no code implementations • 30 Apr 2024 • Denys Godwin, Hanxi Li, Michael Cecil, Hamed Alemohammad
To address this issue, we compare the performance of a foundational Vision Transformer (ViT) model with a baseline Conditional Generative Adversarial Network (CGAN) model for missing value imputation in time series of multispectral satellite imagery.
1 code implementation • 28 Oct 2023 • Johannes Jakubik, Sujit Roy, C. E. Phillips, Paolo Fraccaro, Denys Godwin, Bianca Zadrozny, Daniela Szwarcman, Carlos Gomes, Gabby Nyirjesy, Blair Edwards, Daiki Kimura, Naomi Simumba, Linsong Chu, S. Karthik Mukkavilli, Devyani Lambhate, Kamal Das, Ranjini Bangalore, Dario Oliveira, Michal Muszynski, Kumar Ankur, Muthukumaran Ramasubramanian, Iksha Gurung, Sam Khallaghi, Hanxi, Li, Michael Cecil, Maryam Ahmadi, Fatemeh Kordi, Hamed Alemohammad, Manil Maskey, Raghu Ganti, Kommy Weldemariam, Rahul Ramachandran
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
1 code implementation • NeurIPS 2023 • Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan David Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Andrew Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
no code implementations • 1 Dec 2021 • Alexandre Lacoste, Evan David Sherwin, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Alex Chang, Mehmet Gunturkun, Alexandre Drouin, Pau Rodriguez, David Vazquez
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
no code implementations • 5 Dec 2020 • Hamed Alemohammad, Kevin Booth
Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals.
no code implementations • 5 Dec 2020 • Tharun Mohandoss, Aditya Kulkarni, Daniel Northrup, Ernest Mwebaze, Hamed Alemohammad
Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications.
no code implementations • 5 Dec 2020 • Aditya Kulkarni, Tharun Mohandoss, Daniel Northrup, Ernest Mwebaze, Hamed Alemohammad
The generalization property of CNN is poor for satellite imagery because the data can be very diverse in terms of landscape types, image resolutions, and scarcity of labels for different geographies and seasons.
no code implementations • 23 Apr 2020 • Yannis Kalantidis, Laura Sevilla-Lara, Ernest Mwebaze, Dina Machuve, Hamed Alemohammad, David Guerena
The workshop was held in conjunction with the International Conference on Learning Representations (ICLR) 2020.