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 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 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
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