Image Library

Billions of square kilometers worth of fresh and historical imagery with global coverage and daily updates, all wrapped into an easy web app, so you find what you need fast.

How To:

Find an Image and ID Number

  1. Use the search tools to find your image.

  2. Click the ⊕ “Additional Information” icon

  3. Copy / paste the image ID to your request form

Export an AOI Shapefile

    1. Use the Rectangle or Polygon tool to outline your AOI

    2. Click “Actions”

    3. Click “Download  Aera of Interest” and select “Shapefile”

    4. Upload file to your request form

Note: Imagery in the library is not displayed at full resolution.

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Architecture of ResNet34-UNet model

UNet architecture for semantic segmentation with ResNet34 as encoder or feature extraction part. ResNet34 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.

Architecture of VGG16-UNet model

UNet architecture for semantic segmentation with VGG16 as the encoder or feature extractor. VGG16 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.

Architecture of ResNet34-FCN model

In this model, ResNet34 is used for feature extraction and the FCN operation remains as is. The feature of ResNet architecture is exploited where just like VGG, as the number of filters double, the feature map size gets halved. This gives a similarity to VGG and ResNet architecture while supporting deeper architecture and addressing the issue of vanishing gradients while also being faster. The fully connected layer at the output of ResNet34 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.

Architecture of VGG16-FCN model

In this model, VGG16 is used for feature extraction which also performs the function of an encoder. The fully connected layer of the VGG16 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.

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