This paper presents a survey of quadtree and related hierarchical data structures. These structures are based on the principle of recursive decomposition and are used for representing data in image processing, computer graphics, geographic information systems, and robotics. The focus is on region data (two-dimensional shapes) and to a lesser extent on point, curvilinear, and three-dimensional data. The paper discusses various operations that can be performed using these data structures. The quadtree is a hierarchical data structure that represents data by recursively dividing space into quadrants. It is used to represent region data, where each block is either entirely inside or entirely outside the region. The quadtree can be characterized as a variable resolution data structure. The paper also discusses other hierarchical data structures, such as point quadtrees, k-d trees, and strip trees, and their applications in different domains. The paper discusses various operations that can be performed using quadtrees, including neighbor-finding techniques, conversion, set operations, transformations, areas and moments, connected component labeling, perimeter, component counting, space requirements, skeletons and medial axis transforms, pyramids, quadtree approximation methods, and volume data. The paper also discusses alternative ways to represent quadtrees, such as using a bintree structure, and the use of pointerless quadtree representations. The paper concludes that hierarchical data structures are useful for representing data in a way that allows efficient processing of operations such as set operations and image processing. The quadtree is particularly useful for representing region data, and its use is discussed in the context of various applications, including geographic information systems, computer graphics, and robotics. The paper also discusses the use of quadtrees in image compression and coding, and the relationship between quadtrees and other hierarchical data structures.This paper presents a survey of quadtree and related hierarchical data structures. These structures are based on the principle of recursive decomposition and are used for representing data in image processing, computer graphics, geographic information systems, and robotics. The focus is on region data (two-dimensional shapes) and to a lesser extent on point, curvilinear, and three-dimensional data. The paper discusses various operations that can be performed using these data structures. The quadtree is a hierarchical data structure that represents data by recursively dividing space into quadrants. It is used to represent region data, where each block is either entirely inside or entirely outside the region. The quadtree can be characterized as a variable resolution data structure. The paper also discusses other hierarchical data structures, such as point quadtrees, k-d trees, and strip trees, and their applications in different domains. The paper discusses various operations that can be performed using quadtrees, including neighbor-finding techniques, conversion, set operations, transformations, areas and moments, connected component labeling, perimeter, component counting, space requirements, skeletons and medial axis transforms, pyramids, quadtree approximation methods, and volume data. The paper also discusses alternative ways to represent quadtrees, such as using a bintree structure, and the use of pointerless quadtree representations. The paper concludes that hierarchical data structures are useful for representing data in a way that allows efficient processing of operations such as set operations and image processing. The quadtree is particularly useful for representing region data, and its use is discussed in the context of various applications, including geographic information systems, computer graphics, and robotics. The paper also discusses the use of quadtrees in image compression and coding, and the relationship between quadtrees and other hierarchical data structures.