# Unsupervised Flood Detection on SAR Time Series

arXiv: https://arxiv.org/abs/2212.03675 · HTML: https://mlsanity.com/paper/2212.03675/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 17 of 17)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding](https://mlsanity.com/paper/2011.14097/) (2020)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations](https://mlsanity.com/paper/1805.12219/) (2018)
- [Unsupervised Change Detection Based on Image Reconstruction Loss](https://mlsanity.com/paper/2204.01200/) (2022)
- [Unsupervised Image Regression for Heterogeneous Change Detection](https://mlsanity.com/paper/1909.05948/) (2019)
- [Self-Supervised Multisensor Change Detection](https://mlsanity.com/paper/2103.05102/) (2021)
- [Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images](https://mlsanity.com/paper/2004.07011/) (2020)
- [Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data](https://mlsanity.com/paper/2204.09387/) (2022)
- [Unsupervised Change Detection of Extreme Events Using ML On-Board](https://mlsanity.com/paper/2111.02995/) (2021)

## Built on by (most cited first, 6 of 6)

- [Kuro Siwo: 33 billion m2 under the water. A global multi-temporal satellite dataset for rapid flood mapping](https://mlsanity.com/paper/2311.12056/) (2023)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [DAM-Net: Global Flood Detection from SAR Imagery Using Differential Attention Metric-Based Vision Transformers](https://mlsanity.com/paper/2306.00704/) (2023)
- [FloodLense: A Framework for ChatGPT-based Real-time Flood Detection](https://mlsanity.com/paper/2401.15501/) (2024)
- [A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series](https://mlsanity.com/paper/2307.01378/) (2023)
- [Context-Aware Change Detection With Semi-Supervised Learning](https://mlsanity.com/paper/2306.08935/) (2023)

## Abstract (arXiv)

Human civilization has an increasingly powerful influence on the earth system. Affected by climate change and land-use change, natural disasters such as flooding have been increasing in recent years. Earth observations are an invaluable source for assessing and mitigating negative impacts. Detecting changes from Earth observation data is one way to monitor the possible impact. Effective and reliable Change Detection (CD) methods can help in identifying the risk of disaster events at an early stage. In this work, we propose a novel unsupervised CD method on time series Synthetic Aperture Radar~(SAR) data. Our proposed method is a probabilistic model trained with unsupervised learning techniques, reconstruction, and contrastive learning. The change map is generated with the help of the distribution difference between pre-incident and post-incident data. Our proposed CD model is evaluated on flood detection data. We verified the efficacy of our model on 8 different flood sites, including three recent flood events from Copernicus Emergency Management Services and six from the Sen1Floods11 dataset. Our proposed model achieved an average of 64.53% Intersection Over Union(IoU) value and 75.43% F1 score. Our achieved IoU score is approximately 6-27% and F1 score is approximately 7-22% better than the compared unsupervised and supervised existing CD methods. The results and extensive discussion presented in the study show the effectiveness of the proposed unsupervised CD method.