Deep Learning

Mapping Oil Palm Density at Country Scale: an Active Learning Approach / in review

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.

Privileged Pooling: Better Sample Efficiency Through Supervised Attention / in review

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.

Counting the uncountable: deep semantic density estimation from space

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.

Fast cosmic web simulations with generative adversarial networks

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

Example Project

An example of using the in-built project page.