About the project
Objective
This project aims to develop a collaborative spatial perception framework that constructs various levels of abstract representations in a city-scale area, incorporating LiDAR point clouds, RGBD images, and remote sensing images collected by various agents in a collaborative autonomous system.
Background
The concept of digital twins, involving the creation of virtual representations or models that accurately mirror physical entities or systems, has garnered growing research attention in the realm of smart cities. However, a critical challenge in realizing digital twins lies in efficiently collecting data and recreating the real world, a task that typically demands substantial human effort. To address this gap, autonomous robots, originally designed to reduce human workload, hold immense potential in shaping the future of digital twinning. These robots can potentially assume a pivotal role in autonomously creating and updating the complete mirroring of the physical world, paving the way for the next generation of digital twinning.
About the Digital Futures Postdoc Fellow
Yixi Cai completed his PhD degree in Robotics at Mechatronics and Robotic Systems (MaRS) Laboratory from Department of Mechanical Engineering, University of Hong Kong. His research focuses on efficient LiDAR-based mapping with applications on Robotics. During his PhD journey, he explored the potential of LiDAR technology to enhance the autonomous capabilities of mobile robots, particularly unmanned aerial vehicles (UAVs). He developed ikd-Tree, FAST-LIO2, and D-Map that have been widely used in LiDAR community. He is deeply interested in exploring elegant representations of the world, which would definitely unlock the boundless possibilities in Robotics.
You might find more information about him from his personal website: yixicai.com
Main supervisor
Patric Jensfelt, Professor, Head of Division of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty
Co-supervisor
Olov Andersson, Assistant Professor at Division of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty
About the project
Objective
The project creates opportunities for a new form of public performances where the line between artists and audience is blurred. We work with mixed and augmented reality together with immersive participation and new mobile communication technology. We will demonstrate this in an interactive performance in a public environment.
The project aims at:
- Creating a completely novel arena for immersive, participatory and creative cultural events using mobile communications and 3D/Mixed Reality (MR) innovations
- Allowing artists and audience to participate in real-time performative events like theatre, music and dance, both indoors and outdoors in public spaces
- Developing an experimental mobile AR and MR visualizing and auditive platform allowing creation of new types of participatory artistic performances, utilizing cutting edge colocation and spatial map/digital twin technologies
Background
We focus on the creation of a completely new form of public performances where physical actors can interact with virtual actors, and with physical and virtual objects. The project develops and studies both artistic creative processes and new wireless communication technology such as WiFi7 and 6G. We also use a number of prototypes developed in collaboration between Stockholm University and Ericsson Research during 2023–2024.
Central to the SECE project is the use of mixed reality (MR), enabling us to mix physical and virtual actors, objects and environments. The goal is to enable performances also outdoors, which is a big challenge with today’s technology.
The project involves expertise from many different directions and areas such as mobile communication, augmented and mixed reality, artistry and choreography.
Project webpage on Stockholm University website
Crossdisciplinary collaboration
The SECE project is a collaboration between the Department of Computer and Systems Sciences (DSV) at Stockholm University, Ericsson Research and Kulturhuset Stadsteatern.
About the project
Objective
- Reallocate maintenance resources from purely scheduled (preventive) activities to condition‐based and predictive strategies, thereby freeing time for future upgrades and investments.
- Identify cost drivers within the pump systems to target the layers where maintenance improvements yield the highest savings.
- Develop a predictive maintenance algorithm that incorporates a reliability index and measurable parameters to classify pump status and estimate remaining service life, assigning each pump a condition score (1 = new, 5 = worn).
- Pilot and validate the new solution at Högdalen pump station, installing and evaluating additional sensing equipment as needed.
- Ensure compatibility with Stockholm’s IoT platform, positioning the project as a first step toward broader adoption across city departments and external companies.
Background
Wastewater collection networks are essential for ensuring public health and wellbeing, yet they are susceptible to numerous faults including pipe bursts, pump malfunctions, and valve failures. Traditionally, preventing these issues has depended on frequent inspections and reactive repairs. However, there is growing recognition that a more proactive strategy—one rooted in predictive and condition-based maintenance—can both enhance the reliability of wastewater infrastructure and streamline the resources required to operate it. Such an approach can significantly reduce unexpected downtime, extend equipment lifespans, and ultimately lower overall lifecycle costs.
Despite the promise of prognostic models for predictive maintenance in many industries, water infrastructure has not received as much attention as manufacturing or other sectors. Current diagnostic tools in this domain are often tailored to a specific component or pump type, requiring specialized local measurements such as vibrations, oil temperature, or power consumption. In wastewater networks with diverse types of stations and pumps, designing a model for each component can be time-consuming. Moreover, missing measurements or uncertain behavior pose additional challenges. Consequently, the need has emerged for a flexible, data-driven solution capable of handling variations in station design, measurement availability, and environmental conditions.
The DECORUM (Optimized Predictive Maintenance for Wastewater Pump Stations) project, establishes the cooperation between the City of Stockholm, the Stockholm water utility operator (SVOA), the international water technology firm Xylem, and KTH to fill these gaps. SVOA alone operates roughly 300 wastewater pump stations, each with multiple pumps crucial to the city’s sewage system. Through a six-step development plan, SVOA has already taken steps to reduce maintenance costs while preserving high operational reliability. The next milestone is to move from largely manual, reactive procedures toward data-driven, predictive strategies that detect anomalies early, recommend targeted maintenance, and help technicians make informed decisions about when and how to service equipment.
Crossdisciplinary collaboration
The DECORUM project brings together a multidisciplinary team spanning academia, industry, and municipal stakeholders. KTH researchers contribute expertise in systems modeling, predictive algorithms, and robust control, while SVOA provides domain knowledge of large-scale wastewater operations and real-world operational data. Xylem, as a leading water technology company, offers more in-depth insights into cutting-edge pump hardware and software solutions. By uniting these diverse perspectives, the project can address both theoretical and practical challenges, ultimately delivering a flexible, scalable, and impactful predictive maintenance framework for critical urban infrastructure.
About the project
Objective
Problem statement: Develop future sustainability agendas in an objective and data-driven manner, leveraging large language models (LLMs) to simultaneously address the social, economic and environmental needs of humanity and the planet.
The project has the following goals:
- Effectively utilize the Gemini ecosystem of large language models (LLMs) to analyze thousands of documents, yielding excellent performance in the context of sustainability.
- Perform a detailed analysis of synergies and tradeoffs among the 169 SDG targets, creating a detailed matrix of positive and negative interactions.
- Extend this analysis to find positive and negative connections between the 231 SDG indicators and the 9 Planetary Boundaries.
- Define a new set of goals, addressing the targets from both the SDG and PB frameworks.
Background
The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, is an ambitious blueprint for achieving a better and more sustainable future for all. It comprises 17 goals, 169 targets, and 231 measurable indicators that aim to address a wide array of global challenges, including poverty, inequality, climate change, environmental degradation, peace and justice. The intricate and interconnected nature of these goals means that progress in one area can often catalyze advancements in others.
However, this interconnectedness can also lead to unintended consequences where progress on certain goals may inadvertently hinder progress on others. While some of these interactions are straightforward and predictable, many remain complex and difficult to foresee. Nearly a decade after the launch of the 2030 Agenda, we have accumulated a wealth of data and developed new tools that enhance our ability to identify and understand the positive and negative interactions between the Sustainable Development Goals (SDGs). This understanding is crucial as we approach the deadline of the 2030 Agenda and begin to consider the design of goals for the Post-2030 Agenda. This is highlighted in a recent article in Nature by the PIs*. A critical component of this analysis is the Planetary Boundaries (PBs) framework, introduced by the Stockholm Resilience Center in 2009. The PBs provide a science-based framework for monitoring environmental thresholds that define a safe operating space for humanity. These boundaries are quantifiable and offer a valuable tool for assessing environmental sustainability, although they do not directly address the social dimensions that are integral to the SDG Agenda.
*Extending the SDGs to 2050 – a roadmap. Fuso-Nerini et al., Nature (2024)
Crossdisciplinary collaboration
The PIs have a long history of cross-disciplinary collaboration, combining the AI knowledge of Prof. Vinuesa with the sustainability and systems modeling of Prof. Fuso-Nerini.
About the project
Objective
This project aims to understand better how people assess safety in a particular area of Stockholm – Järva, more specifically, how people’s safety perceptions relate to the quality of the physical and social environment of the area. We investigate the nature of safety on two fronts: an intra-area focus where we examine micro-level safety perceptions by people living and working in Järva and a city-wide focus where we explore ways to capture how people living elsewhere in Stockholm perceive Järva. The research will combine multiple sensors and data types, including phone apps, map-based surveys, and machine-learning models.
Background
Safety is a core component of people’s quality of life, affecting physical and mental health and restricting mobility and accessibility to public places. As such, safety is also a fundamental quality of the urban environment – what happens in places depends on how safe they are (or are perceived to be). Research has long pointed to the fact that indicators of poor maintenance or signs of physical deterioration are more important determinants of poor safety perceptions than actual instances of crime. The buildings’ façades, design, and the sense of ownership they promote are bound to affect crime and safety. Hence, some questions arise: which settings promote safety and for whom? What do these settings look like from a safety perspective?
Järva constitutes an interesting case study for several reasons: the area is undergoing great growth in the coming years – with more than 15,000 housing units being developed, new transportation links, workplaces and schools. However, a significantly higher share of Järva’s population feels unsafe outdoors at night and is more likely to avoid certain places where they live than the Stockholm average. In a previous study where Stockholm citizens were asked to assess Google Street View images regarding safety, the findings showed that the physical environment in Järva was ranked the lowest across Stockholm. Therefore, this project seeks to produce several diagnostics of the safety conditions in Järva and contribute to a better understanding of the spatiotemporal variations of the population’s safety perceptions.
Crossdisciplinary collaboration
Sensoring Safety Perceptions is a collaboration between research teams at KTH and MIT Senseable City Lab (part of the Stockholm Senseable Lab), as well as Stockholm City, Mapita (Maptionnaire), Kista Science City, and CityCon, and other local stakeholders based in the study area.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event:
About the project
Objective
The vision of the proposed project is to establish a framework for dynamically optimized traffic control with a view to reduce the negative impact associated with the transport system in dense urban areas, including congestion, pollutants and noise emissions. For this purpose, the research will consist of assembling a sensor network in Stockholm collecting and processing traffic and emission data. The data will feed a set of advanced modelling tools in order to develop multilayer visualisation and simulation models.
Background
With an estimated 55% of the world’s population residing in urban environments, with projections reaching 68% by 2050, exposure to high noise levels and other environmental factors, such as air pollution, is a growing concern. In this context, the concept of smart cities has emerged to respond to the challenge of quality of living and sustainable development of these urban environments. In particular, the digitalization of society provides an opportunity to assess the exposure to these environmental stressors better to identify root causes for which targeted mitigation strategies may be specifically implemented. This may be even more so when approaching near real-time capability, opening the way for dynamic solutions.
The GEOMETRIC project seeks to contribute to this need by implementing and demonstrating recent state-of-the-art research aiming at real-time representation of traffic and associated environmental stressors in dense urban environments.
Crossdisciplinary collaboration
GEOMETRIC is a collaboration between the City of Stockholm, Kista Science City, and three research teams at KTH with expertise in Sound and Vibration, Connected transport systems, and Geoinformatics.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event:
About the project
Objective
The EFFECT project aims to develop a digital twin of electrified construction site resources, processes, and their dependencies to evaluate the potential cost (efficiency) and benefit (emission reductions) of best-practice electrification of a construction site. Partners KTH, ABConnect, and Gordian, along with the City of Stockholm and 3rd parties PEAB and Northvolt, will use the digital twin to evaluate a construction site and, in a planned continuation project, optimize the steps of electrified construction operations using AI methods similar to those used in chess machines. The digital twin and its application will be developed and tested within the Kvarter Persika living lab, an urban renewal project of 1200 apartments in Södermalm, Stockholm.
Background
By 2050, the number of people living in cities will increase by 60% to 6.5 billion. City construction today is responsible for 23% of the global carbon emissions. Electrification is the de-facto technology for decarbonising our society, including city construction. However, due to the variability, non-linearity, and relatively long duration of new processes linked to electrification, we need more knowledge about the potential benefits and costs of electrified constructions and smart methods for planning and optimizing electrified construction operations.
Crossdisciplinary collaboration
In the EFFECT project, academia, research-based startups, the city and large industrial partners from the construction and energy industries will combine academic disciplines of control theory, simulation, optimization, AI/ML, and network communications to make future city construction more sustainable.