Summary of Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network, by Lorenzo Innocenti et al.
Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network
by Lorenzo Innocenti, Giacomo Blanco, Luca Barco, Claudio Rossi
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 novel machine-learning model combines satellite data from Sentinel-3, meteorological predictions, and remote sensing inputs to generate detailed maps forecasting peak temperatures within a 24-hour period in Turin. The study achieves an Mean Absolute Error (MAE) of 2.09 degrees Celsius for the year 2023 at a resolution of 20 meters per pixel, enhancing our understanding of urban microclimates and emphasizing the importance of cross-disciplinary data integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates maps that predict peak temperatures in Turin using satellite data and other information. The model is good at predicting temperature patterns and can help us understand how cities get hotter than surrounding areas. This study shows how combining different types of data can be helpful for making decisions about city planning and keeping people healthy. |
Keywords
» Artificial intelligence » Machine learning » Mae » Temperature
