A NEW GEOGRAPHIC CONTEXT MEASURE TO SIMILARITY ASSESSMENT BASED ON THE SHAPE CONTEXT DESCRIPTOR
Nowadays there are many geospatial datasets available for the same area. This large availability is derivative from the advances in remote sensing processes, which includes the popularization of drones and the increasing number of satellite platforms. These data are built by distinct producers, with different requirements. It is fair presume that a more complete version of these datasets can be created using data integration techniques. Among them we can find the map conflation methods, in which the first phase often begins with an alignment between the datasets assessed. The procedure of find these correspondences between geospatial datasets is called matching. In this study we present a new geographic context measure that can be used to implement a new matching method at the feature level. This new measure is based on the shape context descriptor proposed by Belongie. The experiments showed that our approach is a feasible solution, which is less sensible to data disturbance then other traditional methods.