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Luus F.P.S.,University of Pretoria | Van Den Bergh F.,Meraka Institute | Maharaj B.T.J.,University of Pretoria
IEEE Geoscience and Remote Sensing Letters

Multitemporal land-use analysis is becoming increasingly important for the effective management of Earth resources. Despite that, consistent differences in the viewing and illumination geometry in satellite-borne imagery introduce some issues in the creation of land-use classification maps. The focus of this letter is settlement classification with high-resolution panchromatic acquisitions, using texture features to distinguish between settlement classes. The important multitemporal variance component of shadow is effectively removed before feature determination, which allows for minimum-supervision across-date classification. Shadow detection based on local adaptive thresholding is employed and experimentally shown to outperform existing fixed threshold shadow detectors in increasing settlement classification accuracy. Both same- and across-date settlement accuracies are significantly improved with shadow masking during feature calculation. A statistical study was performed and found to support the hypothesis that the increased accuracy is due to shadow masking specifically. © 2004-2012 IEEE. Source

Butgereit L.,Meraka Institute
Proceedings - 2011 6th International Conference on Pervasive Computing and Applications, ICPCA 2011

South Africa is a semi-arid country and water resources need to be monitored. The Internet of Things is the phenomenon of more and more things (as opposed to people and services) becoming connected to the Internet. This paper describes a project where four major South African dams are connected to Twitter and Facebook (and other social media such as MXit and Google Chat) in a mechanism which would be easy to replicate for additional dams or rivers. Data is supplied by the South African Department of Water Affairs. Beachcomber (a Mobicents based JEE application) routes the data to appropriate service building blocks and resource adaptors to ensure that the information is widely disseminated. © 2011 IEEE. Source

Featherstone C.,Meraka Institute | Van Der Poel E.,University of South Africa
ACM International Conference Proceeding Series

This paper is to describes a method for interposing computer generated melody with tone linked to unique entities within the text of a novel. Background: A recent study describing a piece of software called "TransProse" has already shown that sentiment in the text of a novel can be used to automatically generate simple piano music that reflects the same sentiment as the novel. This study wished to establish a method whereby, if after aligning the text with the melody, the sentiment in the words surrounding particular characters as they occurred within the novel could produce another melody line, for each character, that could reflect the individual characters' tone and distinguish the melodies ascribed to each character from each other. Method: The sentiment in the text of the novel is extracted by looking up the words in a database that groups the words into emotional groups called "Ekman categories". Simplistic relations between aspects of music such as pitch and tempo are chosen based on the two categories that contained the most words. These chosen attributes are then used to generate the first two melody lines. The paragraphs within which the named entities referring to characters are found is manually determined and the top "Ekman category" of the named entities is obtained through simplistic methods of extraction. Each bar of the melody is aligned with individual paragraphs of text and an additional melody line is generated for each character. Results: Adjusting the fitness function of the Genetic algorithm (GA) that was used was not sufficient to link the tone of the characters to the melody. Assigning each character their own short melodic phrase and varying the phrase appropriately achieved the desired outcome but requires additional work to harmonise better with the first two melody lines. © 2015 ACM. Source

Featherstone C.,Meraka Institute
2013 IST-Africa Conference and Exhibition, IST-Africa 2013

Being able to identify and predict crime trends or track criminal movement would help anyone interested in preventing criminal activity or being able to assess where crime enforcement is needed, particularly in crimes where constant policing is impossible, such as cable theft. Many neighbourhoods in South Africa have formed voluntary community policing groups, who keep in touch using SMS and two way radios. Some have adopted websites and even Twitter as a means of being more easily in touch quickly and transparently. The influential groups recognising the value and using Twitter include, Crime Line (@CrimeLineZA) and the South African Police Service (@SAPoliceService). This paper argues that existing technologies can make communication more useful in terms of data gathering, prediction and spotting broader patterns. An assessment is done to determine if South African people are already using Twitter to report crime and to find out what information they are sharing, with the goal of estabishing whether it could be useful as a source of information for the prevention of crime. © 2013 The Authors. Source

Luus F.P.S.,University of Pretoria | Van Den Bergh F.,Meraka Institute | Maharaj B.T.J.,University of Pretoria
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Settlement classifiers for multitemporal satellite image analysis have to overcome numerous difficulties related to across-date variances in viewing-and illumination geometry. Shadow anisotropy is a prominent contributing factor in classifier inaccuracy, so methods are introduced in this study to enable minimum-supervision classifier design that mitigate the effects of shadow profile differences. A segmentation-based shadow detector is proposed that utilizes a panchromatic segment merging algorithm with parameters that are robust against dynamic range variances seen in multitemporal imagery. The proposed shadow detector improves on the settlement classification accuracy achieved by fixed threshold detection paired with shadow removal in the presented case-study. The relationship between shadow detection accuracy and settlement classification accuracy is investigated, and it is shown that shadow removal produces greater settlement accuracy improvements for across-date experiments specifically. © 2013 IEEE. Source

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