Spatial data conflation is the process of combining overlapping spatial datasets to produce a better dataset with higher accuracy or more information. Conflation is needed in many fields, ranging from transportation planning to the analysis of historical datasets, which require the use of multiple data sources. Geospatial data conflation becomes increasingly important with the advancement of GIS and the emergence of new sources of spatial data such as Volunteered Geographic Information.

Conceptually, conflation is a two-step process involving identifying counterpart features that correspond to the same object in reality, and merging the geometry and attributes of counterpart features. In practice, conflation can be performed either manually or with the aid of GIS with varying degrees of automation. Manual conflation is labor-intensive, time consuming and expensive. It is often adopted in practice, nonetheless, due to the lack of reliable automatic conflation methods.

A main challenge of automatic conflation lies in the automatic matching of corresponding features, due to the varying quality and different representations of map data. Many (semi-)automatic feature methods exist. They typically involve measuring the distance between each feature pair and trying to match feature pairs with smaller dissimilarity using a specially designed algorithm or model. Fully automated conflation is still an active research field.

A horizontal (geometric) datum provides accurate coordinates (e.g., latitude and longitude) for points on Earth’s surface. Historically, surveyors developed a datum using optically sighted instruments to manually place intervisible survey marks in the ground. This survey work incorporated geometric principles of baselines, distances, and azimuths through the process of triangulation to attach a coordinate value to each survey mark. Triangulation produced a geodetic network of interconnected survey marks that realized the datum (i.e., connecting the geometry of the network to Earth’s physical surface). For local surveys, these datums provided reasonable positional accuracies on the order of meters. Importantly, once placed in the ground, these survey marks were passive; a new survey was needed to determine any positional changes (e.g., due to plate motion) and to update the attached coordinate values. Starting in the 1950s, due to the implementation of active control, space-based satellite geodesy changed how geodetic networks were realized. Here, "active" implies that a survey mark’s coordinates are updated in near real-time through, for example, artificial satellites such as GNSS. Increasingly, GNSS and satellite geodesy is paving the way for a modernized geometric datum that is global in scope and capable of providing positional accuracies at the millimeter level.

Linear referencing is a term that encompasses a family of concepts and techniques for associating features with a spatial location along a network, rather than referencing those locations to a traditional spherical or planar coordinate system. Linear referencing is used when the location on the network, and the relationships to other locations on the network, are more significant than the location in 2D or 3D space. Linear referencing is commonly used in transportation applications, including roads, railways, and pipelines, although any network structure can be used as the basis for linearly referenced features. Several data models for storing linearly referenced data are available, and well-defined sets of procedures can be used to implement linear referencing for a particular application. As network analysis and network based statistical analysis become more prevalent across disciplines, linear referencing is likely to remain an important component of the data used for such analyses.