We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks.
We propose an end-to-end architecture for depth estimation that uses geospatial context to infer a synthetic ground-level depth map from a co-located overhead image, then fuses it inside of an encoder/decoder style segmentation network.
Direct methods, such as PoseNet, regress pose from the image as a fixed function, for example using a feed-forward convolutional network.
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification.
A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data.
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females.
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice.
Looking at the world from above, it is possible to estimate many properties of a given location, including the type of land cover and the expected land use.