We introduce the {\em Atlas Urban Index} (AUI), a metric for measuring urban development computed using Sentinel-2 \citep{spoto2012sentinel2} satellite imagery. Existing approaches, such as the {\em Normalized Difference Built-up Index} (NDBI), often struggle to accurately capture urban development due to factors like atmospheric noise, seasonal variation, and cloud cover. These limitations hinder large-scale monitoring of human development and urbanization. To address these challenges, we propose an approach that leverages {\em Vision-Language Models }(VLMs) to provide a development score for regions. Specifically, we collect a time series of Sentinel-2 images for each region. Then, we further process the images within fixed time windows to get an image with minimal cloud cover, which serves as the representative image for that time window. To ensure consistent scoring, we adopt two strategies: (i) providing the VLM with a curated set of reference images representing different levels of urbanization, and (ii) supplying the most recent past image to both anchor temporal consistency and mitigate cloud-related noise in the current image. Together, these components enable AUI to overcome the challenges of traditional urbanization indices and produce more reliable and stable development scores. Our qualitative experiments on Bangalore suggest that AUI outperforms standard indices such as NDBI.

This paper presents a new approach to urban sustainability assessment through the use of Large Language Models (LLMs) to streamline the use of the ISO 37101 framework to automate and standardise the assessment of urban initiatives against the six "sustainability purposes" and twelve "issues" outlined in the standard. The methodology includes the development of a custom prompt based on the standard definitions and its application to two different datasets: 527 projects from the Paris Participatory Budget and 398 activities from the PROBONO Horizon 2020 project. The results show the effectiveness of LLMs in quickly and consistently categorising different urban initiatives according to sustainability criteria. The approach is particularly promising when it comes to breaking down silos in urban planning by providing a holistic view of the impact of projects. The paper discusses the advantages of this method over traditional human-led assessments, including significant time savings and improved consistency. However, it also points out the importance of human expertise in interpreting results and ethical considerations. This study hopefully can contribute to the growing body of work on AI applications in urban planning and provides a novel method for operationalising standardised sustainability frameworks in different urban contexts.

In urban planning, land use readjustment plays a pivotal role in aligning land use configurations with the current demands for sustainable urban development. However, present-day urban planning practices face two main issues. Firstly, land use decisions are predominantly dependent on human experts. Besides, while resident engagement in urban planning can promote urban sustainability and livability, it is challenging to reconcile the diverse interests of stakeholders. To address these challenges, we introduce a Consensus-based Multi-Agent Reinforcement Learning framework for real-world land use readjustment. This framework serves participatory urban planning, allowing diverse intelligent agents as stakeholder representatives to vote for preferred land use types. Within this framework, we propose a novel consensus mechanism in reward design to optimize land utilization through collective decision making. To abstract the structure of the complex urban system, the geographic information of cities is transformed into a spatial graph structure and then processed by graph neural networks. Comprehensive experiments on both traditional top-down planning and participatory planning methods from real-world communities indicate that our computational framework enhances global benefits and accommodates diverse interests, leading to improved satisfaction across different demographic groups. By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs and provides a robust platform for automating complex real-world urban planning processes.

Information and communication technologies are rapidly changing and transforming the citizens' urban life, culture, and habits. Today, cities are lively, active, productive, and innovative, but, at the same time, cities face many problems, such as high density, traffic, waste, water and air pollution, unplanned urbanization, etc. Public and local administrations have focused on finding solutions to these problems and developing new strategies. According to the United Nations' World Population Prospects 2022 most recent forecasts, the world population might reach 8.5 billion in 2030, 9.7 billion in 2050, and 10.4 billion in 2100. By 2050, it is estimated that 68% of the world's population will live in cities. There are many different definitions of smart cities.

Identifying the diverse and often competing values of citizens, and resolving the consequent public value conflicts, are of significant importance for inclusive and integrated urban development. Scholars have highlighted that relational, value-laden urban space gives rise to many diverse conflicts that vary both spatially and temporally. Although notions of public value conflicts have been conceived in theory, there are very few empirical studies that identify such values and their conflicts in urban space. Building on public value theory and using a case-study mixed-methods approach, this paper proposes a new approach to empirically investigate public value conflicts in urban space. Using unstructured participatory data of 4,528 citizen contributions from a Public Participation Geographic Information Systems in Hamburg, Germany, natural language processing and spatial clustering techniques are used to identify areas of potential value conflicts. Four expert workshops assess and interpret these quantitative findings. Integrating both quantitative and qualitative results, 19 general public values and a total of 9 archetypical conflicts are identified. On the basis of these results, this paper proposes a new conceptual tool of Public Value Spheres that extends the theoretical notion of public-value conflicts and helps to further account for the value-laden nature of urban space.

Comprehensive decision-strategy space exploration for efficient territorial planning strategies Olivier Billaud, 1, Maxence Soubeyrand, 1, Sandra Luque, 1 and Maxime Lenormand 1, † 1 TETIS, Univ Montpellier, AgroParisTech, Cirad, CNRS, Irstea, Montpellier, France Multi-Criteria Decision Analysis (MCDA) is a well-known decision support tool that can be used in a wide variety of contexts. It is particularly useful for territorial planning in situations where several actors with different, and sometimes contradictory, point of views have to take a decision regarding land use development. While the impact of the weights used to represent the relative importance of criteria has been widely studied in the recent literature, the impact of order weights determination have rarely been investigated. This paper presents a spatial sensitivity analysis to assess the impact of order weights determination in Multi-Criteria Analysis by Ordered Weighted Averaging. We propose a methodology based on an efficient exploration of the decision-strategy space defined by the level of risk and tradeoff in the decision process. We illustrate our approach with a land use planning process in the South of France. The objective is to find suitable areas for urban development while preserving green areas and their associated ecosystem services. The ecosystem service approach has indeed the potential to widen the scope of traditional landscape-ecological planning by including ecosystem-based benefits, including social and economic benefits, green infrastructures and biophysical parameters in urban and territorial planning. We show that in this particular case the decision-strategy space can be divided into four clusters. Each of them is associated with a map summarizing the average spatial suitability distribution used to identify potential areas for urban development.

As announced by the International Telecommunication Union (ITU), the United Nations specialised agency for information and communication technology (ICT), its annual AI for Good Global Summit has successfully generated thirty-five innovative project proposals leveraging the power of artificial intelligence (AI) for good. "Leveraging the power of ICTs, including artificial intelligence, is imperative if we are to improve the livelihoods of all people, everywhere, through achievement of the United Nations Sustainable Development Goals," said ITU Secretary-General Mr Houlin Zhao. "This year, we hope to spur action to ensure that artificial intelligence accelerates progress towards the Sustainable Development Goals (SDGs)," Mr Zhao said in his welcoming remarks. "Already, AI solutions are being developed to help increase crop yields, manage natural disasters, reduce road congestion, or diagnose heart, eye, and blood disorders." The summit gathered AI innovators with public and private-sector decision-makers, creating collaboration opportunities to execute the AI for Good project proposals in the near and medium terms.

The following is adapted from State of Green Business 2018, published by GreenBiz in partnership with Trucost. There is no shortage of smart people willing to offer their sometimes dire, sometimes optimistic opinions about how humankind's future will be reshaped by computers and software using some sort of artificial intelligence (AI). If there's one thing upon which the naysayers and yeasayers agree, it's that AI is already more real than many people realize. A whopping 70 percent of the companies surveyed last year by Forrester Research plan to use some form of AI by the end of this year. It's tough to think of a tech giant that isn't making AI research a priority: Alphabet (through DeepMind and Google), Amazon, Apple, Facebook, IBM and Microsoft are throwing literally millions of dollars at this opportunity.

Conventional models, while still solid, are no longer up to the heightened challenges of the present. Exponentially improving technologies for the Internet of Things (IoT) and artificial intelligence are enabling urban developments with much higher levels of efficiency and flexibility to conserve resources, promote security, and boost the quality of life. The key development is not the technologies themselves, but their integration around a holistic view of urbanization that enables a series of smart services. Instead of focusing on single services, or specific buildings or highways, leading organizations around the world are using IoT and analytics to optimize infrastructure generally and evolve with changing needs. While getting there will take a great deal of investment and expertise, the result will be places where residents thrive in unexpected ways in their personalized urban developments.