Embedded Files
GeoCV - The Second Workshop on Computer Vision for Geospatial Image Analysis
Geospatial Image Analysis is a rapidly developing field at the intersection of computer vision, machine learning, and geospatial imaging technologies. Images acquired from modern geospatial sensors (passive optical, such as multispectral and hyperspectral, as well as active sensing modalities such as LiDAR and Synthetic Aperture Radar) have the potential to support a wide array of applications, including environmental monitoring, climate science, urban modeling, disaster mitigation, etc. Although the potential of geospatial imagery to inform these applications is immense, there are unique challenges posed by the analysis of such imagery for scalable interpretation of geospatial imagery. These include (but are not limited to) imagery representing a multitude of channels and scales depending on the sensor and the sensing platform (e.g. from drones to satellites), limited ground-truth, exploitation of heterogenous multi-modal imagery (e.g. combining passive optical imagery with Synthetic Aperture Radar and LiDAR measurements), significant domain differences caused by differences in sensors, seasons and/or imaging conditions, etc.
This workshop will serve as a venue to foster collaborations and cross-fertilization of ideas between research groups in academia and the industry working in the areas of computer vision, machine learning, geospatial imaging and their applications to address pressing societal challenges. It will bring together leading researchers at the intersection of these areas working on both cutting-edge algorithmic approaches, and interesting and compelling applications of great societal interest. In addition to research contributions from the community, this workshop will also feature keynote talks from research leaders in industry and academia who are working at the forefront of computer vision for geospatial image analysis.
Call for Papers
Call for Papers
We invite authors to submit high-quality papers on computer vision and image analysis for geospatial imaging. 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 computer vision paradigms to compelling geospatial imaging applications.
Topics of interest include (but are not limited to):
Foundation models and large vision models in remote sensing
Domain generalization and approaches to address out-of-distribution data, including open-set domain adaptation
Agentic AI for spatial reasoning
Self, Weakly and Unsupervised Approaches for Geospatial Image Analysis
Uncertainty quantification and explainable machine learning in remote sensing
Leveraging vision foundation models and multimodal foundation models for tasks such as semantic
Segmentation, object detection, change and anomaly detection and image classification.
Multimodal intelligent perception in remote sensing and Earth Observation.
Applications including ecological monitoring, precision agriculture, sustainable development goals, disaster mapping etc.
Paper Format: We welcome the following types of papers:
Regular research papers: We welcome regular/full research papers at the intersection of computer vision and geospatial imaging and its applications. These papers are limited to 8 pages (additional pages allowed for references) and will follow the WACV conference format. Authors should follow the Author Guidelines and use the WACV 2026 Author Kit available here. Accepted regular research papers that are presented at the workshop will be included in the WACV 2026 Workshop proceedings.
Extended Abstracts: We also welcome extended abstracts that represent emerging and ongoing work at the intersection of computer vision and geospatial imaging and its applications. These must also be submitted through the same CMT system - Accepted abstracts will be presented at the workshop, but they will not be included in the workshop proceedings.
All manuscripts will be peer-reviewed in a double-blind format, following the WACV format.
Manuscript Submission
Manuscript Submission
Both regular papers and abstracts should be submitted via Microsoft CMT.
CMT Link for Paper Submission: https://cmt3.research.microsoft.com/GeoCV2026
Organizers
Organizers
Saurabh Prasad
University of Houston
Jocelyn Chanussot
INRIA
Claudia Paris
University of Twente
Biplab Banerjee
Indian Institute of Technology, Bombay
Danfeng Hong
Southeast University
Keynote Speakers
Keynote Speakers
Sara Beery
MIT CSAIL
Dr. Sara Beery is the Homer A. Burnell Career Development Professor in the MIT Faculty of Artificial Intelligence and Decision-Making. She received her PhD in Computing and Mathematical Sciences at Caltech, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including geospatial and temporal domain shift, learning from imperfect data, fine-grained categories, and long-tailed distributions. Her work has been recognized with a Schmidt Sciences AI2050 Early Career Fellowship, an NSF CAREER Grant, the Amori Doctoral Prize, an Amazon AI for Science Fellowship, a PIMCO Data Science Fellowship, and an NSF GRFP. She partners with industry, nongovernmental organizations, and government agencies to deploy her methods in the wild worldwide. She works to increase access to AI skills through interdisciplinary capacity building and education, and has founded the AI for Conservation slack community, serves as the Biodiversity Community Lead for Climate Change AI, founded and directs the Workshop on Computer Vision Methods for Ecology, and co-PIs the NSF/NSERC Global Center on AI and Biodiversity Change.
Nicolas Longépé
ESA, Φ-lab
Dr. Nicolas Longepe received the M.Eng. degree in Electronics and Communication Systems and the M.S. degree in Electronics from the National Institute of Applied Sciences (INSA), France, both in 2005. He earned his Ph.D. in 2008 from the University of Rennes I, France. From 2007 to 2010, he was with the Earth Observation Research Center at the Japan Aerospace Exploration Agency (JAXA), Japan. Between 2010 and 2020, he worked as a Research Engineer in the Radar Applications Division at Collecte Location Satellites (CLS), France, focusing on innovative SAR-based applications for environmental and natural resource management (including ocean, mangrove, land and forest cover, soil moisture, snow cover, and permafrost) as well as maritime security (such as oil spill monitoring, sea ice and iceberg detection, and ship detection/tracking). Since 2020, he has been with the Φ-lab at the European Space Agency (ESA), where he is particularly involved in the development of next-generation Earth Observation missions (e.g., Φ-sat) that integrate Artificial Intelligence deployed directly at the edge (onboard spacecraft). He also leads and supports the development of Geospatial Foundation Models for EO, enabling semantic reasoning and advanced AI-driven applications across EO data ecosystems.
Hannah Kerner
Arizona State University
Dr. Hannah Kerner is currently an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University. Her research focuses on advancing the foundations and applications of machine learning to foster a more sustainable, responsible, and fair future for all. In her lab, she is conducting research projects in machine learning for remote sensing, algorithmic bias, and machine learning theory. She is the AI/Machine Learning Lead for NASA Harvest and NASA Acres as well as Center Faculty for the ASU Center for Global Discovery and Conservation Science (GDCS). She was recognized on the Forbes 30 Under 30 list in Science in 2021.
Important Dates
Important Dates
Regular Papers (archived in WACV Workshop Proceedings):
Deadline for Paper Submissions: November 30, 2025 December 20, 2025
Paper Decisions Announced: December 30, 2025
Camera Ready Paper Submission: January 9, 2026
Workshop Date: 6-7 March, 2026
Extended Abstracts (not archived in WACV Workshop Proceedings):
Deadline for Extended Abstracts: January 9, 2026
Workshop Program
Workshop Program
Workshop program will be finalized after the peer review period
Past Workshops:
The program for the workshop is now available here, and the workshop proceedings are now available here: https://openaccess.thecvf.com/WACV2025_workshops/GeoCV
Google Sites
Report abuse