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), spatio-temporal non-stationarity, 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. 

Update: The program for the workshop is now available here, and the workshop proceedings are now available here: https://openaccess.thecvf.com/WACV2025_workshops/GeoCV  

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

• Visual Question Answering in Remote Sensing

• Domain Adaptation and approaches to address out-of-distribution data

• Computer Vision for Fusion of Multi-Sensor, Multi-Temporal Remote Sensing

• Self, Weakly and Unsupervised Approaches for Geospatial Image Analysis

• Image Super-Resolution of Geospatial Imagery

• Uncertainty quantification and explainable machine learning in remote sensing

• Multispectral and Hyperspectral Remote Sensing

• SAR Remote Sensing

• Data Fusion

• Change and Anomaly Detection

• Harmonizing data from multiple sensors and scales

• Applications including ecological monitoring, precision agriculture, sustainable development goals, disaster mapping etc.

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

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 2025 Author Kit available on overleaf. Accepted regular research papers that are presented at the conference will be included in the WACV 2025 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. 

Authors of accepted extended abstracts are encouraged to submit a full-length paper corresponding to their extended abstract for consideration in a special issue in the IEEE GRSS Journal on Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) following the workshop. Authors of accepted full-length research papers may also submit extended versions of their manuscripts to this IEEE GRSS JSTARS special issue, as long as they have made significant additions to the Workshop paper. 

All manuscripts will be peer-reviewed in a double-blind format, following the WACV format.

Manuscript Submission

Both regular papers and abstracts should be submitted via Microsoft CMT.
CMT Link for Paper Submission: https://cmt3.research.microsoft.com/GEOCV2025 

Organizers

Important Dates

Deadline for Paper Submissions: November 30, 2024 December 10, 2024  
Paper Decisions Announced: December 30, 2024
Camera Ready Paper Submission: January 10, 2025
Workshop Date: March 4, 2025

Keynote Speakers

We are excited to announce our invited keynote speakers:

Workshop Program

Location: AZ Ballroom Salon 5

8.00am - 8.10am:     Welcome, Opening remarks

8.10am - 9.00am:      Keynote Talk (Levente Klein): Geospatial Foundation Models for Earth Observation

9.00am - 10.15am:    AM Poster Session and Coffee Break

10.15am - 11.05am:   Keynote  Talk (Salman Khan): Turning Earth Observations to Natural Language Dialogues

11.05am - 11.55 am:  Keynote Talk (Xiaoxiang Zhu): AI4EO Transforming Big Earth Data into Actionable Insights: What Works, What Doesn’t?

11.55am - 1.00pm:     Lunch Break

1.00pm - 1.50pm:      Keynote Talk (David Clausi): Computer Vision in Remote Sensing – Sea Ice Monitoring Via Scene Classification

1.50pm - 3.45pm:      PM Poster Session and Coffee Break

4.00pm - 4.50pm:    Keynote Talk (Zhuo Zheng):  Learning Change Representation from Single-Temporal Supervision

Workshop Proceedings: https://openaccess.thecvf.com/WACV2025_workshops/GeoCV 

Poster Schedule [P: Full Length Papers; A: Extended Abstracts]

AM Session 

• [P]  T. Shinohara, "Pre-training of Auto-generated Synthetic 3D Point Cloud Segmentation for Outdoor Scenes"

• [P] J. McMillen, Y. Yilmaz, "FuseForm: Multimodal Transformer for Semantic Segmentation"

• [P] H. Fang, H. Azizpour, "SITS-Extreme: Leveraging Satellite Image Time Series for Accurate Extreme Event Detection"

• [A] J. Pesonen, "Extending the Domain of Wildfire Smoke Segmentation with Diffusion-based Image Mixing"

• [A] A. Perez, B. Verdin, E. Harclerode, A. Mahalanobis, S. Prasad, "A Framework for Adapting Large Vision Models for SAR ATR"

• [A] S. Skakun, "The impact of map accuracy on area estimation with remote sensing data within the design-based inference framework"

• [A] H. Rakotonirina, T. Lohier, J. Baptiste, "Applying Gaussian Mixture Model to DEM Data for Geological Weathering Mapping"

• [A] M. Massey,  A. Imran , "Decoding Earth's Surface: The Role of Multimodal Large Language Models"

• [A] A. Saunders, J. Giezendanner, B. Tellman, "VIIRS Provides Continuity from MODIS for Deep-Learning Enabled Satellite Inundation Mapping"

• [A] J. Giezendanner, Q. Yang, D.S. Civitarese, J. Jakubik, A. Chandra; E. Schmitt, J. Vila; D. Hohl, C. Hill, C. Watson, S. Wang, "Combining Satellite Images and Local Weather Station Data for Accurate Hyper-Local Weather Forecasting at Arbitrary Locations"

• [A] L. Shumilo, "Novel Approach for Winter Cropland Expansion and Migration Assessment Using Neural Network Optical Flow Model"

• [A] S. Shah, S. Khan, O. Rehman, "SAM-Fields: Extracting Base and Functional Agriculture Field Boundaries from Satellite Imagery for small-holder farms in Pakistan"

PM Session

• [P] Ori Linial; George Leifman; Yochai Blau; Nadav Sherman; Yotam Gigi; Wojciech Sirko; Genady Beryozkin, "Enhancing Remote Sensing Representations Through Mixed-Modality Masked Autoencoding

• [P] Amrita Gupta*; Anthony Ortiz; Simone Fobi; Duncan Kebut; Seema Iyer; Rahul Dodhia; Juan Lavista-Ferres, "Mapping Refugee Camps with AI: A Benchmark Dataset and Baseline Models for Humanitarian Applications"

• [P] Juyeop Han; Guilherme Cavalheiro; Josef Biberstein; Elham Alkabawi; Shahad Alqhatni; Fadwa Alaskar; Eman Bin Khunayn; Sertac Karaman, "CaLiSa-NeRF: Neural Radiance Field with Pinhole Camera Images, LiDAR point clouds and Satellite Imagery for Urban Scene Representation"

• [P] Shunsuke Takao, "MD-Glow: Multi-task Despeckling Glow for SAR Image Enhancement"

• [P] Fabian Deuser; Wejdene Mansour; Hao Li; Konrad Habel; Martin Werner; Norbert Oswald, "Temporal Resilience in Geo-Localization: Adapting to the Continuous Evolution of Urban and Rural Environments" 

• [P] Martina Pastorino*; Gabriele Moser; Sebastiano B. Serpico; Josiane Zerubia, "Multiresolution Fusion and Classification of Hyperspectral and Panchromatic Remote Sensing Images"

• [P] Nicolas Houdré*; Diego Marcos; Dino Ienco; Laurent Wendling; Camille Kurtz; Sylvain Lobry, "ProMM-RS: Exploring Probabilistic learning for Multi-Modal Remote Sensing Image Representations"

• [P] Daniel Panangian*; Ksenia Bittner, "Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling"

• [P] Bianco Michael*; David Eigen; Michael Gormish, "Enhancing Worldwide Image Geolocation by Ensembling Satellite-Based Ground-Level Attribute Predictors"

• [P] Hongcheng Jiang*; ZhiQiang Chen, "Hyperspectral Pan-sharpening with Transformer-based Spectral Diffusion Priors"

• [P] Alexander Berian*; Daniel Brignac; JhihYang Wu; Natnael Daba; Abhijit Mahalanobis, "CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate Representation"

• [P] Aaron Perez *; Saurabh Prasad, "Layer Optimized Spatial Spectral Masked Autoencoder for Semantic Segmentation of Hyperspectral Imagery"

• [P] Mariya Jose; Stefan Auer; Jiaojiao Tian*, "Direction guided Segmentation and Vectorization of curbstones from high-resolution ortho-images" 

• [P] Nandini Saini*; Ashudeep   Dubey;  Debasis  Das;  Chiranjoy Chattopadhyay, "Advancing Open-Set Object Detection in Remote Sensing Using Multimodal Large Language Model"

• [P] Ciem Cornelissen*; Sam Leroux; Pieter Simoen, "Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data"

• [P] Rohit Kumar*; Tanishq Sharma; Vedanshi Vaghela; Sanjeev Jha; Akshay Agarwal, "PrecipFormer: Efficient Transformer for Precipitation Downscaling"

• [P] Seyed Mohamad Ali Tousi*; Ramy Farag; Jacket Demby's; Gbenga Omotara; John A. Lory; G. N. DeSouza, "A Zero-Shot Learning Approach for Ephemeral Gully Detection from Remote Sensing using Vision Language Models"