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:
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 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.
Paper Submission Deadline: September 7, 2024 (updated)
Author Notification: September 20, 2024 (updated)
Camera Ready Version: October 07, 2024
Workshop: October 29, 2024
(All submissions are due at 11:59 PM CET)
Technical University of Munich
Oak Ridge National Laboratory
Arizona State University
Oak Ridge National Laboratory
Technical University of Munich
Emory University
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
Weilian Li, HafenCity University Hamburg, Germany
Xuke Hu, German Aerospace Center, Germany
Yu Feng, Technical Universtiy of Munich, Germany
Yuan Zhendong, Utrecht University, Netherlands