Efficient storage and retrieval of georeferenced objects in a semantic database for web-based applications

The use and dissemination of remotely-sensed data is an important resource that can be used for environmental, commercial and educational purposes. Because of this, the use and availability of remotely-sensed data has increased dramatically in recent years. This usefulness, however, is often overshadowed by the difficulty encountered with trying to deal with this type of data. The amount of data available is immense. Storing, searching and retrieving the data of interest is often difficult, time consuming and inefficient. This is particularly true when these types of data need to be rapidly and continually accessed via the Internet, or combined with other types of remotely-sensed data, such as combining Aerial Photography with US Census vector data. This thesis addresses some of these difficulties, a two-fold approach has been taken. First, a database schema which can store various types of remotely-sensed data in one database has been designed for use in a Semantic Object-Oriented Database System (Sem-ODB). This database schema includes in its design a linear addressing scheme for remotely-sensed objects which maps an object’s 2-dimentional (latitude/longitude) location information to a 1-dimensional integrated integer value. The advantages of using this Semantic schema with remotely-sensed data is discussed and the use of this addressing scheme to rapidly search for and retrieve point-based vector data is investigated. In conjunction with this, an algorithm for transforming a remotely-sensed range search into a number of linear segments of objects in the 1-dimensional array is investigated. The main issues and the combination of solutions involved are discussed

Geographic Information Systems and Science

Geographic information science (GISc) has established itself as a collaborative information-processing scheme that is increasing in popularity. Yet, this interdisciplinary and/or transdisciplinary system is still somewhat misunderstood. This book talks about some of the GISc domains encompassing students, researchers, and common users. Chapters focus on important aspects of GISc, keeping in mind the processing capability of GIS along with the mathematics and formulae involved in getting each solution. The book has one introductory and eight main chapters divided into five sections. The first section is more general and focuses on what GISc is and its relation to GIS and Geography, the second is about location analytics and modeling, the third on remote sensing data analysis, the fourth on big data and augmented reality, and, finally, the fifth looks over volunteered geographic information.info:eu-repo/semantics/publishedVersio