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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.