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Abstract Atmospheric processes involve both space and time. Thus, humans looking at atmospheric imagery can often spot important signals in an animated loop of an image sequence not apparent in an individual (static) image. Utilizing such signals with automated algorithms requires the ability to identify complex spatiotemporal patterns in image sequences. That is a very challenging task due to the endless possibilities of patterns in both space and time. Here, we review different concepts and techniques that are useful to extract spatiotemporal signals from meteorological image sequences to expand the effectiveness of AI algorithms for classification and prediction tasks. We first present two applications that motivate the need for these approaches in meteorology, namely the detection of convection from satellite imagery and solar forecasting. Then we provide an overview of concepts and techniques that are helpful for the interpretation of meteorological image sequences, such as (a) feature engineering methods using (i) meteorological knowledge, (ii) classic image processing, (iii) harmonic analysis, and (iv) topological data analysis; (b) ways to use convolutional neural networks for this purpose with emphasis on discussing different convolution filters (2D/3D/LSTM-convolution); and (c) a brief survey of several other concepts, including the concept of “attention” in neural networks and its utility for the interpretation of image sequences and strategies from self-supervised and transfer learning to reduce the need for large labeled datasets. We hope that presenting an overview of these tools—many of which are not new but underutilized in this context—will accelerate progress in this area.
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Gearing up for the 21st century space race
A new space race is imminent, with several industry players working towards satellite-based Internet connectivity. While satellite networks are not themselves new, these recent proposals are aimed at orders of magnitude higher bandwidth and much lower latency, with constellations planned to comprise thousands of satellites. These are not merely far future plans — the first satellite launches have already commenced, and substantial planned capacity has already been sold. It is thus critical that networking researchers engage actively with this research space, instead of missing what may be one of the most significant modern developments in networking. In our first steps in this direction, we find that this new breed of satellite networks could potentially compete with today’s ISPs in many settings, and in fact offer lower latencies than present fiber infrastructure over long distances. We thus elucidate some of the unique challenges these networks present at virtually all layers, from topology design and ISP economics, to routing and congestion control.
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- PAR ID:
- 10140456
- Date Published:
- Journal Name:
- ACM Workshop on Hot Topics in Networks
- Page Range / eLocation ID:
- 113 to 119
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3286062.3286079
