Self-Healing Maps: protecting maps through automatic updating processes.
Grant number: DP170100153 | Funding period: 2017 - 2020
This project aims to expand our ability to automatically integrate real-time data in map databases of high integrity. Our ability to sense the environment in real-time dramatically exceeds our ability to automatically and reliably integrate mapped data. The quality assurance of map data is a lengthy process, leading either to outdated or low integrity maps. Emergency responders, traffic services and the public then act on map data that cause delays and disturbances. This project intends to deliver self-healing mechanisms inspired by the human immune system, which protect maps from erroneous or malicious data, and detect and correct inconsistencies.
Related publications (17)
Synchronising Spatial Metadata Records and Interfaces to Improve the Usability of Metadata Systems
Mohsen Kalantari, Syahrudin Syahrudin, Abbas Rajabifard, Hannah Hubbard
<jats:p>The spatial data infrastructure literature reveals significant gaps in metadata systems concerning their efficiency and ef..
RIM: a ray intersection model for the analysis of the between relationship of spatial objects in a 2D plane
Ivan Majic, Elham Naghizade, Stephan Winter, Martin Tomko
The term between is frequently used to describe spatial arrangements of objects where one described core object is positioned in t..
From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity
Elham Naghizade, Jeffrey Chan, Martin Tomko
The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. ..
Infrastructure-Independent Indoor Localization and Navigation
Stephan Winter, Martin Tomko, Maria Vasardani, Kai-Florian Richter, Kourosh Khoshelham, Mohsen Kalantari
In the absence of any global positioning infrastructure for indoor environments, research on supporting human indoor localization ..
Discovery of topological constraints on spatial object classes using an extended topological model
Stephan Winter, Ivan Majic, Elham Naghizade, Martin Tomko
In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attribu..