Welcome to my YouTube channel! Today, I'm excited to show you how to perform objectbased image classification using Google Earth Engine.

To get started, I defined my area of interest (AOI) in Lahore, Pakistan. This AOI serves as the focal point for our analysis, allowing us to zoom in on a specific geographic area and extract relevant imagery.

Next, I filtered the Sentinel2 image collection based on desired dates and cloud cover to ensure we're working with highquality data. This step is crucial for obtaining accurate classification results.

Then, I defined training polygons for different land cover classes within the AOI, such as bare soil, grass, garden, urban, and water. These polygons serve as the basis for training our classification model, providing labeled samples for the algorithm to learn from.

After extracting training data from the image using these polygons, I trained a Random Forest classifier using the Google Earth Engine platform. This supervised learning approach allows us to classify the image based on its spectral properties while considering spatial context.

Once the classifier was trained, I applied it to the image to generate a classified map, depicting different land cover classes within the AOI. To visualize the results, I created a colorcoded legend, making it easy to interpret the classification output.

Finally, I exported the classified image to Google Drive, enabling further analysis and sharing of the results. This workflow demonstrates the power of objectbased image classification and how it can be implemented using Google Earth Engine for remote sensing applications.

I hope you find this tutorial helpful and insightful. Don't forget to like, share, and subscribe for more tutorials on remote sensing, GIS, and geospatial analysis. Thanks for watching!