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 collection 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 what 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 such challenges 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 a time-series EO data).
In a longer term, users will want to be able to search broadly, interactively, fast and using different or even mixed modalities. For example, you want to search for a specific geospatial objects (e.g., critical infrastructure) and retrieve images from a satellite data collection, retrieve models from a database of existing models for such a task. Similarly, you want to search with an image for locations on Earth that have a certain similarity. You want to monitor and map broad areas to rapidly identify changes, for instance earthquakes and floods, to alert the people and guide rescue teams. Our workshop brings together the art of search engine construction with both 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 2023 is a half-day workshop in the 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023), 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=geosearch23.
Paper Submission Deadline (extended): September 17, 2023
Author Notification (updated): Octorber 06, 2023
Camera Ready Version: October 22, 2023
Workshop: November 13, 2023
(All submissions are due at 11:59 PM CET)
Technical University of Munich
Forschungszentrum Jülich and University of Iceland
University of Santiago de Compostela
Danfeng Hong, Aerospace Information Research Institute, CAS, China
Fabian Deuser, University of the Bundeswehr Munich, Germany
Filip Biljecki, National University of Singapore, Singapore
Francisco Argüello, University of Santiago de Compostela, Spain
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
Wenwen Li, Arizona State University, USA
Xuke Hu, German Aerospace Center, Germany
Yu Feng, Technical Universtiy of Munich, Germany