@inproceedings{MTMT:34504694,
	title = {Active Wildfires Detection and Dynamic Escape Routes Planning for Humans through Information Fusion between Drones and Satellites},
	url = {https://m2.mtmt.hu/api/publication/34504694},
	author = {Liu, Chang and Szirányi, Tamás},
	booktitle = {26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023},
	doi = {10.1109/ITSC57777.2023.10421956},
	unique-id = {34504694},
	year = {2023},
	pages = {1977-1982},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@CONFERENCE{MTMT:34504666,
	title = {Radiation Plan Optimization for UV-C Disinfection Robots},
	url = {https://m2.mtmt.hu/api/publication/34504666},
	author = {Bugár-Mészáros, Barnabás and Majdik, András and Rózsa, Zoltán and Szirányi, Tamás},
	booktitle = {Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája},
	unique-id = {34504666},
	year = {2023},
	pages = {1-4},
	orcid-numbers = {Majdik, András/0000-0003-1807-2865; Szirányi, Tamás/0000-0003-2989-0214}
}

@CONFERENCE{MTMT:34504606,
	title = {LIDAR mérések időbeli felskálázása mono kamera alapján},
	url = {https://m2.mtmt.hu/api/publication/34504606},
	author = {Rózsa, Zoltán and Szirányi, Tamás},
	booktitle = {Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája},
	unique-id = {34504606},
	year = {2023},
	pages = {1-13},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@CONFERENCE{MTMT:34504590,
	title = {Towards modelling an atmospheric pseudo-satellite imaging system},
	url = {https://m2.mtmt.hu/api/publication/34504590},
	author = {Golarits, Marcell and Tizedes, László and Majdik, András and Szirányi, Tamás},
	booktitle = {Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája},
	unique-id = {34504590},
	year = {2023},
	pages = {1-5},
	orcid-numbers = {Golarits, Marcell/0000-0001-9652-4148; Majdik, András/0000-0003-1807-2865; Szirányi, Tamás/0000-0003-2989-0214}
}

@inproceedings{MTMT:34504535,
	title = {Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke},
	url = {https://m2.mtmt.hu/api/publication/34504535},
	author = {Liu, Chang and Szirányi, Tamás},
	booktitle = {2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)},
	doi = {10.1109/SITIS61268.2023.00077},
	unique-id = {34504535},
	abstract = {In recent years, the increasing prevalence and intensity of wildfires have posed significant challenges to emergency response teams. The utilization of unmanned aerial vehicles (UAVs), commonly known as drones, has shown promise in aiding wildfire management efforts. This work focuses on the development of an optimal wildfire escape route planning system specifically designed for drones, considering dynamic fire and smoke models. First, the location of the source of the wildfire can be well located by information fusion between UAV and satellite, and the road conditions in the vicinity of the fire can be assessed and analyzed using multichannel remote sensing data. Second, the road network can be extracted and segmented in real time using UAV vision technology, and each road in the road network map can be given priority based on the results of road condition classification. Third, the spread model of dynamic fires calculates the new location of the fire source based on the fire intensity, wind speed and direction, and the radius increases as the wildfire spreads. Smoke is generated around the fire source to create a visual representation of a burning fire. Finally, based on the improved A∗ algorithm, which considers all the above factors, the UAV can quickly plan an escape route based on the starting and destination locations that avoid the location of the fire source and the area where it is spreading. By considering dynamic fire and smoke models, the proposed system enhances the safety and efficiency of drone operations in wildfire environments. © 2023 IEEE.},
	year = {2023},
	pages = {429-434},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@inproceedings{MTMT:34473012,
	title = {Terrain Depth Estimation for Improved Inertial Data Prediction in Autonomous Navigation Systems},
	url = {https://m2.mtmt.hu/api/publication/34473012},
	author = {Markó, Norbert and Szirányi, Tamás and Ballagi, Áron},
	booktitle = {2023 IEEE International Automated Vehicle Validation Conference (IAVVC)},
	doi = {10.1109/IAVVC57316.2023.10328139},
	unique-id = {34473012},
	year = {2023},
	pages = {1-6},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@article{MTMT:34223038,
	title = {Teaching to Survive: A Citizen-centred Disaster Preparedness Project},
	url = {https://m2.mtmt.hu/api/publication/34223038},
	author = {Majdik, András and Székely, Z and Keszler, Anita and Márkus, Zsolt László and Szirányi, Tamás},
	journal-iso = {ERCIM NEWS},
	journal = {ERCIM NEWS},
	unique-id = {34223038},
	issn = {0926-4981},
	year = {2023},
	eissn = {1564-0094},
	pages = {16-17},
	orcid-numbers = {Majdik, András/0000-0003-1807-2865; Szirányi, Tamás/0000-0003-2989-0214}
}

@inproceedings{MTMT:34107692,
	title = {Collaborative Visual-Inertial Localization of Teams With Floorplan Extraction},
	url = {https://m2.mtmt.hu/api/publication/34107692},
	author = {Gazdag, Sándor and Pásztornicky, Dániel and Jankó, Zsolt and Szirányi, Tamás and Majdik, András},
	booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
	doi = {10.1109/ICASSPW59220.2023.10192967},
	unique-id = {34107692},
	year = {2023},
	pages = {1-5},
	orcid-numbers = {Gazdag, Sándor/0000-0002-1983-6460; Jankó, Zsolt/0000-0002-3739-9874; Szirányi, Tamás/0000-0003-2989-0214; Majdik, András/0000-0003-1807-2865}
}

@article{MTMT:33822096,
	title = {Optical Flow and Expansion Based Deep Temporal Up-Sampling of LIDAR Point Clouds},
	url = {https://m2.mtmt.hu/api/publication/33822096},
	author = {Rózsa, Zoltán and Szirányi, Tamás},
	doi = {10.3390/rs15102487},
	journal-iso = {REMOTE SENS-BASEL},
	journal = {REMOTE SENSING},
	volume = {15},
	unique-id = {33822096},
	abstract = {This paper proposes a framework that enables the online generation of virtual point clouds relying only on previous camera and point clouds and current camera measurements. The continuous usage of the pipeline generating virtual LIDAR measurements makes the temporal up-sampling of point clouds possible. The only requirement of the system is a camera with a higher frame rate than the LIDAR equipped to the same vehicle, which is usually provided. The pipeline first utilizes optical flow estimations from the available camera frames. Next, optical expansion is used to upgrade it to 3D scene flow. Following that, ground plane fitting is made on the previous LIDAR point cloud. Finally, the estimated scene flow is applied to the previously measured object points to generate the new point cloud. The framework’s efficiency is proved as state-of-the-art performance is achieved on the KITTI dataset.},
	year = {2023},
	eissn = {2072-4292},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}

@inproceedings{MTMT:33775933,
	title = {A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal},
	url = {https://m2.mtmt.hu/api/publication/33775933},
	author = {Zhu, Morui and Liu, Chang and Szirányi, Tamás},
	booktitle = {IMPROVE 2023 : Proceedings of the 3rd International Conference on Image Processing and Vision Engineering},
	doi = {10.5220/0012039600003497},
	unique-id = {33775933},
	abstract = {Due to the inevitable contamination of thick clouds and their shadows, satellite images are greatly affected, which significantly reduces the usability of data from satellite images. Therefore, obtaining high-quality image data without cloud contamination in a specific area and at the time we need it is an important issue. To address this problem, we collected a new multi-temporal dataset covering the entire globe, which is used to remove clouds and their shadows. Since generative adversarial networks (GANs) perform well in conditional image synthesis challenges, we utilized a spatial-temporal GAN (STGAN) to eliminate clouds and their shadows in optical satellite images. As a baseline model, STGAN demonstrated outstanding performance in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), achieving scores of 33.4 and 0.929, respectively. The cloud-free images generated in this work have significant utility for various downstream applications in real-world environments. Dataset is publicly available: https://github.com/zhumorui/SMT-CR},
	year = {2023},
	pages = {206-212},
	orcid-numbers = {Szirányi, Tamás/0000-0003-2989-0214}
}