Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar

no code implementations • 14 Apr 2023 • Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon, Huy Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie

The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps.

Self-Supervised Learning

Building Detection from Satellite Images on a Global Scale

no code implementations • 27 Jul 2017 • Amy Zhang, Xian-Ming Liu, Andreas Gros, Tobias Tiecke

Our work is some of the first to create population density maps from building detection on a large scale.

Density Estimation

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation

no code implementations • 8 Dec 2016 • Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang

Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling is extremely expensive and subjective.

Semantic Segmentation Weakly-supervised Learning