Datasets generated in the study

This observational study employs an innovative deep learning framework to map river obstructions globally, overcoming the limitations of traditional detection methods by identifying various categories such as dams, low-head dams, locks, and partial dams. The primary dataset, known as DL-GROD (Deep Learning-Global River Obstructions Database), includes over 50,000 records of artificial river obstructions verified through meticulous visual inspection using Google Earth imagery. The available DL-GROD on this platform consists of distinct samples delineated as square or rectangular segments on the map, rather than the entirety of the dataset.