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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Multi-Modal Self-Supervised Representation Learning For Earth Observation, Pallavi Jain, Bianca Schoen Phelan, Robert J. Ross
Multi-Modal Self-Supervised Representation Learning For Earth Observation, Pallavi Jain, Bianca Schoen Phelan, Robert J. Ross
Conference papers
Self-Supervised learning (SSL) has reduced the performance gap between supervised and unsupervised learning, due to its ability to learn invariant representations. This is a boon to the domains like Earth Observation (EO), where labelled data availability is scarce but unlabelled data is freely available. While Transfer Learning from generic RGB pre-trained models is still common-place in EO, we argue that, it is essential to have good EO domain specific pre-trained model in order to use with downstream tasks with limited labelled data. Hence, we explored the applicability of SSL with multi-modal satellite imagery for downstream tasks. For this we utilised …
Automatic Flood Detection In Sentinei-2 Images Using Deep Convolutional Neural Networks, Pallavi Jain, Bianca Schoen-Phelan, Robert J. Ross
Automatic Flood Detection In Sentinei-2 Images Using Deep Convolutional Neural Networks, Pallavi Jain, Bianca Schoen-Phelan, Robert J. Ross
Conference papers
The early and accurate detection of floods from satellite imagery can aid rescue planning and assessment of geophysical damage. Automatic identification of water from satellite images has historically relied on hand-crafted functions, but these often do not provide the accuracy and robustness needed for accurate and early flood detection. To try to overcome these limitations we investigate a tiered methodology combining water index like features with a deep convolutional neural network based solution to flood identification against the MediaEval 2019 flood dataset. Our method builds on existing deep neural network methods, and in particular the VGG16 network. Specifically, we explored …
The Application Of Web 2.0 Technologies As An Experimental Method Of Teaching Remote Sensing At Dit, Ireland, Avril Behan
The Application Of Web 2.0 Technologies As An Experimental Method Of Teaching Remote Sensing At Dit, Ireland, Avril Behan
Conference Papers
This paper describes the implementation of an experimental method of delivery for a module of remote sensing material to second year students on the BSc (Hons) in Geomatics at the Dublin Institute of Technology. The project began as a reaction to poor levels of student engagement and unsatisfactory grades, as well as the requirement to focus more on real-world type problems due to the implementation of a work-placement semester for third year students. Both pedagogical considerations (movement towards formative feedback, interactivity and group-based work) and the effect of technological drivers, such as the popularity of the internet in general and …