SDSS 2022: 3rd Spatial Data Science Symposium Virtual September 22-23, 2022 |
Conference website | http://sdss2022.spatial-data-science.net/ |
Submission link | https://easychair.org/conferences/?conf=sdss20220 |
Submission deadline | July 26, 2022 |
Spatial and temporal thinking is important not just because everything happens at some places and at some time, but because knowing where and when things are happening is key to understanding how and why they happened or will happen. Spatial data science is concerned with the representation, modeling, and simulation of spatial processes, as well as with the publication, retrieval, reuse, integration, and analysis of such space- and place-centric data. It generalizes and unifies research from fields such as geographic information science/geoinformatics, geo/spatial statistics, remote sensing, environmental studies, and transportation studies, and fosters applications of methods developed in these fields in other disciplines ranging from social to biological and physical sciences.
Data-driven methods, such as machine learning models, have been attracting attention from the Geoscience community for the past several years. For instance, they have been successfully used to quantify the semantics of place types, to classify geo-tagged images, to predict traffic and air quality, to improve resolution of remotely sensed images, to recognize objects in such imagery, to predict and compare trajectories, to name but a few. Geospatial observations may be vague, uncertain, heterogeneous, dependent on other nearby observations, and multimodal; thus, spatial and temporal principles should be included in techniques such as deep neural networks. Unsurprisingly, research has shown that by doing so, we can substantially outcompete more general (non-spatial) models when applied to geo-data or applications with a spatial and temporal component.
To keep this discussion alive and help the community to exchange ideas and lessons learned about spatial and temporal aspects of Data Science, we are hosting the 3rd Spatial Data Science Symposium (SDSS 2022) as a distributed virtual meeting. The symposium aims to bring together researchers from both academia and industry to discuss experiences, insights, methodologies, and applications, taking spatial and temporal knowledge into account while addressing their domain-specific problems. The format of this symposium will be a combination of keynotes, scientific sessions, as well as paper presentations. In contrast to classical conferences, the community will decide on those sessions, and the main focus will be on interaction. Hence, we welcome submissions for both papers and sessions (see below). SDSS 2022 will be a distributed symposium in a sense that while the event as such will be online, we will host (and help others to host) individual get-togethers to jointly experience the symposium in person.
Submission Guidelines
We welcome short papers (6 pages) and vision papers (4 pages). All submissions must be original and must not be simultaneously submitted to another journal or conference/workshop. All submissions must be in English. Proceedings of the symposium will be publicly available at well-established UC eScholarship and each accepted paper will be assigned an individual DOI. All papers must be formatted according to LNCS templates. Submissions will be peer-reviewed by the Program Committee. Papers must be submitted via EasyChair:
- Short papers (6-page) describe your most recent work where spatial and temporal thinking plays roles and discuss their roles.
- Vision papers (4-page) describe your vision of the role spatial and temporal thinking plays in data-driven appraoches in GISicence and geography in general.
We are contacting journals to potentially organize a special issue following this event. Selected papers will be invited to submit an extended version to the journal. More details will be announced soon.
List of Topics
- Geospatial thinking in the arts
- Spatial and temporal knowledge representation and reasoning
- Geospatial artificial intelligence (GeoAI) & spatially explicit machine learning
- Neuro-symbolic representation learning for spatial and temporal data
- Spatial and temporal data mining
- Spatial and spatiotemporal data uncertainty
- Geographic information retrieval
- Geospatial knowledge graphs
- Geospatial semantics
- Spatial statistics / Geostatistics
- Geo-simulation
- Diversity, inclusion, and equity in spatial data science
- Geospatial applications that use data-driven methods, including but not limited to:
- Movement analysis
- Disaster response
- Environmental studies
- Geoprivacy
- Social sensing
- Location-based services
- Humanitarian relief
- Crime analysis
- Urban analytics
- …
Organizing Committees
General Chair:
Krzysztof Janowicz, University of Vienna, Austria and University of California, Santa Barbara, USA
Program Chairs:
- Ana Basiri, University of Glasgow, UK
- Kitty Currier, University of California, Santa Barbara, USA
- Grant McKenzie, Department of Geography, McGill University
- Johannes Scholz, Graz University of Technology, Austria
- Shirly Stephen, University of California, Santa Barbara, USA
- Rui Zhu, University of Bristol, UK
Program Committee:
TBD
Invited Speakers
- TBD
Publication
Proceedings of the symposium will be publicly available at eScholorship and each accepted paper will be assigned with an individual DOI. All papers must be formatted according to LNCS templates. Submissions will be peer-reviewed by the Program Committee.
Venue
The conference will be held in virually this year and welcome scientists from all the world.
Contact
All questions about submissions should be emailed to Rui Zhu (ruizhu@ucsb.edu) or Ling Cai (lingcai@ucsb.edu).
Sponsors
TBD