Dear colleagues

Please consider submitting a paper to the Special Issue "Spatio-Temporal Models and Geo-Technologies" of the ISPRS International Journal of Geo-Information (see motivations and topics below).

Deadline extended to June 30 2021

For more information and details, please see

Best regards

Dr. Geraldine Del Mondo
Dr. Peng Peng
Prof. Dr. Feng Lu
Prof. Dr. Jerome Gensel
Guest Editors

Over the past few years, several theoretical spatiotemporal models have been successively proposed for a better representation of geographical phenomena. From early GIS modelling approaches, a series of extensions have been suggested to integrate time within object-based, field-based and dual representations of geographical data, while a series of formal qualitative approaches have also contributed to more fundamental frameworks that support advanced spatial reasoning capabilities. Meanwhile, many successful environmental and urban GIS research studies have demonstrated that the temporal dimension can be implicitly integrated by different representation and analytical frameworks. This Special Issue is calling for innovative works that integrate the spatial and temporal dimensions within theoretical, formal and practical GIS solutions as well as urban and environmental applications that demonstrate a sound integration of the spatial and temporal dimensions. 

The topics of interest include, but are not limited to:

- Formal spatiotemporal reasoning;

- Spatiotemporal ontologies and standards;

- Spatiotemporal modelling approaches;

- Graph-based representations to space and time phenomena;

- Qualitative spatial and temporal reasoning;

- Multiscale modelling;

- Combination of qualitative and quantitative approaches;

- Spatiotemporal query languages within GIS;

- Spatiotemporal GIS interfaces;

- Temporal indoor GIS;

- Location-based time GIS;

- Real-time GIS;

- Temporal web and wireless GIS;

- Spatiotemporal technologies;

- Spatiotemporal urban and environmental GIS;

- Spatiotemporal semantic web;

- Spatiotemporal data mining.