Blog posts

Enhancing Geological Mapping through Inpainting of Surface Infrastructure Artefacts

4 minute read

Published: April 12, 2024

Remote sensing data such as aerial and satellite imagery serve as valuable tools for mapping geological features. However, these datasets often present statistical limitations by capturing only the surface of the Earth. This limitation becomes pronounced when regions of interest, such as economic mineralization or specific geological formations are studied mainly because surface infrastructure artefacts, including mines, roads, dams, and drill pads, inadvertently influence the signature within the imagery. Such non-geological artefacts interfere with machine learning models when focused on geological responses, necessitating the masking or removal of these artefacts.

Self-Supervised Learning on the Coasts of Antarctica: A Fun Project

4 minute read

Published: December 10, 2023

In the world of mining exploration and geological analysis, vast amounts of data often arrive devoid of labels, posing a challenge for conventional supervised learning methods. However, within this challenge lies an exciting opportunity - the utilization of unsupervised learning techniques to unveil hidden insights nestled within geological datasets. Among these methods, self-supervised learning emerges as a potent tool, reshaping how we extract valuable information and pinpoint potential mining sites.

Using Radiomics for Tree Bark Identification

16 minute read

Published: September 20, 2022

Using Radiomics for Tree Bark Identification

Since the term was first coined in $2012$, Radiomics has widely been used for medical image analysis. Radiomics refers to the automatic or semi-automatic extraction of a large of number of quantitative features from medical images. These features have been able to uncover characteristics that can differentiate tumoral tissue from normal tissue and tissue at different stages of cancer.

Selective Supervised Contrastive Learning with Noisy Labels

9 minute read

Published: May 10, 2022

Contrastive Learning is able to learn good latent representations that can be used to achieve high performance in downstream tasks. Supervised contrastive learning enhances the learned representations using supervised information. However, noisy supervised information corrupts the learned representations. In this blog post, I will summarize the paper published in CVPR 2022 that proposes an algorithm to learn high quality representations in existence of noisy supervised information. The title of this paper is Selective Supervised Contrastive Learning with Noisy Labels.