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Fedorov, Igor
Publications (2 of 2) Show all publications
Vrotsou, K., Navarra, C., Kucher, K., Fedorov, I., Schück, F., Unger, J. & Neset, T.-S. (2023). Towards a Volunteered Geographic Information-Facilitated Visual Analytics Pipeline to Improve Impact-Based Weather Warning Systems. Atmosphere, 14(7), Article ID 1141.
Open this publication in new window or tab >>Towards a Volunteered Geographic Information-Facilitated Visual Analytics Pipeline to Improve Impact-Based Weather Warning Systems
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2023 (English)In: Atmosphere, E-ISSN 2073-4433, Vol. 14, no 7, article id 1141Article in journal (Refereed) Published
Abstract [en]

Extreme weather events, such as flooding, are expected to increase in frequency and intensity. Therefore, the prediction of extreme weather events, assessment of their local impacts in urban environments, and implementation of adaptation measures are becoming high-priority challenges for local, regional, and national agencies and authorities. To manage these challenges, access to accurate weather warnings and information about the occurrence, extent, and impacts of extreme weather events are crucial. As a result, in addition to official sources of information for prediction and monitoring, citizen volunteered geographic information (VGI) has emerged as a complementary source of valuable information. In this work, we propose the formulation of an approach to complement the impact-based weather warning system that has been introduced in Sweden in 2021 by making use of such alternative sources of data. We present and discuss design considerations and opportunities towards the creation of a visual analytics (VA) pipeline for the identification and exploration of extreme weather events and their impacts from VGI texts and images retrieved from social media. The envisioned VA pipeline incorporates three main steps: (1) data collection, (2) image/text classification and analysis, and (3) visualization and exploration through an interactive visual interface. We envision that our work has the potential to support three processes that involve multiple stakeholders of the weather warning system: (1) the validation of previously issued warnings, (2) local and regional assessment-support documentation, and (3) the monitoring of ongoing events. The results of this work could thus generate information that is relevant to climate adaptation decision making and provide potential support for the future development of national weather warning systems.

Place, publisher, year, edition, pages
MDPI, 2023
weather warning systems, flooding, volunteered geographic information, visualization, visual analytics, artificial intelligence, machine learning, natural language processing, classification, social media
National Category
Computer and Information Sciences
urn:nbn:se:liu:diva-196332 (URN)10.3390/atmos14071141 (DOI)001037893300001 ()
Vinnova, 2020-03388

This research was funded by Sweden's Innovation Agency, VINNOVA, grant number 2020-03388, 'AI for Climate Adaptation'.

Available from: 2023-07-18 Created: 2023-07-18 Last updated: 2024-07-04
Alqaysi, H., Fedorov, I., Qureshi, F. Z. & ONils, M. (2021). A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms. Journal of imaging, 7(11), Article ID 277.
Open this publication in new window or tab >>A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms
2021 (English)In: Journal of imaging, ISSN 2313-433X, Vol. 7, no 11, article id 277Article in journal (Refereed) Published
Abstract [en]

Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets-(1) Klim and (2) Skagen-collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.

Place, publisher, year, edition, pages
MDPI, 2021
YOLOv4; background subtraction; bird detection; sky surveillance; wind farms monitoring
National Category
Other Computer and Information Science
urn:nbn:se:liu:diva-185168 (URN)10.3390/jimaging7110227 (DOI)34821858 (PubMedID)2-s2.0-85118110304 (Scopus ID)

Funding agencies:  Knowledge foundation, the Next generation IndustrialIoT project. 

Available from: 2022-05-18 Created: 2022-05-18 Last updated: 2022-06-10

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