Primary Convener:
Yuchi Ma, Stanford University

Convener:
Sherrie Wang, Massachusetts Institute of Technology
Hannah Kerner, Arizona State University
Shuo Chen, Purdue University

Early Career Convener:
Siddharth Sachdeva, Organization Not Listed

Chair:
Yuchi Ma, University of Wisconsin Madison
Sherrie Wang, Stanford University
Hannah Kerner, Arizona State University
Shuo Chen, Purdue University
Siddharth Sachdeva, Organization Not Listed

Artificial Intelligence (AI) has proven powerful for utilizing the increasing amounts of remote sensing (RS) data for applications such as landcover mapping, crop yield prediction, and water resources management. However, many AI models require a substantial amount of ground truth labels for training. Collecting these labels often involves pixel-wise image labeling or in-field sample collection, which can demand significant labor and financial resources. Due to the distribution shift and spatial heterogeneity among regions, models trained in one region often demonstrate poor performance when applied to other regions. Therefore, it is important to develop approaches to address labeled data scarcity and effectively train AI models for RS applications. This session welcomes studies and applications of transfer learning, self-supervised learning, and foundation models on RS that improve performance under domain shift or limited data, domain knowledge-guided methods to reduce the need for real-world data samples, and benchmark datasets in RS.

Index Terms
0480 Remote sensing
1906 Computational models, algorithms
1926 Geospatial
1942 Machine learning

Cross-Listed:
SY - Science and Society
B - Biogeosciences
EP - Earth and Planetary Surface Processes

Suggested Itineraries:
Machine Learning and AI

Neighborhoods:
1. Science Nexus