Speakers
Description
Research in urban sustainability, climate adaptation, and resilience increasingly relies on the integrating of heterogenous data sets, including Earth observation (EO) products, fieldwork and surveys, administrative records, crowdsourcing data, and qualitative stakeholder knowledge. Advances in EO, artificial intelligence (AI) and digital urban technologies have significantly expanded the urban data availability. Yet a central challenge remains: how can heterogenous urban data be transformed into of developing actionable, trustworthy, and just knowledge systems remains that support decision-making under uncertainty?
Drawing on research from the Development of Urban and Rural Regions group – including climate adaptation modelling in Jakarta and Mumbai, adaptation research in Manila, Hue and Ho Chi Minh City, crowdsourcing-based disaster resilience assessment in Germany, as well as Urban Transformative Readiness Scorecard approach, this poster identifies three interconnected challenges.
First, interoperable urban knowledge systems: Urban climate and sustainability research needs to move beyond harmonising heterogeneous datasets towards systems that connect environmental, social, institutional and governance information across spatial and temporal scales. This requires FAIR data principles, semantic interoperability, AI-assisted analysis and explicit treatment of uncertainty. Such systems must also recognise that urban processes are shaped by interactions across scales: broader environmental, regional and global dynamics influence cities, but are mediated by local contexts, histories, institutions and practices. Interoperability should therefore preserve the specificity of place rather than treat local context as secondary. It must also bridge different data-generation methods, from remote sensing and administrative records to social sensing, while accounting for how these sources capture both local conditions and wider-scale influences.
Second, co-produced and just urban knowledge: Qualitative, participatory and community-generated knowledge remains poorly represented in conventional urban data infrastructures, despite its importance for understanding lived experience, local priorities and adaptive capacities. Interviews, participatory mapping, citizen science, and other locally generated data can reveal contextual conditions that quantitative datasets overlook. Their integration, however, raises challenges around privacy, consent, anonymisation, metadata, ownership, interpretability and responsible reuse across formats such as text, audio, images and participatory maps.
Third, data politics, power and governance: Urban data systems are shaped not only by technical choices but also by institutional and political interests. As AI, digital twins and platform-based approaches become more influential, questions arise over who controls data, sets the rules for their use, and is accountable for resulting decisions. Data silos and gatekeeping can protect institutional privileges, restrict scrutiny and influence which evidence enters decision-making. Algorithmic systems may further redistribute authority through opaque classifications and automated decisions. Building trustworthy urban knowledge systems therefore requires attention to data sovereignty, institutional accountability, transparency and the power relations surrounding the production and use of urban knowledge.
We argue that great data for urban sustainability requires more than larger or technically interoperable datasets. The challenge is to develop urban knowledge systems that are technically compatible, context-sensitive, ethically governed, and capable of incorporating various forms of knowledge. This poster will discuss these issues with the support of case studies across different geographies and scales to demonstrate the advances and especially the remaining challenges in the field.