Numerous strides have been made at the intersection of computer vision, machine learning and remote sensing. Although remotely sensed data play a critical role in a wide array of applications such as environmental monitoring, climate science and urban modeling, they preset unique challenges for scalable interpretation. In recent years, foundation models are emerging as a powerful framework that can be adapted for a variety of downstream vision tasks. In the arena of remote sensing, prior work has been focused on task-specific models that are optimized for specific applications and downstream tasks at hand (e.g. land-cover mapping, target recognition, object detection etc. from specific sensors). There is a significant and emergent interest in developing and deploying task-agnostic generalized large vision and vision language models that can be tailored for a variety of downstream remote sensing tasks.

This workshop will feature keynotes and presentations at the cutting-edge of foundation models and large vision models for remote sensing - it will bring together researchers working on both foundation and large vision models and geospatial image analysis to address the nuances presented by using such emergent models for remotely sensed imagery (e.g. a multitude of sensors with different sensing characteristics/specifications, diverse imaging modalities, ranging from passive-optical multi/hyperspectral to active-imaging such as SAR and LiDAR; limited ground-reference data etc.). Our emphasis will range from large vision and foundation models that are showing promise in the computer vision community to foundation models that are pre-trained on large-quantities of earth-observation imagery - this workshop will provide a venue for the community to present works that push the envelope on adapting these models for effective inference of multi-sensor, multi-temporal, multi-scale earth observation imagery. 

Call for Papers

We invite authors to submit high-quality papers at the intersection of emerging vision models and remote sensing. Submitted manuscripts will be peer-reviewed, and refereed for originality, presentation, empirical results and overall quality. In addition to papers focused on algorithmic novelty, we also encourage papers that demonstrate effective deployment of recent architectures to compelling geospatial imaging applications. 

Topics of interest include (but are not limited to):

• Foundation Models, Large Vision Language Models and Large Multi-Modal Models in Remote Sensing 

• Discriminative and Generative Models  

• Training of Large Vision Models (e.g. masked image modeling, new datasets, and benchmarks) 

• Deploying Large Vision Models for downstream tasks (e.g. segmentation, classification, regression, object detection, counting, change detection etc.) 

• Adaptation strategies, prompt tuning and visual instruction tuning 

• Few-shot and Continual learning 

• Open-set recognition and classification 

• Applications to multi-sensor and multi-temporal datasets 

Paper Submission: All submissions will be handled electronically through Microsoft CMT. The paper submission link will be available here soon. 

Paper Format: Papers are limited to 8 pages (additional pages allowed for references) and will follow the CVPR conference format. Authors should follow the Author Guidelines and use the CVPR 2025 Author Kit available here. Accepted research papers that are presented at the conference will be included in the CVPR 2025 Workshop proceedings.

Manuscript Submission

Manuscripts should be submitted via Microsoft CMT.
CMT Link for Paper Submission: https://cmt3.research.microsoft.com/MORSE2025/ 

Organizers

Important Dates

Deadline for Paper Submissions: March 5 9, 2025*
Paper Decisions Announced:  March 31, 2025
Camera Ready Paper Submission: April 7, 2025
Workshop Date: June 11 or June 12, 2025 (TBD)

*Late contributions may be considered, please contact the organizers if you are unable to meet the deadline.

Keynote Speakers

To be announced

Workshop Schedule

Schedule will be available after the peer review period.