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.
Details to follow
Symposium date: September 22-23, 2022
Paper submission deadline: July 18, 2022 July 26, 2022
Notification of paper acceptance: August 18, 2022 August 25, 2022
Camera ready version: August 25, 2022 September 8, 2022
Proposal submission deadline: July 26, 2022
Notification of session acceptance: August 2, 2022
Organizers
Organizers
Organizers
Organizers
Organizers
We welcome short papers (6 pages) and vision papers (4 pages) on the following (or similar) topics:
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: Easychair Submission System.
We invite any person or team who is interested in Spatial Data Science to propose a session for SDSS 2022. Any activity that can fit into a 60-minute time slot is welcome. (Longer sessions may be proposed as a combination of multiple slots.)
In a maximal 2-page submission, please indicate:
University of Vienna and University of California, Santa Barbara
University of Glasgow, UK
Kitty CurrierUniversity of California, Santa Barbara, USA
Grant McKenzieMcGill University, Canada
Johannes ScholzGraz University of Technology, Austria
Shirly StephenUniversity of California, Santa Barbara, USA
Rui ZhuUniversity of Bristol, UK
Registration is free. Register via the AirMeet link below.
https://www.airmeet.com/e/c091b070-11f0-11ed-9710-f1f6739b8ca1