About the project
Objective
In the Deep Wetlands project, we are developing a machine learning platform to monitor water extent changes in wetlands by integrating multiple data sources from satellite images, altimetry radars, and other space sensors. Wetlands are vital ecosystems for the functioning of the Earth system and necessary to achieve sustainable development. Water availability determines whether wetlands can thrive and deliver services to humans. However, thick vegetation mostly covers water changes, impairing their remote detection from space. Wetlands are disappearing rapidly; approximately 70% have been lost in the last 120 years.
Despite the danger that wetlands are currently facing, there is no global high-resolution assessment of wetland changes. This limits the in-depth and temporal analysis of wetlands from space. Changes in wetlands are unnoticed as most space-based technologies cannot fully account for water below vegetation and are limited to large water bodies. Our grand challenge is quantifying the wetland surface area changes on existing wetlands.
About the Digital Futures Postdoc Fellow
Francisco J. Peña is a postdoctoral researcher working in the field of artificial intelligence and remote sensing. He works jointly at the Software and Computer Systems (SCS) division of KTH Royal Institute of Technology and the Department of Physical Geography of Stockholm University in Sweden. Francisco is also a member of the Distributed Computing at KTH (DC@KTH). Before that, he was a postdoctoral researcher at University College Dublin (2018-2020). He obtained his PhD from University College Cork in June 2019.
His research interests include:
- Natural Language Processing
- Computer Vision
- Deep Learning
- Recommender Systems
- Remote Sensing
Main supervisor
Fernando Jaramillo, Assistant Professor, Stockholm University.
Co-supervisor
Amir Payberah, Assistant Professor, Division of Software and Computer Science, KTH.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
Dragons seeks to support inclusive, safe, resilient, and sustainable urban development by merging computational methods to gain insights into urban SET systems with governance approaches to act on these insights. Hence, its guiding questions are: (Q1) How can we combine available data to monitor inclusiveness, safety, resilience, and sustainability in urban SET systems? (Q2) How can we understand the evolution and interaction of structures and processes related to these goals? (Q3) How can urban governance incorporate the findings from Q1 and Q2?
Background
With over half of the world’s population living in cities and most population growth projected to occur in urban areas, making cities inclusive, safe, resilient, and sustainable is a key policy concern expressed in the eleventh UN Sustainable Development Goal (SDG 11). To ensure these properties in urban development, policymakers need to navigate the complex interplay between social (including economic and political), ecological, and technological factors shaping and shaped by human urban activity. This requires an interdisciplinary approach to urban areas as Social-Ecological-Technological Systems (SET systems).
About the Digital Futures Postdoc Fellow
Corinna Coupette studied law at Bucerius Law School and Stanford Law School, completing their First State Exam in Hamburg in 2015. They obtained a PhD in law (Dr. iur.) from Bucerius Law School and a BSc in computer science from LMU Munich, both in 2018, as well as an MSc in computer science in 2020 and a PhD in computer science (Dr. rer. nat.) in 2023, both from Saarland University. Their legal dissertation was awarded the Bucerius Dissertation Award in 2018, and the Otto Hahn Medal of the Max Planck Society in 2020, and their interdisciplinary research profile was recognized by the Caroline von Humboldt Prize for outstanding female junior scientists in 2022.
The overarching goal of Corinna’s research is to understand how we can combine code, data, and law to better model, measure, and manage complex systems. To this end, they explore novel ways of connecting computer science and law, such as using algorithms to collect and analyze legal data as networks or formalizing and implementing legal and mathematical desiderata for responsible data-centric machine learning with graphs.
Main supervisor
Aristides Gionis, WASP Professor of Computer Science, EECS, KTH.
Co-supervisor
Örjan Bodin, Professor, Stockholm Resilience Center and Stockholm University.
About the project
Objective
This research takes the experiential notion of being a foreigner as the starting point to represent how crises unfold in distant territories digitally. This ethnographic research utilises earthquakes as case studies to show how everyday experiences are shaped by how our bodies are connected to geographic singularities. By alluding to our human capacity for adaptability, this research aims to (a) generate novel interactive experiences to communicate the changing nature of the Anthropocene Era. Furthermore, (b) to generate design methods considering the global citizen’s perspective.
Background
Amidst the Anthropocene Era and the COVID pandemic, the resolution of traditionally ill-defined problems in design requires the recognition of non-dominant paradigms and cultural perspectives. Moreover, living in a world of shared uncertainties due to a series of political, environmental and social changes demands cultivating adaptability to face unexpected future scenarios.
From a methodological perspective, this research engages in first-person Soma Design research, which places the locus on the body and experience. This somatic epistemology is becoming increasingly influential in developing digital and interactive technologies in the last decade.
About the Digital Futures Postdoc Fellow
Claudia Núñez-Pacheco is an interaction design researcher and artist. She holds a PhD and a master’s degree from the Sydney School of Design at the University of Sydney in interaction design and electronic arts. Her research investigates how bodily ways of knowing can be used as crafting materials to design aesthetic experiences. In her research journey, Claudia has engaged in a multidisciplinary exploration that merges material thinking, wearable technology, human-computer interaction (HCI) and design methods with tools from experiential psychology. Claudia has been awarded twice by the National Commission for Scientific and Technological Research Scholarship (Chile), in addition to disseminating her research through various international HCI and design academic fora.
Main supervisor
Kia Höök, Professor, Division of Media Technology and Interaction design, KTH.
Co-supervisor
Thiemo Voigt, Professor, RISE Computer Science.
About the project
Objective
This research aims to design a new frontier for wireless extended reality (XR), achieved by intelligent reflecting surface (IRS)-aided terahertz (THz) communications, for providing reliable, low-latency and energy-efficient wireless XR services. Advanced machine learning algorithms will be exploited to develop intelligent, green, wireless XR solutions. The results are expected to apply to the wireless XR research and provide good insights for other fundamental wireless communication research areas.
Background
Extended reality (XR) is changing people’s lifestyles through the interaction of physical and virtual spaces. Wireless XR enables users to move freely and have a better quality of experience. However, wireless XR faces critical challenges such as high data rates, low interaction latency and limited power budget. Terahertz (THz) communications have emerged as a promising solution towards wireless XR due to its abundant spectrum. Meanwhile, intelligent reflecting surfaces (IRSs) can be deployed to compensate for the severe signal attenuation at THz frequencies. Although THz communications and IRS have been widely exploited for wireless communications, how to employ them in wireless XR is still an open question.
About the Digital Futures Postdoc Fellow
Chen Chen is a Digital Futures Postdoctoral Researcher at KTH Royal Institute of Technology. He received the B.E. degree from the East China University of Science and Technology, China, in 2018 and the PhD from the University of Sheffield, UK, in 2022. He was a Marie Curie PhD Fellow. From 2022 to 2023, he was a Postdoctoral Research Associate at the University of Liverpool, UK. His research interests include massive MIMO, wireless intelligence, wireless security, mmWave/THz networks, signal processing, and machine learning.
Main supervisor
Carlo Fischione, Professor, Division of Network and Systems Engineering, KTH.
Co-supervisor
Emil Björnson, Professor, Division of Communication Systems, KTH.
About the project
About the Digital Futures Postdoc Fellow
Mareike Glöss main research interest is understanding digital transformations and their diffusion into everyday life. She looks at how novel computing technologies are appropriated and how this impacts everyday life. An important part of her work is translating results to inform the design and development of new technologies. Mareike is especially interested in public spaces and how people move through them. She has studied auto-mobility aspects – on the road with Swedish commuters or as a passenger in Californian cabs and Uber rides.
But there is a big chance that auto-mobility will only be a small part of future mobility (at most). Thus, more recently, she started to think about new approaches to mobility away from single modes of transportation. According to Mareike Glöss, we must start thinking much more about personal journeys and intermodal forms of transport. Closely related to this is her interest in Smart Cities. Those are still treated very much like a future vision, but cities are already very smart. Just in a much more chaotic form than we had imagined. And this chaos is something she would like to untangle.
Main supervisor
Rob Comber, Associate Professor, Division of Media Technology and Interaction Designs, KTH.
Co-supervisor
Jonathan Metzger, Professor at the School of Architecture and the Built Environment, Urban Planning and Environment, KTH.
About the project
Objective
The research goal of this proposal is to develop a novel hierarchical control framework that enables multi-vehicle systems (MVS) to distribute, manage, and execute complex tasks in possibly dynamic and unstructured environments with safety guarantees and computational efficiency. For specified low-level navigation tasks, a unified theory will be developed devoted to distributed estimation and formation tracking control problems endowed with reactive collision avoidance abilities based on onboard local sensors (e.g., cameras or range finders). Decision-making mechanisms will be integrated to coordinate high-level navigation tasks to provide MVS with the capability to cooperatively specify the overall task and competitively allocate sub-tasks for each vehicle while ensuring efficient time and energy consumption.
The proposed framework will also enable a set (possibly the whole group) of agents to instantaneously choose and execute between different tasks reacting to real-time environmental changes in a prescribed mission. The innovative framework and algorithms will be developed with rigorous mathematical analysis and realistic simulations, potentially including engineered implementations that address practical urban challenges like search and rescue and intelligent transportation using aerial and sidewalk robots.
Background
Recent decades have witnessed the rapid expansion of autonomous robotic vehicles, such as self-driving cars, drones, autonomous marine vehicles, etc. They are envisioned as essential tools to venture into unsafe areas, enhance human sensory and manipulation abilities, or explore unstructured and possibly dangerous environments. Since a single autonomous agent typically makes it impossible to perform tasks in vast areas or complicated tasks that have to be decomposed into sub-tasks performed by multiple agents, both industry and academia have shown a great interest in the scientific area of networked autonomous vehicle systems, which are systems that can interact and coordinate with each other.
Although academic work on controlled multi-vehicle systems (MVS) has become ever-expanding recently, a large gap exists between current capabilities and required ones in real-world scenarios. For MVS to perform a wide range of tasks, autonomous navigation and control is a fundamental ability under which each vehicle should interact safely with neighbour vehicles and the surrounding environment. However, most existing control algorithms rely on full-state measurements, limiting their applicability to specific applications with suitably equipped experimental areas.
GPS signal is typically unreliable for practical scenarios involving tasks in urban canyons or congested environments. Hence, robust and computationally efficient distributed controllers and estimation algorithms must be designed based on onboard exteroceptive sensors (such as laser range finders, vision, and acoustic sensors). However, the documented results for robotic vehicles using onboard sensors have been limited to ad hoc scenarios. Moreover, the current practice of MVS is often conducting simple missions with restricted autonomy based on offline and centralized supervision and planning, assuming the environment is static and known, such as lighting shows via drones and delivering in warehouses via mobile robots.
About the Digital Futures Postdoc Fellow
Zhiqi Tang is a Digital Futures Postdoctoral Fellow at the Division of Decision and Control Systems of KTH Royal Institute of Technology, Sweden. From 2021 to 2022, she was a postdoctoral researcher with the Institute for Systems and Robotics. She was an Invited Assistant Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico (IST) in Portugal.
She earned a double PhD in Automatic Control and Robotics from IST, University of Lisbon, Portugal, and I3S-CNRS, Université Côte d’Azur, France, in 2021. She obtained her B.S. in Electrical and Computer Engineering from the University of Macau, Macau SAR, China, in 2015. Her research interests focus on the estimation, control, and decision-making in multi-agent systems, with applications in Robotics and Transportation Systems.
Main supervisor
Jonas Mårtensson, Associate Professor, Division of Decision and Control Systems at KTH.
Co-supervisor
Karl H. Johansson, Professor, Division of Decision and Control Systems at KTH.
Michele Simoni, Assistant Professor, Transport Systems Analysis at KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This research (3Dfire) aims to enhance forest fire detection and characterization through advanced remote sensing and AI techniques, facilitating improved management and mitigation strategies within forest ecoregions. Our AI algorithms will streamline the processing of extensive freely available remote sensing data, like Sentinel-1 and -2, to drastically reduce the time and costs associated with fire monitoring and prediction. Consequently, it will provide a comprehensive understanding of fire dynamics within the region. Additionally, “3Dfire” will identify and analyze the risk factors and drivers of fires in the Miombo woodlands, including the impact of extreme climate events and anthropogenic factors on fire severity, frequency, and duration.
The project’s outcomes will offer valuable insights to researchers and policymakers responsible for forest fire prevention, minimizing societal and ecological impacts, and promoting sustainable forest management practices. Ultimately, “3DFire” is pivotal in establishing a comprehensive global-scale perspective on AI for forest applications and transitioning towards climate-smart forest management.
Background
Miombo woodlands cover 270 million ha across southern Africa and are increasingly threatened by natural and anthropogenic forces. Despite their importance for biodiversity and 100 million forest-dependent people, this ecoregion receives little attention from the scientific community. The annual forest loss in just a part of the Miombo ecoregion was recently estimated to be more than half a million hectares. Fires are a primary cause of vegetation loss in Miombo, occurring approximately 50% more frequently than in other global ecoregions. They also trigger massive CO2 emissions in sub-Saharan Africa and threaten many ongoing sustainable forest management projects, such as Reducing Emissions from Deforestation and Degradation (REDD). The lack of accurate fire records challenges the estimation of CO2 emissions and evaluation of fire management activities in forests.
Although some dimensions of Miombo’s wildfires, such as frequency and severity, have been explored through coarse- and medium-resolution satellite time-series data from MODIS and Landsat, knowledge of the exact spatial extent, duration, and timing of the fires and their relations with effective driving forces is still lacking. However, fire regimes in miombo are dominated by small burn patches, particularly in the drier regions. Their limited spatial resolution makes it difficult to detect and characterize these small fires using the aforementioned satellites. Extreme climate events significantly elevate the probability of forest fires, while anthropogenic drivers affect all dimensions of fires. Meanwhile, extreme climate events and human pressures on forests are expected to increase as global temperatures rise and the population of southern Africa doubles by 2050. To address these limitations, this research will focus on characterizing and mapping various dimensions of fires, including the severity, frequency, timing, and duration of forest fires in miombo. We will leverage the time series data of fires derived from the Sentinel data and employ deep learning-based algorithms, particularly our developed residual attention UNet5 (RAUNet5).
About the Digital Futures Postdoc Fellow
Zeinab Shirvani is a postdoctoral researcher affiliated with the Division of Geoinformatics at KTH. She obtained a PhD in Remote Sensing/Cartography focusing on forest disturbances from TUD, Germany, in 2020. Before her current position, Zeinab was a postdoctoral researcher at the Swedish University of Agricultural Sciences (SLU) for two years. Zeinab has made significant contributions to remote sensing and machine learning throughout her research career, particularly in mapping woodland fires in tropical dry forests using RAUNet. Her expertise lies in applying geospatial artificial intelligence and remote sensing techniques to study forest disturbances.
Main supervisor
Yifang Ban, Professor and Director of the Division of Geoinformatics at the Department of Urban Planning and Environment at KTH.
Co-supervisor
Ulla Mörtberg, Professor and associate professor (docent), Department of Sustainable Development, Environmental Science and Engineering (SEED) at KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.