Diwas524 / River-Network-Extraction-from-Satellite-Image-using-UNet-and-Tensorflow

We presented our initial model for detecting rivers and watersheds from satellite pictures in this study, which was based on image processing methodology. The methodology was tested on a set of images obtained from the Sentinel-2 Satellite. For better recognition of rivers and watersheds, a new level of segmentation was used. We obtained a good accuracy using our proposed model, which is significantly higher than other U-net and Tensorflow implemented models available to date. This study could pave the way for future studies on developing water resource management, which is critical for future generations. The suggested methodology is generic in the sense that it can be used to extract various arboreal networks in medical pictures, such as blood vessels.

River-Network-Extraction-from-Satellite-Image-using-UNet-and-Tensorflow

We presented our initial model for detecting rivers and watersheds from satellite pictures in this study, which was based on image processing methodology. The methodology was tested on a set of images obtained from the Sentinel-2 Satellite. For better recognition of rivers and watersheds, a new level of segmentation was used. We obtained a good accuracy using our proposed model, which is significantly higher than other U-net and Tensorflow implemented models available to date.

This study could pave the way for future studies on developing water resource management, which is critical for future generations. The suggested methodology is generic in the sense that it can be used to extract various arboreal networks in medical pictures, such as blood vessels.