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1. developers.google.com › earth-engine › datasetsOpenLandMap Precipitation Monthly | Earth Engine Data ...

   developers.google.com › earth-engine › datasets

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   Open in Code Editor. Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2018, IMERG, CHELSA Climate, and WorldClim.

2. developers.google.com › machine-learning › clusteringImplement k-Means Clustering | Machine Learning | Google for ...

   developers.google.com › machine-learning › clustering

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   2022年7月18日 · The TensorFlow API lets you scale k-means to large datasets by providing the following functionality: Clustering using mini-batches instead of the full dataset. Choosing more optimal initial...

3. developers.google.com › earth-engine › datasetsEarth Engine Data Catalog | Google for Developers

   developers.google.com › earth-engine › datasets

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   Digital Elevation Models (DEMs) describe the shape of Earth’s terrain. The Earth Engine data catalog contains several global DEMs such as Shuttle Radar Topography Mission (SRTM) data at 30-meter resolution, regional DEMs at higher resolutions, and derived products such as the WWF's HydroSHEDS hydrology database. Explore elevation data.

4. developers.google.com › machine-learning › clusteringk-Means Advantages and Disadvantages | Machine Learning | ...

   developers.google.com › machine-learning › clustering

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   2022年7月18日 · Clustering data of varying sizes and density. k-means has trouble clustering data where clusters are of varying sizes and density. To cluster such data, you need to generalize k-means as described in the Advantages section. Clustering outliers. Centroids can be dragged by outliers, or outliers might get their own cluster instead of being ignored.

5. developers.google.com › machine-learning › clusteringRun the Clustering Algorithm | Machine Learning | Google for ...

   developers.google.com › machine-learning › clustering

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   • Step One
   • Step Three
   • Step Four

   The algorithm randomly chooses a centroid for each cluster. In our example, wechoose a kof 3, and therefore the algorithm randomly picks 3 centroids.

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   For every cluster, the algorithm recomputes the centroid by taking the averageof all points in the cluster. The changes in centroids are shown in Figure 3 byarrows. Since the centroids change, the algorithm then re-assigns the points tothe closest centroid. Figure 4 shows the new clusters after re-assignment.

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   The algorithm repeats the calculation of centroids and assignment of pointsuntil points stop changing clusters. When clustering large datasets, you stopthe algorithm before reaching convergence, using other criteria instead. You do not need to understand the math behind k-means for this course. However,if you are curious, see below for the mathemat...

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6. developers.google.com › earth-engine › datasetsMOD13A2.061 Terra Vegetation Indices 16-Day Global 1km

   developers.google.com › earth-engine › datasets

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   The MOD13A2 V6.1 product provides two Vegetation Indices (VI): the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). The NDVI is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The EVI has improved sensitivity …

7. codelabs.developers.google.com › codelabs › mdc-102-webMDC-102 Web: Material Structure and Layout (Web)

   codelabs.developers.google.com › codelabs › mdc-102-web

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   2020年10月11日 · Created by a team of engineers and UX designers at Google, MDC features dozens of beautiful and functional UI components and is available for Android, iOS, web and Flutter.material.io/develop. In codelab MDC-101, you used two Material Components (MDC) to build a login page UI: the text field and button. Now let's expand upon this by adding ...