A new least squares adjustment method for map conflation

Digital map conflation is a methodology to reconcile discrepancies between two datasets to get a new and consistent dataset. The new dataset may have high positional accuracy or rich attributes. Conflating urban maps needs to reconcile coordinate discrepancies and simultaneously preserve some characteristics of features. This should depend on various departments' requirements. This paper introduces an least square adjustment approach for map conflation. It regards those demands as different observations and applies parameter adjustment method to solve the equation system. This method is tested on some maps and compared with other popular methods. The test results show that this approach can bring about good result on positioning features and has an excellent ability to maintain characteristics of features.