Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

Urban AI is increasingly embedded in the governance of security, mobility, and everyday life through sensors, cameras, and algorithmic systems promoted under smart city agendas. While these technologies promise efficiency and protection, their rapid normalisation raises tensions between security, democratic accountability and individual freedom. This commentary critically examines AI-enabled urban surveillance as a contemporary mode of statecraft, arguing that dominant safety narratives often obscure deeper processes of behavioural regulation, data extraction and power consolidation within urban infrastructures. Drawing on examples from both democratic and authoritarian contexts, it shows how AI-driven monitoring can reshape citizenship, amplify inequality through algorithmic bias and erode privacy and autonomy. To address these challenges, the study advances a multi-level analytical framework tracing how smart city paradigms materialise as surveillance infrastructures, generate social consequences, and produce democratic tensions that diverge into care-oriented or control-oriented governance pathways. The commentary concludes that safeguarding urban freedom requires treating urban AI governance as a political project grounded in transparency, participation, civil liberties and democratic oversight, rather than a purely technical exercise.

Deep learning models can accurately predict urban land surface temperature, but predictive performance does not ensure reliable estimates of interventions. This study evaluates the counterfactual reliability of TITAN-3D CR, a nine-channel deep learning model incorporating counterfactual consistency regularisation, using 668 held-out tiles across four Adelaide regions. Three findings expose important inferential limitations. First, conditioning on a post-treatment canopy-related covariate reduces the observed tree-reference temperature differential from 4.81°C to 2.3°C, whereas defensible pre-treatment adjustment reduces it by only 7.6%. Second, model-implied warming following tree removal varies from 3.25-5.39°C across defensible counterfactual constructions, with construction uncertainty approximately 14 times greater than sampling uncertainty. Third, despite 99.4% matched-scene constraint satisfaction, isolated synthetic interventions remain strongly seed dependent. These results show that counterfactual urban thermal modelling requires systematic evaluation of intervention construction, model stability, and uncertainty. Such evaluation is essential for developing trustworthy modelling tools to support urban heat adaptation decisions.

Urban heat is intensifying worldwide, yet many AI-driven planning tools focus primarily on spatial optimisation while overlooking the social, behavioural, and institutional dynamics that ultimately determine whether climate adaptation measures succeed or fail. This short communication argues that technical optimisation alone is an insufficient condition for effective urban heat adaptation. For interventions to be durable, legitimate, and widely adopted, they must align with societal behavioural readiness, institutional feasibility, and strong equity safeguards. We therefore advocate shifting from conventional risk-based approaches (centred on identifying where hazards are most severe) toward a readiness-based framework that prioritises where interventions are most socially and institutionally viable. Unlike traditional concepts of resilience or adaptive capacity, which often remain broad or descriptive, the proposed framework operationalises readiness through measurable behavioural indicators and institutional proxies. By embedding these dimensions within AI-enabled assessment tools, the framework translates contextual readiness into actionable intelligence while safeguarding the interests of minority and vulnerable populations. Drawing on behavioural science, institutional theory, and environmental justice, we reframe the core planning question from ‘Where is the heat most severe?’ to ‘Where is intervention most feasible, equitable, and inclusive?’ We outline an interdisciplinary research agenda integrating AI, governance, psychology, and rights-based planning to enable socially attuned, context-sensitive, and legally robust urban heat adaptation strategies.