Speaker
Description
In an era of global challenges and crises, multi-spatial and multi-temporal data have become essential for the effective monitoring of urban change and the evaluation of policy success across administrative boundaries. However, data monopolies—wherein commercial entities and selected institutions exercise exclusive control over access to critical geospatial datasets—impede transparency, public scrutiny, and equitable decision-making processes. Furthermore, while artificial intelligence (AI) has enhanced data analysis capabilities, it has also made it more opaque. Employing non-transparent and selective AI models, trained on proprietary and non-open datasets, risks introducing hidden biases into urban decision-making processes. This risk is especially acute in developing regions.
To counter this, European open data policies, such as the Open Data Directive (2019/1024) and the European Data Strategy, present a significant opportunity. By mandating the free availability of high-quality datasets (including Earth observation and geospatial data), these policies pave the way for transparent, peer-reviewed spatial research. Key institutions such as the European Space Agency (ESA), the Copernicus Programme, the European Data Portal and the Joint Research Centre (JRC) are at the forefront of this initiative, ensuring that urban analysis remains open, replicable, and publicly accountable.
In this context, this paper explores how AI-driven remote sensing technologies, open data policies, and peer-reviewed urban analytics can break through these monopolies and offer a more just and accountable approach to urban planning, i.e. an approach that prioritises equitable access to resources, transparency in decision-making, and inclusivity in urban governance. Crucially, this work challenges historical representations of urban form, often subjective, politically motivated, and shaped by selective narratives. In contrast to maps, master plans, and administrative definitions of cities, which are heavily influenced by power dynamics, AI-driven empirical geospatial data offers a more neutral, evidence-based approach to urban research. By treating geospatial data as a public good and ensuring rigorous peer review, this paper advocates for collaborative, transnational governance of urban analytics—embedding equity, accountability, and transparency as core principles of 21st-century urban planning.
| Keywords | Open data; urban analytics; transparency; data monopolies; peer-review |
|---|---|
| Best Congress Paper Award | No |
