SecureWatch

Instant access to the highest quality satellite imagery via web-browser or API.

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A new era in geospatial information

GLOBAL COVERAGE

Define areas of interest worldwide including more than 6,000 capitals and metropolitan areas in 30 cm resolution.

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Convenient pricing structure so that you pay only for what you use, view or download.

PREMIUM IMAGERY

Browse the highest quality imagery, view highly aesthetic mosaics or see change over time.

CLOUD ACCESS

Instantly stream and download the data you need, easily share with others or integrate into your existing workflow.

A SecureWatch subscription gives you instant access to the best satellite imagery and geospatial data via web browser or API integration; And you don’t have to be an imagery expert or have in-house tools.
SecureWatch is designed to make both fresh and archive imagery accessible to anyone who needs it, whether you’re concerned with a specific area or the entire globe.

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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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