The current information age has led, globally, to an exponential increase regarding the availability and the use of the information, both structured and unstructured, a phenomenon known as Big Data. The term Big Data refers not only to the massive volume and variety of data itself, but also to the set of technologies that surround it, in order to collect, store, retrieve, manage, process and analyze data in order to solve complex problems in society, respectively for increasing the quality of life in all its aspects. Given that approximately 80% of the data generated daily has a spatial component, and studies indicate that more than 150 zettabytes (150 trillion gigabytes) of data will require analysis by 2025, it is necessary to create Big Data solutions for storage, organizing, manipulating, viewing, and retrieving relevant information. Today, in the midst of the �data revolution�, more and more countries are launching ambitious programs aimed at developing their use. These programs test the ability of decisionmakers to recognize, structure and exploit data, which is considered a valuable resource, and create the means to generate value from it by facilitating access. The Big Data phenomenon has also conquered the military field, in which the current and emerging object of large-scale data analysis areas is the exploitation of classical techniques such as rule-based systems, shape analysis, tree structures and other analysis technologies in order to develop efficient tools. In this paper we will start from the investigation of the basic characteristics of Big Data and continued with technical details that involves the generation, collection, storage and analysis of geospatial Big Data needed to transform these data into an actionable intelligence.
Nowadays data are generated by different sources, at an incredible rate, and the traditional approaches for their collection, storage and analysis are not suitable. Big Data is analyzed and used by state institutions, business environment, transportation, health, communications, banking system, utilities, defense and other components of modern society in order to support their decisions as well as the human activities. Geospatial data is an important component of Big Data and aerial/satellite images offer a lot of details about the environment, events and their evolution in time. This paper presents the significant techniques used in three main stages of Geospatial Big Data lifecycle, namely data collection, storage and analysis. Geospatial data collections are mainly executed by using web crawlers in order to find meaningful data and, during this stage some preprocessing operations can be done (standardization, completion and integration). Cloud storage and distributed file systems are widely used for Geospatial Big Data storage and new types of non-relational and relational databases are developed. The very challenging aspects for Big Data analysis are related to feature identification and extraction from aerial or satellite images, using feature-based extraction and deep learning algorithms.
Nowadays are widely used geospatial information-based software applications, among which navigation systems on vehicles, systems monitoring and management of traffic etc., requiring the use of accurate, complete and up-to-date roads, stored in different types of databases. There are many automated or semi-automated algorithms used for road extraction, but in this article is presented a new semi-automated algorithm for extracting roads from high resolution aerial and satellite images based on the weighted correlation of transverse profiles. The algorithm uses, as initial data, two starting points from which one obtains the path's orientation and template profile. Also, the operator must set threshold value of correlation coefficient between cross profiles, search distance, search angle, length of transverse profile and maximum number of rejections. Compared to other semi-automated road extraction algorithms, this algorithm is less sensitive to radiometric changes at the ends of the profile due to the assignment of higher weights to central pixels.
The topic of change detection of different topographic features on the field using aerial images or satellite high-resolution imagery in digital format is very important for the military domain as well as for the civilian one (agriculture, forestry, natural disaster management, etc.). In this paper the authors treat some methods used for automated detection of changes on the terrain based on panchromatic aerial photographs (gray levels, 8 bits for color representation) acquired at different dates. One method is implemented in ERDAS Imagine software and the others were developed empirically by a third party. A new method for change detection is proposed by the authors, using Sobel edge detection operator, obtaining thus clearer contours of modified elements on the ground. The quality of the resulting modified elements depends very much on technical characteristics of compared images such as collection dates, atmospheric conditions during the capturing process, similarity of radiometry, spatial resolution and precise overlay, as well as the presence of clouds or shadows of topographic details on the images.
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