As climate change is increasing the frequency and intensity of climate and weather hazards, improving detection and monitoring of flood events is a priority. Being weather independent and high resolution, Sentinel 1 (S1) radar satellite imagery data has become the go-to data source to detect flood events accurately. However, current methods are either based on fixed thresholds to differentiate water from land or train Artificial Intelligence (AI) models based on only S1 data, despite the availability of many other relevant data sources publicly. These models also lack comprehensive validations on out-of-sample data and deployment at scale. In this study, we investigated whether adding extra input layers could increase the performance of AI models in detecting floods fromS1 data. We also provide performance across a range of 11historical events, with results ranging between 0.93 and 0.97accuracy, 0.53 and 0.81 IoU, and 0.68 and 0.89 F1 scores. Finally, we show the infrastructure we developed to deploy ourAI models at scale to satisfy a range of use cases and user requests.