2024 |
Gkouvra, Elpida; Betsas, Thodoris; Pateraki, Maria Exploitation of Open Source Datasets and Deep Learning Models for the Detection of Objects in Urban Areas Proceedings Article In: 2024 IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW), pp. 4103-4108, 2024. Abstract | Links | BibTeX | Tags: Cameras, Conferences, Context modeling, Data models, deep learning, deep learning models, Image segmentation, mobile mapping, Object detection, open-source datasets, Training, Transfer learning, Urban areas @inproceedings{10769185,In this work we utilize different open-source datasets and deep learning models for detecting objects from image data captured by a mobile mapping system integrating the multi-camera Ladybug 5+1 in an urban area. In our experiments we exploit sets of pre-trained models and models trained via transfer learning techniques with available open source datasets for object detection, semantic-, instance-, and panoptic segmentation. Tests with the trained models are performed with image data from the Ladybug 5+ camera. |
2020 |
Antoniou, Vyron; Potsiou, Chryssy A deep learning method to accelerate the disaster response process Journal Article In: Remote Sens., vol. 12, no. 3, pp. 544, 2020, ISSN: 20724292. Abstract | Links | BibTeX | Tags: artificial intelligence, deep learning, Deep learning autoencoder, Disaster response management, Helicopter landing site analysis, Machine Learning, Object detection, Satellite imagery, Volunteered geographic information @article{Antoniou2020,This paper presents an end-to-end methodology that can be used in the disaster response process. The core element of the proposed method is a deep learning process which enables a helicopter landing site analysis through the identification of soccer fields. The method trains a deep learning autoencoder with the help of volunteered geographic information and satellite images. The process is mostly automated, it was developed to be applied in a time-and resource-constrained environment and keeps the human factor in the loop in order to control the final decisions. We show that through this process the cognitive load (CL) for an expert image analyst will be reduced by 70%, while the process will successfully identify 85.6% of the potential landing sites. We conclude that the suggested methodology can be used as part of a disaster response process. |
2024 |
Exploitation of Open Source Datasets and Deep Learning Models for the Detection of Objects in Urban Areas Proceedings Article In: 2024 IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW), pp. 4103-4108, 2024. |
2020 |
A deep learning method to accelerate the disaster response process Journal Article In: Remote Sens., vol. 12, no. 3, pp. 544, 2020, ISSN: 20724292. |