Techniques for effective and efficient fire detection from social media images

Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFDnR), which combines feature extractor and evaluation functions to support instance-based learning, (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -- the FlickrFire dataset, and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFDnR was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.Comment: 12 pages, Proceedings of the International Conference on Enterprise Information Systems. Specifically: Marcos Bedo, Gustavo Blanco, Willian Oliveira, Mirela Cazzolato, Alceu Costa, Jose Rodrigues, Agma Traina, Caetano Traina, 2015, Techniques for effective and efficient fire detection from social media images, ICEIS, 34-4

FR-H3: a new QTL to assist in the development of fall-sown barley with superior low temperature tolerance

Fall-sown barley will be increasingly important in the era of climate change due to higher yield potential and efficient use of water resources. Resistance/tolerance to abiotic stresses will be critical, and foremost among the abiotic stresses is low temperature. Simultaneous gene discovery and breeding will accelerate the development of agronomically relevant fall-sown barley germplasm with resistance to low temperature. We developed two doubled haploid mapping populations using two lines from the University of Nebraska (NE) and one line from Oregon State University (OR): NB3437f/OR71 (facultative × facultative) and NB713/OR71 (winter × facultative). Both were genotyped with a custom 384 oligonucleotide pool assay (OPA). QTL analyses were performed for low temperature tolerance (LTT) and vernalization sensitivity (VS). The role of VRN-H2 in VS was confirmed and a novel alternative winter allele at VRN-H3 was discovered in the Nebraska germplasm. FR-H2 was identified as a probable determinant of LTT and a new QTL, FR-H3, was discovered on chromosome 1H that accounted for up to 48 % of the phenotypic variation in field survival at St. Paul, MN, USA. The discovery of FR-H3 is a significant advancement in barley LTT genetics and will assist in developing the next generation of fall-sown varieties. © 2012 Springer-Verlag Berlin Heidelberg