Summary of Deep Learning For Spatio-temporal Fusion in Land Surface Temperature Estimation: a Comprehensive Survey, Experimental Analysis, and Future Trends, by Sofiane Bouaziz et al.
Deep Learning for Spatio-Temporal Fusion in Land Surface Temperature Estimation: A Comprehensive Survey, Experimental Analysis, and Future Trends
by Sofiane Bouaziz, Adel Hafiane, Raphael Canals, Rachid Nedjai
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a comprehensive review of deep learning (DL) based Spatio-Temporal Fusion (STF) techniques for estimating Land Surface Temperature (LST). The paper highlights the importance of LST data in understanding key environmental processes and discusses the trade-off between spatial and temporal resolutions in satellite sensors. It also introduces a novel taxonomy for DL-based STF methods and presents a benchmark dataset for LST estimation. The authors analyze recent advancements, mathematically formulate the STF problem, and conduct extensive experiments to support their findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computer vision techniques to improve temperature data from satellites. It’s like taking two blurry photos of the same place at different times, and then combining them to get a clear picture of what the temperature looked like over time. The authors look at how other researchers have used this technique in the past and share their own ideas for making it better. They also provide a big set of data that others can use to test their own techniques. |
Keywords
» Artificial intelligence » Deep learning » Temperature
