SUPERVISED IMAGE CLASSIFICATION IN GOOGLE EARTH ENGINE

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Supervised image classification in Google Earth Engine is a powerful process that involves using machine learning algorithms to categorize pixels in satellite or aerial imagery into predefined land cover classes. It is a fundamental technique in remote sensing and geospatial analysis. 

Here's a brief description of supervised image classification in Google Earth Engine:

  1. Data Acquisition: The process begins by selecting and acquiring the satellite or aerial imagery that covers the area of interest (AOI) for classification. Google Earth Engine provides access to an extensive collection of remote sensing data, including Landsat, Sentinel, and more.
  2. Area of Interest (AOI) Definition: The user defines the AOI, which is the geographic region where the classification will be applied. This can be done by drawing a boundary on the map or importing a shapefile.
  3. Training Data Collection:Training data is essential for supervised classification. It involves selecting sample points or polygons within the AOI and assigning them to specific land cover classes (e.g., water, forest, urban). These points serve as the ground truth for the model.
  4. Data Preprocessing:The satellite image is preprocessed by clipping it to the AOI, selecting relevant spectral bands, and potentially masking clouds or other unwanted features. This ensures that the image data is clean and ready for classification.
  5. Model Selection: Choose a classification algorithm to train the model. Google Earth Engine provides various classifiers, including Random Forest, Support Vector Machine, and more. The choice of classifier depends on the specific project requirements and data characteristics.
  6. Training the Model: The selected model is trained using the training dataset, which includes both the image data and corresponding land cover labels. The model learns to identify spectral patterns associated with each land cover class.
  7. Image Classification: The trained model is applied to the entire satellite image, classifying each pixel into one of the predefined land cover classes. The resulting classified image represents the spatial distribution of land cover within the AOI.
  8. Accuracy Assessment: To evaluate the accuracy of the classification, a separate testing dataset can be used. The model is applied to this dataset, and the results are compared to the ground truth data to calculate accuracy metrics.
  9. Visualization: The classified image is visualized in Google Earth Engine, allowing users to explore the land cover distribution. Visualization can be customized to display different land cover classes with distinct colors.
  10. Export and Analysis: If the classification results meet the project's requirements, the classified image can be exported for further analysis and integration into GIS (Geographic Information System) software. It can be used for land use planning, environmental monitoring, and various applications.

Supervised image classification in Google Earth Engine is a valuable tool for land cover mapping, environmental monitoring, and geospatial analysis. It leverages machine learning algorithms to automate the process of land cover classification, providing insights into land use changes and landscape dynamics.

The map shows the landuse landcover classification of serampore tehsil of hoogly district.



Google Earth Engine code for LULC





LULC of serampore tehsil

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