About the project

Objective
The research team’s ambition is to develop a new research area in urban development (studies). Well-being in smart cities is the defined research area, focusing on interactions of human-machine-computers or “cyber-physical-human systems” based on human decision-making on an institutional, individual and neurological abstraction level. The smart city of the future is our main application area, as these are complex cyber-physical-human systems. The project will develop a framework for capturing interactions and dynamics in these systems and demonstrate the applications in user case studies.

Background
The health condition of a human being is the basis of individual and social well-being. The driving force for human-social behaviour and many choices individuals make is the desire for well-being, which will manifest in the future of smart cities. Networks, human agents, cyber agents, and physical infrastructure perform feedback and interactions in smart cities. Smart cities can efficiently and sustainably increase human well-being.

Cross-disciplinary collaboration
The research team represents the School of Electrical Engineering and Computer Science (EECS, KTH), the School of Industrial Engineering and Management (ITM, KTH) and the School of Architecture and the Built Environment (ABE, KTH).

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Find out more on HiSS webpage

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

Results

A general objective of the project is to link dominant mechanisms of decision-making and choice between the micro, meso and macro scales that are most relevant for advancing the sustainability agenda in smart cities. The specific objectives are related to theoretical and experimental studies of different aspects of decision-making at micro, meso and macro scales that help answer the following questions:

Publications

We like to inspire and share interesting knowledge!

  1. M. Lenninger, M. Skoglund, P. Herman & A. Kumar. Are single-peaked tuning curves tuned for speed rather than accuracy? Nature Communications (in review).
  2. M. Lundqvist, S.L. Brincat, M.R. Warden, T.J. Buschman, E.K. Miller & P. Herman. Working memory control dynamics follow principles of spatial computing. Nature Communications (in review).
  3. M. Molinari, J. Anund Vogel, D. Rolando. Using Living Labs to tackle innovation bottlenecks: the KTH Live-In Lab case study,Applied energy(Extension under review).
  4. N. Chrysanthidis, F. Fiebig, A. Lansner & P. Herman. “Traces of semantization-from episodic to semantic memory in a spiking cortical network model”, eNeuro, July 2022, 9 (4). https://doi.org/10.1523/ENEURO.0062-22.2022.
  5. Fontan, V. Cvetkovic, K. H. Johansson. On behavioral changes towards sustainability for connected individuals: a dynamic decision-making approach, in 4th IFAC Workshop on Cyber-Physical Human Systems, Houston, Texas, December 1-2, 2022.
  6. Taras Kucherenko, Rajmund Nagy, Michael Neff, Hedvig Kjellström, and Gustav Eje Henter. Multimodal analysis of the predictability of hand-gesture properties. In
  7. International Conference on Autonomous Agents and Multi-Agent Systems, 2022.
  8. M. Lundqvist, J. Rose, S.L. Brincat, M.R. Warden, T.J. Buschman, P. Herman, & E.K. Miller. “Reduced variability of bursting activity during working memory.” Scientific Reports 12, no. 1 (2022): 1-10.
  9. N.B. Ravichandran, A. Lansner & P. Herman. “Brain-like combination of feedforward and recurrent network components achieves prototype extraction and robust pattern recognition”. In: Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, Springer, Cham
  10. D. Rolando, W. Mazzotti, M. Molinari. Long-Term Evaluation of Comfort, Indoor Air Quality and Energy Performance in Buildings: The Case of the KTH Live-In Lab Testbeds, Energies, vol. 15, no. 14, pp. 4955, 2022.
  11. N. Chrysanthidis, F. Fiebig, A. Lansner & P. Herman. “Semantization of episodic memory in a spiking cortical attractor network model”, Journal of Computational Neuroscience, vol. 49, no. SUPPL 1, pp. S86–S87, 2021.
  12. A. Karvonen, V. Cvetkovic, P. Herman, K.H. Johansson, H. Kjellström, M. Molinari & M. Skoglund. “The ‘New Urban Science’: towards the interdisciplinary and transdisciplinary pursuit of sustainable transformations.” Urban Transformations 3, no. 1 (2021): 1-13.
  13. M. Molinari, J. Anund Vogel, D. Rolando. Using Living Labs to tackle innovation bottlenecks: the KTH Live-In Lab case study, in Energy Proceedings – Applied Energy Symposium: MIT A+B, 2021.
  14. N.B. Ravichandran, A. Lansner & P. Herman. “Semi-supervised learning with Bayesian Confidence Propagation Neural Network”, in Proc. European Symposium on Artificial Neural Networks (ESANN) 2021. doi.org/10.14428/esann/2021.es2021-156.
  15. Ruibo Tu, Kun Zhang, Hedvig Kjellström, and Cheng Zhang. Optimal transport for causal discovery. In International Conference on Learning Representations, 2022.
  16. Carles Balsells Rodas, Ruibo Tu, and Hedvig Kjellström. Causal discovery from conditionally stationary time-series, arXiv:2110.06257, 2021.
  17. M. Lenninger, M. Skoglund, P. Herman and A. Kumar. Bandwidth expansion in the brain: Optimal encoding manifolds for population coding. In Cosyne, 2021.
  18. S. Molavipour, G. Bassi, and M. Skoglund. On neural estimators for conditional mutual information using nearest neighbors sampling. IEEE Transactions on Signal Processing 69:766-780, 2021.
  19. M. Sorkhei, G. Eje Henter, and H. Kjellström. Full-Glow: Fully conditional Glow for more realistic image generation. In DAGM German Conference on Pattern Recognition, 2021.
  20. Chenda Zhang and Hedvig Kjellström. A subjective model of human decision making
  21. based on Quantum Decision Theory, arXiv:2101.05851, 2021.
  22. M. Molinari and D. Rolando. Digital twin of the Live-In Lab Testbed KTH: Development and calibration. In Buildsim Nordic, 2020.
  23. D. Rolando and M. Molinari. Development of a comfort platform for user feedback: The experience of the KTH Live-In Lab. In International Conference on Applied Energy, 2020.
  24. E. Stefansson, F. J. Jiang, E. Nekouei, H. Nilsson, and K. H. Johansson. Modeling the decision-making in human driver overtaking. In IFAC World Congress, 2020.
  25. Y. Yi, L. Shan, P. E. Paré, and K. H. Johansson. Edge deletion algorithms for minimizing spread in SIR epidemic models. arXiv preprint arXiv:2011.11087, 2020.

About the project

Objective
This impact project focuses on one of the main areas of the Democritus collaborative project, the digitization of drinking water and wastewater networks. Based on the results of the Democritus project, the objectives of this impact project are the following:

The results will be disseminated through stakeholder workshops, open-source software and accessible video material.

Background
The Smart Society critically depends on large infrastructures like electrical grids or water distribution networks that need to operate efficiently, with predictable performance and meet strict safety and security requirements. In the Democritus collaborative project, we have investigated the problems of monitoring and managing these large critical infrastructures with the help of digitization. Within this large area, we focused on three main topics: (a) distributed learning over wireless networks, (b) learning accuracy and security in large systems, and (c) learning for detection and localization with application to water networks.

Cross-disciplinary collaboration
The team consists of experts from the School of Electrical Engineering and Computer Science and the School of Engineering Sciences at KTH, from Stockholm University and RISE, with research experience in network design and optimization, learning and decision making, security of cyber-physical systems, and large-scale experimentation. The project collaborates with SVOA, the Stockholm Water company.

About the project

Objective
The main goal of the extension HiSSx is to plan for long-term scientific and societal impact by establishing a new transdisciplinary research centre on a human-centric smart built environment. Three specific tasks have been identified to reach this goal:

Background
The building sector accounts for over one-third of global energy consumption and emissions; therefore, transforming the built environment is urgent to achieve the societal climate and sustainability goals by 2030. Traditional solutions for smart and sustainable buildings and cities have focused and prioritized the technological perspective, overlooking the human dimension and leading to sub-optimization. The project HiSS tackled this complex, unresolved, and cross-disciplinary challenge addressing human decision-making and behaviour in complex environments like smart buildings. Using a multi-layered system perspective approach, the project HiSS aimed to integrate knowledge, tools, and methodologies from cognitive and behavioural sciences, control, building, and energy technology.

Among multiple success factors, HiSS developed a novel model for choice probability; the project also delved into and made notable contributions to human behavioural modelling, learning and modelling social networks and addressing key limitations in behavioural modelling and control in buildings. These advancements in the study of human behaviour further inspired us to investigate cognitive brain processes and simulate human decision-making in neuroeconomic game scenarios using computational brain models. Joint interest in brain function also sparked a collaborative effort towards a deeper theoretical understanding of how the brain processes input information at the level of neural circuits.

These modelling advancements, together with an innovative data-driven control architecture and the development of a digital twin for smart building testbeds, provided the proof-of-concept of the feasibility of scalable human-centric controls in buildings. These concepts have proven and demonstrated the technical feasibility of the exploitation of human-centric networks and controls in the KTH Live-In Lab, a platform of smart building testbeds and have been integrated into the analysis of policy expectations of households’ role in the smart grid, resulting in identified gaps, where social issues are not accounted for.

Cross-disciplinary collaboration
The cross-disciplinary research team is formed by seven PIs representing three KTH Schools: Electrical Engineering and Computer Science (EECS), Architecture and the Build Environment (ABE), and Industrial Engineering and Management (ITM). The project team will also involve key industrial and societal stakeholders and our external academic collaborators from Technion, Imperial College London and Uppsala University.

About the project

Objective
The collaborative project DataLEASH in Action aims to develop novel methods that enable the sharing and learning from data. Legal privacy concerns often prevent implementations of technical solutions so that case studies (sandbox pilots) involving legal and technical competences as proposed in this impact project are seen as the most promising strategy forward. These case studies are pivotal in understanding the nuances of legal requirements and developing technically feasible solutions. The objective is to strike a balance where legal requests are not overly demanding yet necessitate state-of-the-art technical solutions.

Background
Digitalization has resulted in more and more data being generated and collected from various sources (such as health care, customer service, surveillance cameras, etc.). The data is valuable for processing and additional analysis to improve predictions and planning. Advances in machine learning have improved this kind of data analysis, while data-protection regulation such as the GDPR has introduced constraints, limiting what data can be used and for what purpose. There is, thus a tension between the utility of data and the privacy of the individuals the data is about.

Cross-disciplinary collaboration
DataLEASH in Action brings together researchers from the School of Electrical Engineering and Computer Science (EECS, KTH), the Department of Computer and Systems Sciences (DSV) and the Department of Law both at Stockholm University

About the project

Objective
The Decision-making in Critical Societal Infrastructures (DEMOCRITUS) project develops methods for monitoring and controlling large-scale infrastructures with the help of digitalization. We design new methods for learning over large datasets, propose networking solutions that support monitoring, learning and control, and construct data-driven models of the monitored physical processes. As an application, Democritus focuses on the water distribution systems, which exhibit many unsolved challenges for future societal systems. We study real-time leak detection, detection and mitigation of possible contamination or attacks, global decision-making while observing local data privacy, and the efficient utilization of smart meters.

Background
The Smart Society critically depends on large infrastructures like electrical grids or water distribution networks that need to operate efficiently, with predictable performance and meet strict safety and security requirements. They must also be able to make informed decisions under constraints and in real time – one simple error can have devastating consequences. Despite their technological diversity, the digitalization of these infrastructures can follow a common set of novel design principles.

Cross-disciplinary collaboration
The team consists of experts from the School of Electrical Engineering and Computer Science and the School of Engineering Sciences at KTH, from Stockholm University and from RISE with research experience in network design and optimization, learning and decision making, security of cyber-physical systems, and large-scale experimentation.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

Results

Results from three main areas of research include dimensioning of network and learning resources for distributed learning, wireless protocols for supporting machine learning, and methods to localise leaks in water networks from passively collected data. A stakeholder workshop on the project’s topic was organised in 2021 with participation from the public and private sectors, and collaboration with water distribution companies and the KTH Water Centre has been strengthened.

Publications

We like to inspire and share interesting knowledge!

Water distribution networks

DP Souza, R Du, B da Silva Jr, J Mairton, CC Cavalcante, C Fischione, Leakage Detection In Water Distribution Networks: Efficient Training By Data Clustering, IWA World Water Congress & Exhibition 2022.

DP Sousa, R Du, J Mairton Barros da Silva Jr, CC Cavalcante, C. Fischione, “Leakage detection in water distribution networks using machine-learning strategies”, Water Supply 23 (3), 1115-1126, 2023.

L. Lindström, S. Gracy, S. Magnússon and H. Sandberg, “Leakage Localization in Water Distribution Networks: A Model-Based Approach”, European Control Conference, 2022.

M. Mascherpa, I. Haasler, B. Ahlgren, J. Karlsson: “Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information.” Accepted for publication in European Journal of Control.

Cyber-physical systems

M, Zhang, Q. Xu, S Magnússon, R. Pilawa-Podgurski and G. Guo, “Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System”, IEEE Energy Conversion Congress and Exposition, 2022.

D Weinberg, Q Wang, TO Timoudas, C Fischione, “A review of reinforcement learning for controlling Building Energy Systems from a computer science perspective”, Sustainable Cities and Society, 104351,3, 2022.

Bengt Ahlgren. Simulator Framework for Developing Decision-Making Methods for Critical Infrastructure. In 18th Swedish National Computer Networking and Cloud Computing Workshop (SNCNW 2023), Kristianstad University, Kristianstad, June 14-15, 2023.

Large-scale and distributed learning and optimization

A. Alanwar, A. Berndt, K.H. Johansson and H. Sandberg, “Data-Driven Set-Based Estimation Using Matrix Zonotopes with Set Containment Guarantees”, European Control Conference, 2022.

X. Wu, S. Magnússon, H. Reza Feyzmahdavian, M. Johansson, “Optimal convergence rates of totally asynchronous optimization”, IEEE Conference on Decision and Control (CDC), 2022.

X. Wu, S. Magnússon, H. Reza Feyzmahdavian, and M. Johansson, “Delay-adaptive step-sizes for asynchronous learning” International Conference on Machine Learning (ICML), 2022.

TO Timoudas, S Zhang, S Magnússon, C Fischione, ”A General Framework to Distribute Iterative Algorithms with Localized Information over Networks”, IEEE Transactions on Automatic Control, 2023.

S. Vaishnav and S. Magnússon, “Energy-Efficient and Adaptive Gradient Sparsification for Federated Learning” 2023 IEEE International Conference on Communications (ICC), Rome, Italy, 2023.

S. Vaishnav and S. Magnússon, “Intelligent Processing of Data Streams on the Edge Using Reinforcement Learning” 2023 IEEE International Conference on Communications Workshops (ICC), Rome, Italy, 2023

I. Haasler, A. Ringh, Y. Chen, J. Karlsson, “Multi-marginal optimal transport with a tree-structured cost and the Schroedinger bridge problem,” SIAM Journal on Control and Optimization, 59(4), 2428-2453, 2021.

I. Haasler, J. Karlsson, and A. Ringh, “Control and estimation of ensembles via structured optimal transport, A computational approach based on entropy-regularized multi-marginal optimal transport,” IEEE Control Systems Magazine 41 (4), 50-69, 2021.

I. Haasler, A. Ringh, Y. Chen, and J. Karlsson, “Efficient computations of multi-species mean field games via graph-structured optimal transport,” IEEE Conference on Decision and Control, 2021.

J. Fan, I. Haasler, J. Karlsson, and Y. Chen, “On the complexity of the optimal transport problem with graph-structured cost,” AISTATS, 2022.

I. Haasler, A. Ringh, Y. Chen, J. Karlsson, “Scalable computation of dynamic flow problems via multi-marginal graph-structured optimal transport” Accepted for publication in Mathematics of Operations Research.

Networking for/with machine learning

H. Hellström, V. Fodor and C. Fischione, “Over-the-Air Federated Learning with Retransmissions,” IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021.

H. Hellström, V. Fodor, C. Fischione, “Unbiased Over-the-Air Computation via Retransmissions,” IEEE , Global Communications Conference (GLOBECOM), 2022.

S Razavikia, JA Peris, JMB Da Silva, C Fischione, “Blind Asynchronous Over-the-Air Federated Edge Learning”, 2022 IEEE Globecom Workshops (GC Wkshps), 1834-1839, 2, 2022.

H Hellström, JMB da Silva Jr, MM Amiri, M Chen, V Fodor, HV Poor, C. Fischione, “Wireless for machine learning: A survey”, Foundations and Trends® in Signal Processing 15 (4), 290-39916, 2022.

P Park, P Di Marco, C Fischione, Optimized over-the-air computation for wireless control systems, IEEE Communications Letters 26 (2), 2022.

Z Chen, EG Larsson, C Fischione, M Johansson, Y Malitsky, Over-the-Air Computation for Distributed Systems: Something Old and Something New, IEEE Networking Magazine, 2023.

H Hellström, V Fodor, C Fischione, “Federated Learning Over-the-Air by Retransmissions”, IEEE Transactions on Wireless Communications, 2023.

S Razavikia, JMB Silva Jr, C Fischione, “Computing Functions Over-the-Air Using Digital Modulations”, IEEE ICC 2023.

Y Kim, E Al Hakim, J Haraldson, H Eriksson, JMB da Silva, C Fischione, Dynamic clustering in federated learning, ICC 2021-IEEE International Conference on Communications, 1-610, 2021.

R Du, S Magnusson, C Fischione, The Internet of Things as a deep neural network, IEEE Communications Magazine 58 (9), 20-2511, 2020.

R Du, S Magnússon, C Fischione, The IoT as a Deep Neural Network, IEEE Communications Magazine, 2, 2020.

A Mahmoudi, HS Ghadikolaei, C Fischione, Cost-efficient distributed optimization in machine learning over wireless networks, ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-77, 2020.

A Mahmoudi, HS Ghadikolaei, C Fischione, Machine learning over networks: co-design of distributed optimization and communications, 2020 IEEE 21st International Workshop on Signal Processing Advances in Communications.

X. Wang, F. S. Samani, and R. Stadler, “Online feature selection for rapid, low-overhead learning in networked systems,” 16th International Conference on Network and Service Management (CNSM 2020). IEEE, 2020.

R. S. Villaca and R. Stadler, “Online learning under resources constraints,” in 2021 17th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2021). IEEE, 2021.

X. Wang, FS. Samani, A. Johnsson,R. Stadler, “Online Feature Selection for Low-overhead Learning in Networked Systems (Demonstration),” In 2021 17th International Conference on Network and Service Management (CNSM) 2021 Oct 25 (pp. 527-529). IEEE, 2021.

X. Wang, R. Stadler, “Online Feature Selection for Efficient Learning in Networked System,” IEEE Transactions on Network and Service Management. 2022 Jun 8.

Network protocol design

P Park, P Di Marco, C Fischione, Wireless for control: Over-the-air controller, IEEE Communications Letters 25 (10), 3437-34411, 2021.

TO Timoudas, R Du, C Fischione, Enabling massive IoT in ambient backscatter communication systems, ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-64 2020.

R Du, TO Timoudas, C Fischione, Comparing backscatter communication and energy harvesting in massive IoT networks, IEEE Transactions on Wireless Communications 21 (1), 429-4431, 2021.

P Park, HS Ghadikolaei, C Fischione, Proactive fault-tolerant wireless mesh networks for mission-critical control systems, Journal of Network and Computer Applications 186, 1030824, 2021.

M. Zeng, V. Fodor, Energy minimization for delay constrained mobile edge computing with orthogonal and non-orthogonal multiple access, Ad Hoc Networks, Vol. 98, 2020.

M. Zeng and V. Fodor, “Parallel Processing at the Edge in Dense Wireless Networks,” in IEEE Open Journal of the Communications Society, vol. 3, 2022.

J. A. Peris and V. Fodor, “Modelling multi-cell edge video analytics,” ICC 2022 – IEEE International Conference on Communications, 2022.

J. A. Peris and V. Fodor, “Distributed Join-the-Shortest-Queue with Sparse and Unreliable Information Updates,” ICC 2022 – IEEE International Conference on Communications, 2022.

B. Ahlgren, and K-J Grinnemo, “ZQTRTT: a multipath scheduler for heterogeneous traffic in ICNs based on zero queueing time ratio”, In Proceedings of the 9th ACM Conference on Information-Centric Networking (ICN ’22), 2022.

About the project

Objective
Our planet faces unprecedented environmental challenges, including rapid urbanization, deforestation, pollution, loss of biodiversity, melting glacier, rising sea-level, and climate change. In recent years, the world also witnessed numerous natural disasters, from droughts, heat waves and wildfires to flooding and hurricanes, killing thousands and causing billions in property and infrastructural damages. With its synoptic view and large area coverage at regular revisits, satellite remote sensing has played a crucial role in monitoring our changing planet.

The overall objective of the EO-AI4GlobalChange (EO-AI4GlobalChange: Earth Observation Big Data and Deep Learning for Change Detection and Environmental Impact Assessment: Urbanization and Forest Fire Monitoring as Examples) project is to develop innovative and robust methods for monitoring global environmental changes using Earth Observation big data and deep learning. This research will focus on three major global environmental challenges: urbanization, wildfires and flooding.

Open and free Earth observation big data such as Sentionel-1 SAR and Sentinel-2 MSI data have been used to demonstrate the novel deep learning-based methods in selected cities worldwide and various wildfire and flooding sites across the globe. For urban mapping, a novel Domain Adaptation (DA) approach using semi-supervised learning has been developed for built-up area extraction. Several novel methods have been developed for urban change detection, including a dual-stream U-Net and a Siamese Difference Dual-Task network. For early detection of active fires, Gated Recurrent Units and transformer networks have been used to improve GOES-R and VIIRS dense time series detections.

Figure 1. The Dixie Fire burned 963,309 acres (389,837 ha), was the largest single (i.e. non-complex) wildfire in recorded California history and the second-largest wildfire overall. The burned areas were clearly detected by the Sentinel-2 MSI (Left) and Sentinel-1 SAR (Right) images.

Figure 2. Started on July 13, 2021, and contained on October 25, 2021, the progression of the Dixie Fire was captured by Sentinel-1 and Sentinel-2 images.

For wildfire progression monitoring, transfer learning-based models have been evaluated to exploit Sentinel-1 SAR and Sentinel-2 MSI data. Civil contingencies agencies can use the timely and reliable information the project generates to support effective emergency management and decision-making during and after wildfires and flooding. Automatic and continuous mapping of urban areas and their changes can support sustainable and resilient city planning and contribute to monitoring the UN 2030 Urban Sustainable Development Goal (SDG 11).

Background
In recent years, the world has experienced many devastating wildfires due to human-induced climate change, most recently in Australia around the turn of 2019/2020. Wildfires kill and displace people, damage property and infrastructure, burn vegetation and harm wildlife, and cost billions of euros to fight. Up-to-date and reliable information on fire risk, active fires, fire extent, progression and damage assessment is critical for effective emergency management and decision support.

The pace of urbanization has been unprecedented. Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution, urban heat island, loss of biodiversity and ecosystem services, and making cities more vulnerable to disasters. Therefore, accurate and consistent information on urban changing patterns is essential to support sustainable urban development and the UN’s New Urban Agenda.

Activities & Results

Find out what’s going on!

Activities, awards, and other outputs

Notable Presentations

Conference Presentations

Results

The overall objective of the EO-AI4GlobalChange project is to develop innovative and robust methods for monitoring environmental changes using Earth Observation big data and deep learning. This research will focus on three major global environmental challenges: urbanization, wildfires and flooding.

The specific objectives are:

  1. to develop novel, automatic and globally applicable methods for effective change detection using deep learning and big data analytics to exploit all available Earth Observation data;
  2. to adapt the developed methods for continuous urban change detection, for flood mapping, and for wildfire monitoring including early detection of active fires, near real-time monitoring of wildfire progression and rapid damage estimation;
  3. to assess the environmental impacts of urbanization and wildfires on biodiversity and ecosystem services.

The research achievements are:

List of open-data repositories and developed software

Publications

We like to inspire and share interesting knowledge!

Peer-reviewed journal publications and book chapter

  1. Hafner, S. Y. Ban and A. Nascetti, 2022. Unsupervised Domain Adaptation for Global Urban Extraction Using Sentinel-1 and Sentinel-2 Data. Remote Sensing of Environment. Vol. 280,113192, https://doi.org/10.1016/j.rse.2022.113192.
  2. Hafner, S., A. Nascetti, H. Azizpour and Y. Ban, 2022. Sentinel-1 and Sentinel- 2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net. IEEE Geoscience and Remote Sensing Letters, Vol. 19, pp. 1-5. doi: 10.1109/LGRS.2021.3119856.
  3. Mugiraneza, T., S. Hafner, J. Haas, Y. Ban. 2022. Monitoring urbanization and environmental impact in Kigali, Rwanda using Sentinel-2 MSI data and ecosystem service bundles. International Journal of Applied Earth Observation and Geoinformation, Vol. 109, 102775. DOI: https://doi.org/10.1016/j.jag.2022.102775.
  4. Yadav, R., A. Nascetti, Y. Ban. 2022. Deep Attentive Fusion network for Flood Detection on Uni-temporal Sentinel-1 Data. Frontiers in Remote Sensing, section Microwave Remote Sensing (Accepted).
  5. Hu, X., Y. Ban, and A, Nascetti. 2021. Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning. Remote Sensing, 13, no. 8: 1509.
  6. Hu, X., Y. Ban, and A, Nascetti. 2021. Sentinel-2 MSI data for active fire detection in major fire-prone biomes: A multi-criteria approach. International Journal of Applied Earth Observation and Geoinformation, 101, 102347.
  7. Zhang, P., Y. Ban, and A. Nascetti. 2021. Learning U-Net without Forgetting for Near Real-Time Wildfire Monitoring by the Fusion of SAR and Optical Time Series. Remote Sensing of Environment, 1-12. https://doi.org/10.1016/j.rse.2021.112467.
  8. Zhao Y, and Y. Ban. 2022. GOES-R Time Series for Early Detection of Wildfires with Deep GRU-Network. Remote Sensing. 2022; 14(17):4347. https://doi.org/10.3390/rs14174347.
  9. Furberg, D., and Y. Ban. 2021. Satellite monitoring of urbanization and environmental impacts in Stockholm, Sweden, through a multiscale approach. Urban Remote Sensing, 2nd Edition, Ed: X. Yang, Wiley.
  10. Furberg, D., Ban, Y. & Mörtberg, U., 2020. Monitoring urban green infrastructure changes and impact on habitat connectivity using high-resolution satellite data. Remote Sensing, 12(18), 3072. https://doi.org/10.3390/rs12183072.
  11. Ban, Y., Zhang, P., Nascetti, A., Bevington, A. R., Wulder, M. A., 2020. Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning. Scientific Reports, 10(1), 1–15. https://www.nature.com/articles/s41598-019-56967-x.

Peer-Reviewed Conference Papers

  1. Hafner, S., Y. Ban and A. Nascetti, 2022. Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2022, Kuala Lumpur, Malaysia.
  2. Hu, X., P. Zhang and Y. Ban, 2022. Gan-Based SAR to Optical Image Translation in Fire- Disturbed Regions. Proceedings of IGARSS’2022, Kuala Lumpur, Malaysia.
  3. Zhang, P., X. Hu, and Y. Ban, 2022. Wildfire-S1S2-Canada: A Large-Scale Sentinel-1/2 Wildfire Burned Area Mapping Dataset Based on the 2017-2019 Wildfires in Canada. Proceedings of IGARSS’2022, Kuala Lumpur, Malaysia.
  4. Zhao, Y., Y. Ban, 2022. Global Scale Burned Area Mapping Using Bi-Temporal ALOS-2 PALSAR-2 L-Band Data. Proceedings of IGARSS’2022, Kuala Lumpur, Malaysia.
  5. Yadav, R., A. Nascetti and Y. Ban, 2022. “Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data,” Proceedings, IGARSS’2022.
  6. Yadav, R., Y. Ban and A. Nascetti, 2022. “Building Change Detection using Multi-Temporal Airborne LiDAR Data,” Proceedings, XXIV ISPRS Congress (2022 edition), 6–11 June 2022, Nice, France.
  7. Hafner, S., Y. Ban and A. Nascetti, 2021. Exploring the Fusion of Sentinel-1 SAR and Sentinel-2 MSI Data for Built-Up Area Mapping Using Deep Learning. Proceedings of IGARSS’2021, Brussels, Belgium.
  8. Zhao, Y., Y. Ban and A. Nascetti, 2021. “Early Detection of Wildfires with GOES-R Time- Series and Deep GRU Network,” Proceedings of IGARSS’2021, Brussels, Belgium.

About the project

Objective
The Learning in Routing Games for Sustainable Electromobility (RoSE) project will employ large-scale simulation, learning, and game theory to develop sustainability-aware traffic routing tools. The tools will leverage and fuse heterogeneous, noisy, and often incomplete data from various sources, such as infrastructure condition data, traffic flow data, and power distribution grid data. The key contribution is to account for operational costs, infrastructure condition deterioration, and environmental externalities in the design of socially desirable, sustainable traffic routing mechanisms. We will address questions such as: How should heavy-duty vehicles be routed at scale to find a good trade-off between operational costs, sustainability, and electric power grid constraints?

Background
The transportation sector is the largest contributor to greenhouse gas emissions worldwide (about 24% of CO2 emissions in the EU and about 28% in the USA). The electrification of road transportation presents an opportunity to defer emissions from roads to electric power generation. However, the ambition to achieve zero-emission mobility requires a new, sustainability-oriented approach to transportation planning at a societal scale, respecting infrastructural constraints and individual incentives while being resilient to infrastructure component failures and data uncertainty. A promising approach is using digital traffic routing tools that maximise green energy utilisation while respecting other vital environmental constraints.

Crossdisciplinary collaboration
RoSE is a collaboration between KTH (EECS and ABE school) and MIT (Department of Civil and Environmental Engineering) in Cambridge, USA.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event: