GIS4IoRT

GIS4IoRT: Bridging the Gap Between Robots and Maps

The GIS4IoRT project is a research and innovation initiative focused on developing a “plug-and-play” middleware to integrate data from the Internet of Robotic Things (IoRT) with Geographic Information Systems (GIS). By leveraging cloud-based infrastructure and advanced AI, the project aims to solve the interoperability challenges that currently prevent robot sensor data from being easily visualized and analyzed within standard mapping environments. Funded through the CHIST-ERA program, the project facilitates the seamless handling of heterogeneous, multi-modal, and multi-dimensional datasets to support smarter decision-making in sectors like agriculture and urban planning.

GIS4IoRT Project Website

At its core, GIS4IoRT focuses on building a technical bridge between autonomous machinery and GIS software. The project is developing a dedicated QGIS plugin that serves as a user-friendly interface, allowing users to browse real-time robot data and perform complex spatio-temporal queries. Key technological pillars include AI-based approaches to ensure data reliability and a cloud-native architecture capable of processing diverse data types—including Lidar, multi-modal imagery, robot trajectories, and record-like sensor data. These tools are designed to transform raw robotic output into actionable geographic insights without requiring specialized technical expertise.

A key feature of the project is its validation through real-world use cases, particularly in the field of precision agriculture. By testing the middleware in agricultural settings, the project demonstrates how robotic sensors can monitor crop health or environmental conditions and immediately sync that data with spatial analysis tools. This practical approach ensures that the “plug-and-play” middleware is robust enough to handle the complexity of dynamic spatio-temporal data and the pattern evolution typical of real-world robotic deployments.

By democratizing access to robotic data and simplifying its integration into GIS, GIS4IoRT seeks to advance the methodological framework for next-generation Geographic Information Systems. The project challenges existing research paradigms by addressing data wrangling, reliability, and the interpretability of analytical results, ultimately paving the way for more effective spatio-temporal analysis across various industrial and scientific domains.

My role

In the GIS4IoRT project, I define query patterns at the intersection of GIS and IoRT by translating robotic movement and sensor data into spatial queries, including trajectory analysis, real-time proximity alerts, and historical spatio-temporal trends.

Factsheet