We propose a scheme that uses privileged information, in the form of keypoint annotations for the training data, to learn strong models from small and/or biased training sets.
We evaluate the damages on coconut crops caused by Typhoon goni using Senitnel-2 imagery. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study.
We propose a new, active deep learning method to estimate oil palm density at scale of from Sentinel-2 satellite images, and apply it to generate complete maps for Malaysia and Indonesia.
We exploit existing field guides to learn bird species recognition in large scale datasets for zero-shot recognition of unseen species
We propose a new method to count objects of specific categories, including oil palm trees, olive trees and cars. All of these objects are significantly smaller than the ground sampling distance of a satellite image.
We demonstrate the application of a machine learning technique called Generative Adversarial Networks (GAN) to learn models that can efficiently generate new, physically realistic realizations of the cosmic web
An example of using the in-built project page.