Conflation is a human-assisted process and generally can be broken down into the following common subtasks. Depending on the nature and quality of the data in a specific conflation problem, some of these subtasks may be trivial or not required.
Data Pre-processing-- This step normalizes the input datasets to ensure that they are compatible. For instance, they must have the same coordinate system. This may also involve format translation and any other basic preparation of the datasets.
Data Check-- During this step the internal consistency of the datasets is verified and if necessary improved.
Dataset Alignment-- When datasets are sufficiently misaligned, an initial alignment process is required to allow more precise conflation to be carried out. This alignment is typically coarse grained in nature, not descending to the level of aligning individual features.
Feature Matching-- During this step common features between the datasets are matched. After this phase has been performed the discrepancies between the datasets will have been identified. It is often useful to provide statistical summaries of data quality, or to visualize the discrepancies.
Geometry Alignment removes discrepancies between geometries.
Information Transfer involves updating one dataset with information from the other. This information can be either attributes or geometry to be added to an existing feature, or entire features to be added to the dataset.