The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

/pdf/linked-data-the-story-so-far-2a9zvmaysa.pdf

Linked Data - the story so far

http://www.geog.ucl.ac.uk/~mdisney/teaching/PPRS/papers/foody.pdf

Status of land cover classification accuracy assessment

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

/pdf/change-detection-techniques-4g34rgajio.pdf

Change detection techniques

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

/pdf/a-survey-of-image-classification-methods-and-techniques-for-5bc5ltr9dq.pdf

A survey of image classification methods and techniques for improving classification performance

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

The following issues relate to quality assessment of image classification: the classification methods as such, the methods to evaluate the classification results, and the requirements of the application. In this paper, a number of evaluation methods are reviewed, and it is concluded that those based on confusion matrices and the KHAT analysis are the most suited if one is interested in comparing classifiers. The novelty of this paper is that much attention is given to the subjectivity present in every evaluation scheme, and that the concept of accuracy is extended to quality by creating the link between accuracy, objectives, and costs. A protocol is proposed for quality assessment related to the economical reality. An example based on a hypothetical data set shows that the economic cost of misclassification can be high, and that it may be advantageous for the user to reconsider either the objectives, the type of data used, or other aspects of the remote-sensing system that he uses to produce the map.

Quality assessment of image classification algorithms for land-cover mapping

Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach

The study of the relations between environmental changes and socioeconomic situations is of great relevance to the definition of the impact that national policies have on the environment. In order to understand such relations, in this work, we have taken into consideration those changes taking place on a provincial scale in the period going from 1975 to 1992. To this end, both thematic and socioeconomic data have been taken into account, with the purpose of finding a correlation between the natural environment on the one hand and the anthropic environment on the other.

An > data fusion

The European geoportal––one step towards the establishment of a European Spatial Data Infrastructure

In the literature, multiple classifier systems (MCSs) have proved to be a valuable approach to combining classifiers, and under some conditions MCSs are able to mimic ideal Bayesian labeling. This paper focuses on the family of MCSs based on dynamic classifier selection (DCS) and proposes a modification to dynamic classifier selection by local accuracy (DCS-LA). Experiments show that the proposed method outperform MCS strategies based on belief functions and the DCS-LA in terms of minimum and maximum class accuracies and kappa coefficient of agreement and is a valid alternative to majority voting. Moreover, the experiments show that MCSs based on the classification results of classifiers characterized by a low design complexity like maximum likelihood and nearest mean classifiers can yield accuracies that are quite comparable to those of highly optimized classifiers.

Paul C. Smits

Papers

Quality assessment of image classification algorithms for land-cover mapping

Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach

An > data fusion

The European geoportal––one step towards the establishment of a European Spatial Data Infrastructure

Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection