, climate regulation, soil formation and biological control of pests could be worth as much as $80–160 billion annually in a scenario of intense UA implementation. Our results demonstrate significant country‐to‐country variability in UA‐derived ecosystem services and reduction of food insecurity. These estimates represent the first effort to consistently quantify these incentives globally, and highlight the relative spatial importance of built environments to act as change agents that alleviate mounting concerns associated with global environmental change and unsustainable development.

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“…Fifteen features proposed by [51] and three features from [52] are derived. This selection fits with their function availability in the Google Earth Engine (GEE) [53]. Formulas of these features are shown in Tables A1 and A2.…”

Section: Vegetation Index (Vi) Formulamentioning

confidence: 79%

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

et al. 2018

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Smallholder farmers cultivate more than 80% of the cropland area available in Africa. The intrinsic characteristics of such farms include complex crop-planting patterns, and small fields that are vaguely delineated. These characteristics pose challenges to mapping crops and fields from space. In this study, we evaluate the use of a cloud-based multi-temporal ensemble classifier to map smallholder farming systems in a case study for southern Mali. The ensemble combines a selection of spatial and spectral features derived from multi-spectral Worldview-2 images, field data, and five machine learning classifiers to produce a map of the most prevalent crops in our study area. Different ensemble sizes were evaluated using two combination rules, namely majority voting and weighted majority voting. Both strategies outperform any of the tested single classifiers. The ensemble based on the weighted majority voting strategy obtained the higher overall accuracy (75.9%). This means an accuracy improvement of 4.65% in comparison with the average overall accuracy of the best individual classifier tested in this study. The maximum ensemble accuracy is reached with 75 classifiers in the ensemble. This indicates that the addition of more classifiers does not help to continuously improve classification results. Our results demonstrate the potential of ensemble classifiers to map crops grown by West African smallholders. The use of ensembles demands high computational capability, but the increasing availability of cloud computing solutions allows their efficient implementation and even opens the door to the data processing needs of local organizations.

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Google Earth Engine: Planetary-scale geospatial analysis for everyone

Cited by 8,075 publications

References 19 publications

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Global Estimation of Biophysical Variables from Google Earth Engine Platform

A Global Geospatial Ecosystem Services Estimate of Urban Agriculture

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

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