Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Article de journal
Dans: IEEE Open Journal of the Computer Society, vol. 7, p. 492–503, 2026, ISSN: 26441268 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadeh_vision-based_2026,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029942397&doi=10.1109%2FOJCS.2026.3663268&partnerID=40&md5=b11484e035458c84b1d3f6780b92c91c},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
2.
Floyd, M. W.; Davoust, A.; Esfandiari, B.
Considerations for real-time spatially-aware case-based reasoning: A case study in robotic soccer imitation Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5239 LNAI, p. 195–209, 2008, ISSN: 03029743, (ISBN: 3540855017; 9783540855019 Place: Trier).
Résumé | Liens | BibTeX | Étiquettes: Aware systems, Case based reasoning, Case bases, Case studies, Case-base reasoning, European, Feature selection, Mobile robotic, Prototyping, Real-time constraints, RoboCup soccer, Robotic soccer, Robotics, Spatial environments, Time frames
@article{floyd_considerations_2008,
title = {Considerations for real-time spatially-aware case-based reasoning: A case study in robotic soccer imitation},
author = {M. W. Floyd and A. Davoust and B. Esfandiari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-52449121574&doi=10.1007%2f978-3-540-85502-6_13&partnerID=40&md5=cba3038fb365eb8aa7088030a41dde83},
doi = {10.1007/978-3-540-85502-6_13},
issn = {03029743},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {5239 LNAI},
pages = {195–209},
abstract = {Case-base reasoning in a real-time context requires the system to output the solution to a given problem in a predictable and usually very fast time frame. As the number of cases that can be processed is limited by the real-time constraint, we explore ways of selecting the most important cases and ways of speeding up case comparisons by optimizing the representation of each case. We focus on spatially-aware systems such as mobile robotic applications and the particular challenges in representing the systems' spatial environment. We select and combine techniques for feature selection, clustering and prototyping that are applicable in this particular context and report results from a case study with a simulated RoboCup soccer-playing agent. Our results demonstrate that preprocessing such case bases can significantly improve the imitative ability of an agent. © Springer-Verlag Berlin Heidelberg 2008.},
note = {ISBN: 3540855017; 9783540855019
Place: Trier},
keywords = {Aware systems, Case based reasoning, Case bases, Case studies, Case-base reasoning, European, Feature selection, Mobile robotic, Prototyping, Real-time constraints, RoboCup soccer, Robotic soccer, Robotics, Spatial environments, Time frames},
pubstate = {published},
tppubtype = {article}
}
Case-base reasoning in a real-time context requires the system to output the solution to a given problem in a predictable and usually very fast time frame. As the number of cases that can be processed is limited by the real-time constraint, we explore ways of selecting the most important cases and ways of speeding up case comparisons by optimizing the representation of each case. We focus on spatially-aware systems such as mobile robotic applications and the particular challenges in representing the systems' spatial environment. We select and combine techniques for feature selection, clustering and prototyping that are applicable in this particular context and report results from a case study with a simulated RoboCup soccer-playing agent. Our results demonstrate that preprocessing such case bases can significantly improve the imitative ability of an agent. © Springer-Verlag Berlin Heidelberg 2008.