AI and remote sensing for biodiversity and global change
Global change and the loss of biodiversity are one of the major challenges that threat human well-being at present. Detecting, understanding and monitoring spatiotemporal changes in the earth surface from the local to the global scale are of paramount importance for researchers and policy makers all around the world. Currently, one of the main resources for conducting such studies is remotely sensed data, from ground-based sensors to aerial and satellite imagery. Daily, Terabytes of data are produced at different spatial, spectral, radiometric and temporal resolutions. Extracting useful patterns from this type of data is very complex. Artificial intelligence algorithms in general and deep learning models in particular have a high potential to detect complex patterns in this type of data. Our research in this topic is fusing remote sensing and IA technologies with the aim of contributing to the Sustainable Development Goals.
Responsible: Siham Tabik
|Herrera Triguero, Francisco||herrera@P4G08pECfRIqdecsai.ugr.es||DaSCI Technology Applications Area, Data Science and Big Data Area, Computational Intelligence Area||PhD|
|Villar Castro, Pedro||pvillarc@ugrSZCB6FM9B.es||Data Science and Big Data Area||PhD|
|Gutiérrez Salcedo, Salvador||salvador.gutierrez@uca6n_IA0_NFZ.es||Data Science and Big Data Area||PhD|
|Tabik, Siham||sihamMQ.iO3Au@ugr.es||Data Science and Big Data Area||PhD - Ramón y Cajal|