Summary of Imafd: An Interpretable Multi-stage Approach to Flood Detection From Time Series Multispectral Data, by Ziyang Zhang et al.
IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data
by Ziyang Zhang, Plamen Angelov, Dmitry Kangin, Nicolas Longépé
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed Interpretable Multi-Stage Approach for Flood Detection (IMAFD) addresses two critical challenges in flood detection: computational expense and lack of interpretable decision-making processes. IMAFD combines dynamic time series analysis with static semantic segmentation to identify potential flooding and provides insight into the decision-making process. The approach consists of four stages: sequence-level suspected image identification, multi-image level change detection, image-level semantic segmentation, and decision making. IMAFD efficiently reduces processing frames for dense change detection and provides interpretable decision-making processes through semantic change detection. Evaluation on three datasets (WorldFloods, RavAEn, and MediaEval) demonstrates competitive performance compared to other methods while offering interpretability and insight. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to detect floods using computers. Floods are important to detect because they can cause damage and affect people’s lives. The new method is called IMAFD and it works by looking at pictures taken from space or the air. It identifies where there might be flooding, and then explains why it thinks that is happening. This is different from other methods that just tell you what is happening, but don’t explain why. The method was tested on three different groups of images and worked well compared to other methods. |
Keywords
» Artificial intelligence » Semantic segmentation » Time series
