What Goes Where: Predicting Object Distributions from Above
This addresses the challenge of interpreting overhead imagery for applications like urban planning or environmental monitoring, but it is incremental as it builds on existing cross-view learning methods.
The paper tackles the problem of predicting object types and counts from overhead imagery using weakly supervised ground-level images, achieving a network that captures semantically meaningful features without manual annotations.
In this work, we propose a cross-view learning approach, in which images captured from a ground-level view are used as weakly supervised annotations for interpreting overhead imagery. The outcome is a convolutional neural network for overhead imagery that is capable of predicting the type and count of objects that are likely to be seen from a ground-level perspective. We demonstrate our approach on a large dataset of geotagged ground-level and overhead imagery and find that our network captures semantically meaningful features, despite being trained without manual annotations.