Updated deadline for Notification to Authors: November 13th, 2023
Geospatial Artificial Intelligence (GeoAI) integrates methods from spatial sciences (e.g., geographic information systems – GIS) and AI to enable knowledge extraction from big geospatial data. GeoAI is extensively applied in conjunction with Earth Observation (EO) data, which entails capturing information about the Earth’s surface using sensors mounted on e.g. satellites and in-situ instruments. By incorporating advancements from computer vision (CV) on EO data, GeoAI finds extensive applications in human dynamics, precision agriculture, disaster management, humanitarian assistance, and national security. Unlike traditional natural images used in CV benchmarks, EO data presents unique characteristics and challenges that include spatial & temporal awareness, data volumes & diversity, and multimodal reasoning. Importantly, appropriately addressing these challenges hold the potential for groundbreaking applications benefiting human and environmental well-being.
Outcomes targeted by this 1st Workshop on Computer Vision for Earth Observation (CV4EO) Applications at WACV’24 include promoting the exchange between computer vision researchers with experts from geoscience domains, as well as bridging the gap between computer vision base research with government agencies (problem owners), national laboratories (applied science) and industry (data providers and solution deployment) in the context of challenges and opportunities related to image understanding methods for EO applications. Since applications include humanitarian assistance, disaster response, precision agriculture, national security missions, environment monitoring, promoting the collaboration across all involved parties can greatly benefit the development of CV-enabled tools that can effectively inform decision making for such cases potentially having direct impact on lives and the environment.
The workshop aims to achieve the following goals:
- Promote Multidisciplinary Interaction: CV for EO is a multidisciplinary space with diverse data sources, application domains, and involved disciplines. This workshop will encourage cross-disciplinary interactions, fostering knowledge exchange and collaborative efforts.
- Address Challenges in Multimodal Data: Data from existing remote sensing modalities are heterogeneous and complementary in many aspects. Passive imagery sources vary significantly in number of channels (e.g., multispectral vs. hyperspectral data), while active imagery such as Synthetic Aperture Radar (SAR) include both amplitude and phase components. Variations in data representation formats are another challenge, as geospatial data are often represented in vector format and their rasterization for consumption by co-opted CV models lead to information loss. Given this variety of remote sensing modalities, the workshop will focus on strategies for learning while leveraging multiple EO data sources effectively. We invite discussions that address data challenges related to heterogeneity, spatial and temporal resolution, satellite view angle, data fusion and representation formats.
- Enable scaling of CV4EO applications: Current EO satellite constellations collect 100+TBs of data a day, and images can be billions of pixels large. While these volumes impose challenges for data management and require customization of model training pipelines, the volume and diversity of remote sensing archival data represent a great potential for a wide variety of applications and the development of large-scale models.
Benefit the Computer Vision Community: Tackling the data volumes and multimodal reasoning challenges in CV for EO also has broader implications for the CV community. Similar challenges are faced in domains such as biomedical image analysis. The workshop will explore how advancements in CV for EO can contribute to addressing these shared challenges.
- Explore Impactful Applications: The workshop will expose members of the CV community to the exciting and impactful applications within the EO domain. Decision-making processes in disaster response, national security, and environmental protection will be discussed, showcasing the real-world applications of CV4EO.
Foster Talent Formation and Recruitment: The workshop aims to attract and engage talents in CV for EO, as institutions conducting geospatial data analysis face fierce competition for CV and AI expertise. By introducing attendees to applied research opportunities and practical applications, the workshop will contribute to talent formation and recruitment efforts.