Artificial intelligence is moving into one of the most complicated laboratories on Earth: the modern city. In a new study published in npj Urban Sustainability, X. Zhai, P. M. Bach, J. Ghazoul and colleagues examine how AI could reshape urban ecosystem restoration—not simply by automating tasks, but by changing the relationships among people, ecological processes and technological systems. Their article, titled “Artificial intelligence for urban ecosystem restoration: reshaping social–ecological–technological interactions,” presents restoration as a challenge that cannot be solved by planting trees or installing sensors alone. Cities are living systems in which concrete, water, wildlife, infrastructure, public policy and human behavior constantly influence one another. AI, the researchers argue, could become a powerful instrument for understanding and managing those interactions, provided it is developed and deployed with ecological and social realities in mind.
Urban restoration is particularly difficult because cities are simultaneously damaged ecosystems and densely inhabited human environments. Rivers may be channelized, soils sealed beneath asphalt, wetlands replaced by buildings and natural habitats fragmented into isolated patches. At the same time, urban residents depend on the same spaces for housing, transport, cooling, recreation and economic activity. A restoration project that improves biodiversity but increases flooding risk, displaces vulnerable communities or restricts access to public space can produce new problems while solving old ones. AI offers a way to process vast and rapidly changing information about these systems. Satellite imagery, drone surveys, environmental sensors, weather records, mobility data and community observations can be combined to reveal patterns that would be difficult to detect through conventional fieldwork alone.
Technically, the value of AI lies in its ability to identify relationships within complex, high-dimensional datasets. Machine-learning models can classify land cover, detect changes in vegetation, estimate surface temperatures and map habitat fragmentation from images. Time-series algorithms can analyze air pollution, rainfall, soil moisture and water quality as they fluctuate across neighborhoods. More advanced models may simulate how different restoration strategies influence ecological and social outcomes over time. For example, an urban planning system could compare the likely effects of restoring a stream, expanding tree canopy or converting vacant land into a wetland. Such models do not “understand” ecosystems in the human sense, and they do not eliminate uncertainty. Instead, they calculate patterns and probabilities from available data, allowing decision-makers to explore possible futures before committing resources on the ground.
The study’s central contribution is its emphasis on social–ecological–technological interactions. This perspective treats AI not as a neutral machine placed above society, but as part of the urban ecosystem it is intended to manage. The data used to train an algorithm are collected through institutions, technologies and human choices. If some neighborhoods have dense sensor coverage while others are poorly monitored, an AI system may produce more accurate recommendations for affluent areas and weaker conclusions for communities already exposed to environmental risks. Historical data can also reproduce earlier planning inequalities. A model trained on past decisions may interpret unequal access to parks, clean water or cooling infrastructure as a normal pattern rather than a problem requiring correction. In this context, technical accuracy alone is not enough; the quality, representativeness and governance of data become ecological and political questions.
AI could also transform the way restoration is monitored after a project is completed. Traditional assessments may rely on periodic surveys that capture only brief snapshots of an ecosystem. Automated image analysis and sensor networks could provide continuous information about plant survival, invasive species, wildlife activity, soil conditions, water flows and heat patterns. This would make restoration more adaptive. If a newly planted corridor fails to support expected biodiversity, managers could adjust species selection, irrigation or habitat design rather than waiting years for a final evaluation. Real-time monitoring could be especially important as climate change intensifies heatwaves, extreme rainfall and drought. However, the study’s framing implies that more data should not automatically lead to more intervention. Ecosystems are dynamic, and managers must distinguish meaningful ecological change from short-term variation, sensor errors or artifacts created by the algorithms themselves.
The most visible promise of AI may be its ability to connect ecological information with public decision-making. Digital platforms could help residents visualize how a proposed green space might reduce local heat, absorb stormwater or support pollinators. Natural-language systems could translate technical assessments into accessible explanations, while participatory mapping could allow communities to identify flooding, pollution or unsafe conditions that official datasets overlook. These tools may broaden participation in restoration planning, but they can also create the illusion of inclusion if public feedback is collected without influencing final decisions. Community-generated data raise questions about privacy, consent and ownership, particularly when information reveals movement patterns, health conditions or the locations of culturally significant sites. A socially responsible AI system must therefore be designed not only to gather more information, but also to determine who controls it and who benefits from its use.
The researchers’ focus also highlights a danger that accompanies technological enthusiasm: the temptation to treat AI as a substitute for ecological knowledge and public institutions. Algorithms can optimize a selected objective, but the choice of that objective is a human judgment. A system designed to maximize carbon storage may favor fast-growing vegetation while overlooking native species or water demand. A model focused on reducing urban heat might recommend tree planting in locations where underground infrastructure, land ownership or maintenance capacity make the proposal unrealistic. A platform intended to identify restoration priorities could rank sites according to measurable indicators while missing historical, cultural or emotional values that communities consider essential. AI can support decisions, but it cannot determine what a city ought to value. That responsibility remains with residents, scientists, planners and policymakers.
The article arrives as cities worldwide search for strategies that can address biodiversity loss, climate stress and environmental inequality at the same time. Its message is likely to resonate because it places artificial intelligence inside a much larger transformation: the shift from isolated restoration projects toward continuously managed urban ecological networks. Yet the success of that shift will depend on safeguards as much as on computational power. Transparent models, open methods, independent evaluation and clear accountability will be needed when AI-supported recommendations influence land use or public spending. Restoration systems should be tested across different climates, neighborhoods and social conditions rather than validated only in data-rich locations. Human expertise must remain central, especially when models confront unfamiliar ecological conditions or conflicting community priorities. Used carefully, AI could help cities see hidden connections and respond faster; used carelessly, it could automate old biases at unprecedented speed.
By reframing urban restoration as a partnership among ecological science, social knowledge and digital technology, Zhai, Bach, Ghazoul and their co-authors point toward a future in which cities are managed less like collections of infrastructure and more like evolving ecosystems. The challenge is not to make nature obey an algorithm, but to use computation to improve how societies observe, discuss and repair the environments on which they depend. That distinction may determine whether AI becomes another layer of urban control or a tool for more resilient and inclusive restoration. As the technology races forward, the most important question is not whether artificial intelligence can reshape cities. It is whether cities can shape artificial intelligence around ecological limits, democratic participation and the long-term well-being of both human and nonhuman life.
Subject of Research: Artificial intelligence and urban ecosystem restoration
Article Title: Artificial intelligence for urban ecosystem restoration: reshaping social–ecological–technological interactions
Article References: Zhai, X., Bach, P.M., Ghazoul, J. et al. Artificial intelligence for urban ecosystem restoration: reshaping social–ecological–technological interactions. npj Urban Sustain (2026). https://doi.org/10.1038/s42949-026-00453-7
Image Credits: AI Generated
DOI: 10.1038/s42949-026-00453-7
Keywords: Artificial intelligence, urban ecosystem restoration, urban sustainability, social–ecological–technological interactions, machine learning, biodiversity, climate resilience, environmental governance
