AAAI 2026technical0 citations
Time2Agri: Temporal Pretext Tasks for Agricultural Monitoring
Moti Rattan Gupta, Anupam Sobti
Abstract
Self Supervised Learning (SSL) has emerged as a prominent paradigm for label-efficient learning, and has been widely utilized by remote sensing foundation models (RSFMs). Recent RSFMs including SatMAE and DoFA primarily rely on masked autoencoding (MAE), contrastive learning or some combination of them. However, these pretext tasks often overlook the unique temporal characteristics of agricultural landscape, namely nature
BibTeX
@inproceedings{aaai2026_time2agritempora,
title = {Time2Agri: Temporal Pretext Tasks for Agricultural Monitoring},
author = {Moti Rattan Gupta and Anupam Sobti},
booktitle = {AAAI 2026},
year = {2026}
}