3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data
Atlanta, USA
October 29th, 2024

Mission

The collection of geospatial data has exploded due to the rapid change of Web 2.0, the Internet of Things (IoT), open science movement, rise of Earth Observation (EO) technologies and many more. Consequently, geospatial data is composed of complex and diverse format, modality, resolution, and size that continue to be harnessed with unprecedented velocity. All these facts pose a pressing need and challenge on how to effectively and efficiently search and mine large collections of geospatial data for interesting patterns. In this context, not only does one need to know where to look to find objects of interest but also which model to use for different searching tasks. What if prior efforts had already created models on an exact or very similar task? How should users search for such models? When models are available how should they be stored? Many applications become possible if we manage to make large data collections and models searchable by content, metadata, and analytic tasks. Application users would like to solve these challenges by knowing which model to use, which task is the model relevant for, and how to simultaneously search across all geospatial data representations (vector, raster, text, fields, point clouds, etc.), and finding all objects of a certain type in a huge data cube (e.g., a large point cloud or time-series EO data). In the long term, users will want to be able to search broadly, interactively, quickly and using different or even mixed modalities. For example, you might want to search for a specific geospatial object (e.g., critical infrastructure) and retrieve images from a satellite data collection, retrieve models from a database of existing models for such tasks. Similarly, you might want to search with an image for locations on Earth that have a certain similarity. You might want to monitor and map broad areas to rapidly identify changes, such as earthquakes and floods, to alert people and guide rescue teams. Our workshop brings together the art of search engine construction with geospatial data modeling, data processing, and management to provide a forum for researchers and practitioners interested in the general topic of GeoSearch.
Example topics include but are not limited to:

  • Multimedia Information Retrieval
  • Search in Large Spatio-temporal Data Cubes
  • Search in Multi-modality Data
  • Searching Geospatial Objects
  • Searching Vector Geometry
  • Searching Graphs and Networks
  • Searching 3D Point Clouds
  • Searching Spatial Trajectories
  • Search for Positioning, Localization and Navigation
  • Model Generalizability for Search
  • Raw Data Search in Remote Sensing (i.e. with clouds, top-of-atmosphere)
  • Spatial Representation Learning for Search
  • Metadata design and cataloguing of Machine Learning Models
  • Design of Data and Model Relational Structures
  • Keyword-Search in Spatial Data
  • Spatial Index Structures for Search
  • Spatio-temporal Search Interfaces
  • Scalable Search Frameworks and Implementations
  • Scalable Search Algorithms for different computing technologies (HPC, cloud computing, edge computing, quantum computing)
  • Data Representations and Containers facilitating content-based retrieval
  • Ontological Methods and Learning
  • Geospatial Knowledge Graphs for Search

Submission Format

The GeoSearch 2024 is a half-day workshop in the 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2024), which consists of keynotes and individual paper presentations. Two submission format will be included in this workshop:

  • Full research paper: 6-8 pages
  • Short research paper or demo paper: 4 pages

Full research papers should present mature research on a specific problem or topic in the context of geospatial search. We also welcome short research articles or industry demonstrations of existing or developing methods, toolkits, and best practices for AI applications in the geospatial domain.

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at http://www.acm.org/publications/proceedings-template . All submitted papers will be peer reviewed to ensure the quality and the clarity of the presented research work. Submissions will be single-blind — i.e., the names affiliations of the authors should be listed in the submitted version.

Submission system: https://easychair.org/my/conference?conf=geosearch24.

Key Dates

 

Paper Submission Deadline (extended): August 23, 2024

Author Notification (updated): September 16, 2024

Camera Ready Version: October 07, 2024

Workshop: October 29, 2024

(All submissions are due at 11:59 PM CET)

Workshop Chairs

 

Hao Li

Technical University of Munich

 

Abhishek Potnis

Oak Ridge National Laboratory

 

Wenwen Li

Arizona State University

 

Dalton Lunga

Oak Ridge National Laboratory

 

Martin Werner

Technical University of Munich

 

Andreas Züfle

Emory University

 

Program Committee

Danfeng Hong, Aerospace Information Research Institute, CAS, China
Fabian Deuser, University of the Bundeswehr Munich, Germany
Filip Biljecki, National University of Singapore, Singapore
Steffen Knoblauch, Heidelberg University, Germany
Gengchen Mai, University of Georgia, USA
Katharina Andreas, Technical Universtiy of Munich, Germany
Markus Götz, Karlsruhe Inst. of Technology, Germany
Morris Riedel, University of Iceland, Iceland
Pedram Ghamisi, HZDR & IARAI, Germany
Shawn Newsam, University of California, Merced,USA
Wei Huang, Tonji University, China
Xuke Hu, German Aerospace Center, Germany
Yu Feng, Technical Universtiy of Munich, Germany

Technical Support