Correcting errors in visually interpreted land use data — An machine learning approach

As manually correcting error in visual interpretation data is both costly and labor intensive, the automated error detection and correction approaches is in need. In this paper, a prototype method is developed to detect and correct errors in visually interpreted landuse data. This method involves image segmentation, anomaly detection and decision tree classification techniques. The method is tested on landuse dataset with known accuracy (95%-50%). Result shows that the accuracy of landuse data can be greatly improved by the error correction method, and the approach can be practical when the accuracy of the interpretation data is no less than 70%.