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Cloud Score+ S2_HARMONIZED V1
Cloud Score+ is a quality assessment (QA) processor for medium-to-high resolution optical satellite imagery. The Cloud Score+ S2_HARMONIZED dataset is being operationally produced from the harmonized Sentinel-2 L1C collection, and Cloud Score+ outputs can be used to identify relatively clear pixels and effectively remove clouds … cloud google satellite-imagery sentinel2-derived -
Dynamic World V1
Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes. Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days … global google landcover landuse landuse-landcover nrt -
Google Global Landsat-based CCDC Segments (1999-2019)
This collection contains precomputed results from running the Continuous Change Detection and Classification (CCDC) algorithm on 20 years of Landsat surface reflectance data. CCDC is a break-point finding algorithm that uses harmonic fitting with a dynamic RMSE threshold to detect breakpoints in time-series data. The … change-detection google landcover landsat-derived landuse landuse-landcover -
JRC Global Surface Water Mapping Layers, v1.2 [deprecated]
This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2019 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its … geophysical google jrc landsat-derived surface surface-ground-water -
JRC Global Surface Water Mapping Layers, v1.4
This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its … change-detection geophysical google jrc landsat-derived surface -
JRC Global Surface Water Metadata, v1.4
This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its … geophysical google jrc landsat-derived surface surface-ground-water -
JRC Monthly Water History, v1.4
This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its … geophysical google history jrc landsat-derived monthly -
JRC Monthly Water Recurrence, v1.4
This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its … geophysical google history jrc landsat-derived monthly -
JRC Yearly Water Classification History, v1.4
This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its … annual geophysical google history jrc landsat-derived -
Murray Global Intertidal Change Classification
The Murray Global Intertidal Change Dataset contains global maps of tidal flat ecosystems produced via a supervised classification of 707,528 Landsat Archive images. Each pixel was classified into tidal flat, permanent water or other with reference to a globally distributed set of training data. The … coastal google intertidal landsat-derived murray surface-ground-water -
Murray Global Intertidal Change Data Mask
The Murray Global Intertidal Change Dataset contains global maps of tidal flat ecosystems produced via a supervised classification of 707,528 Landsat Archive images. Each pixel was classified into tidal flat, permanent water or other with reference to a globally distributed set of training data. The … coastal google intertidal landsat-derived murray surface-ground-water -
Murray Global Intertidal Change QA Pixel Count
The Murray Global Intertidal Change Dataset contains global maps of tidal flat ecosystems produced via a supervised classification of 707,528 Landsat Archive images. Each pixel was classified into tidal flat, permanent water or other with reference to a globally distributed set of training data. The … coastal google intertidal landsat-derived murray surface-ground-water -
Satellite Embedding V1
The Google Satellite Embedding dataset is a global, analysis-ready collection of learned geospatial embeddings. Each 10-meter pixel in this dataset is a 64-dimensional representation, or "embedding vector," that encodes temporal trajectories of surface conditions at and around that pixel as measured by various Earth observation … annual global google landsat-derived satellite-imagery sentinel1-derived -
WRI/Google DeepMind Global Drivers of Forest Loss 2001-2022 v1.0
This dataset maps the dominant driver of tree cover loss from 2001-2022 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected … agriculture deforestation forest forest-biomass google landandcarbon -
WRI/Google DeepMind Global Drivers of Forest Loss 2001-2023 v1.1
This dataset maps the dominant driver of tree cover loss from 2001-2023 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected … agriculture deforestation forest forest-biomass google landandcarbon -
WRI/Google DeepMind Global Drivers of Forest Loss 2001-2024 v1.2
This dataset maps the dominant driver of tree cover loss from 2001-2024 globally at 1 km resolution. Produced by the World Resources Institute (WRI) and Google DeepMind, the data were developed using a global neural network model (ResNet) trained on a set of samples collected … agriculture deforestation forest forest-biomass google landandcarbon
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Datasets tagged google in Earth Engine
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[[["\u003cp\u003eThis webpage showcases a diverse collection of Earth Engine datasets, including those focused on land cover, surface water, and change detection.\u003c/p\u003e\n"],["\u003cp\u003eMany of the datasets leverage Landsat and Sentinel-2 satellite imagery for analysis and insights.\u003c/p\u003e\n"],["\u003cp\u003eNotable datasets include Dynamic World for near-real-time land cover mapping and the JRC Global Surface Water datasets for monitoring water bodies.\u003c/p\u003e\n"],["\u003cp\u003eThe Murray Global Intertidal Change dataset offers valuable information on tidal flat ecosystems using Landsat imagery.\u003c/p\u003e\n"],["\u003cp\u003eSeveral Google-produced datasets are available, such as Cloud Score+ for image quality assessment and the Landsat-based CCDC Segments for change detection.\u003c/p\u003e\n"]]],["Cloud Score+ identifies clear pixels and removes clouds from Sentinel-2 imagery. Dynamic World provides near-real-time land use/land cover data for nine classes from Sentinel-2. Google's CCDC algorithm detects breakpoints in 20 years of Landsat data. JRC datasets map surface water's location, distribution, and change from 1984-2021 using Landsat data. The Murray dataset classifies tidal flat ecosystems globally using supervised classification of over 700,000 Landsat images.\n"],null,[]]
