Geospatial information in the form of satellite imagery and its derived applications have wide reaching capabilites to optimise workflows and disrupt entire industries. The ability to capture geospatially accurate Very High Resolution (VHR) satellite images from anywhere on Earth gives analysts, CEOs, commanders, rescuers and researchers unparalleled insights and opens the doors to endless possibilities.
Browse the INCITE Library and download them all for FREE.
SAR imagery enables all-weather monitoring, penetrates dry soil, and offers resolution as high as 25 cm. Thanks to that, it’s invaluable for applications like emergency response, defence and intelligence, or agriculture. How does SAR work? What are its advantages and limitations? And what other data sources can you integrate it with? Read the article to learn more.
Satellite sensors captured unique architecture, breathtaking nature and centuries of history. Explore the Colloseum, La Sagrada Familia, the Leaning Tower of Pisa, and other landmarks.
Off Nadir Angle (ONA) plays a crucial role in the quality of optical satellite imagery. It influences its resolution and clarity, decides the visibility of features, and makes it easier or harder to identify objects. Moreover, ONA is used to create stereo imagery and 3D models of the Earth’s surface. Read on to learn more.
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.
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.
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.
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.