# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood Datasets  
With the advancement of big data and artificial intelligence, flood disaster management has expanded into new research areas. Supported by various sensor technologies, numerous publicly available datasets have been created to provide foundational information for flood management studies. These datasets have been systematically compiled by scholars such as [PERSON] [125; 126], and building upon this work, we have categorized flood-related datasets into three primary types: single-type datasets, multimodal datasets, and text-image datasets (Table 6). The majority of these datasets focus on the flood event itself, while a smaller portion captures related indicators such as roads and buildings. Satellite imagery predominantly originates from Sentinel-1 and Sentinel-2. Due to the uneven global distribution of social media users and the diversity of languages and posting formats, unified social media datasets related to floods remain limited. However, in recent years, some researchers have developed datasets based on platforms such as Twitter [127]. Additionally, datasets gathered from physical sensors, such as Sen1 Floods11, Sen12-Flood, and FloodNet, are widely recognized by the research community. In contrast, xBD is less commonly used due to its specialized data focus and registration requirements. Furthermore, free satellite datasets containing flood hazard information are available from platforms such as USGS Earth Explorer, Sentinel Copernicus Browser, and NOAA Data Access Viewer. These include data from Landsat 1-9, Sentinel-2, TerraSAR-X, MODIS, AVHRR, and other sensors, all of which are instrumental in flood research and monitoring.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Data Type** & **Datasets** & **Content** \\ \hline \multirow{7}{*}{Single-type} & Sen1 Floods11 & Satellite imagery of 11 flood events across the world between 2017 and 2019 \\ \cline{2-3}  & xBD & Satellite imagery with labeled building damage (United States, India, Nepal, and Bangladesh) \\ \cline{2-3}  & NASA Sentinel-1 & Satellite imagery of 5 flood events between 2017 and 2019 \\ \cline{2-3}  & S1S2-Water & 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary \\ \cline{2-3}  & S1S2-Flood & Satellite imagery of areas that suffered a major flooding event during the 2019 winter \\ \cline{2-3}  & Sen12-Flood & Satellite imagery of flood events between 2018 and 2019 (East Africa, South-west Africa, the Middle-East, and Australia) \\ \cline{2-3}  & UrbanSARFloods & Satellite imagery of 18 flood events across the world \\ \hline \multirow{7}{*}{Roadway flooding image} & Visual images of road conditions during flood events used to train road flood prediction models \\ \cline{2-3}  & Flood Area Segmentation & 290 remote sensing images and self-annotated masks for flood area segmentation \\ \cline{1-1} \cline{2-3}  & FloodNet & High-resolution drone images of the affected areas after Hurricane Harvey \\ \cline{1-1} \cline{2-3}  & & \\ \end{tabular}
\end{table}
Table 6: Publicly available flood-related datasets.