Stair, Agriculture and Entomology Building, Brisbane

View to circulation stair from exterior.

Entrance, Agriculture and Entomology Building, Brisbane

View to entrance.

Brick detail, Agriculture and Entomology Building, Brisbane

Detailed view of venting in brick work. Bricks used were commons, with prevalent dark blue iron staining.

Door and window detail, Agriculture and Entomology Building, Brisbane

View to side entrance opening and window detail with sliding timber shutters above, from exterior.

Agriculture and Entomology Building, Brisbane

View to north and east facing wings, along circulation path to the roof feature connection between wings.

Windows and venting to elevation, Agriculture and Entomology Building, Brisbane

View to elevation with timber sliding shutters, aluminium double hung sash windows and brick venting.

Agriculture and Entomology Building, Brisbane

View across car park to north-west elevation.

Stair exterior, Agriculture and Entomology Building, Brisbane

View to circulation stair, with venting detail, from exterior.

Global localization with non-quantized local image features

In the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Exploration of alpine orographic precipitation patterns with radar image processing and clustering techniques

A methodology of exploratory data analysis investigating the phenomenon of orographic precipitation enhancement is proposed. The precipitation observations obtained from three Swiss Doppler weather radars are analysed for the major precipitation event of August 2005 in the Alps. Image processing techniques are used to detect significant precipitation cells/pixels from radar images while filtering out spurious effects due to ground clutter. The contribution of topography to precipitation patterns is described by an extensive set of topographical descriptors computed from the digital elevation model at multiple spatial scales. Additionally, the motion vector field is derived from subsequent radar images and integrated into a set of topographic features to highlight the slopes exposed to main flows. Following the exploratory data analysis with a recent algorithm of spectral clustering, it is shown that orographic precipitation cells are generated under specific flow and topographic conditions. Repeatability of precipitation patterns in particular spatial locations is found to be linked to specific local terrain shapes, e.g. at the top of hills and on the upwind side of the mountains. This methodology and our empirical findings for the Alpine region provide a basis for building computational data-driven models of orographic enhancement and triggering of precipitation. Copyright (C) 2011 Royal Meteorological Society .

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Building high technology complete products for the markets of humanitarian organizations and developing countries

Tämä tutkielma käsittelee high-tech kokonaisratkaisun rakentamista kehitysmaiden ja humanitääristen organisaatioiden markkinoille. Tavoitteena on löytää ne komponentit joita case-yritys Mediburner Ltd:n polttouuni tarvitsee rinnalleen. Jotta täydentävien elementtien määritteleminen olisi mahdollista, pitää ensin selvittää keitä ovat asiakkaat, ja mitkä ovat heidän tarpeensa. Tutkimusmetodina käytetään kuvailevaa case-tutkimusta. Empiirinen materiaali kerättiin henkilökohtaisissa- ja puhelinkeskusteluissa. Niihin henkilöihin, joiden tavoittaminen oli aikaeron vuoksi hankalaa, otettiin yhteyttä sähköpostitse. Toinen tietolähde olivat dokumentit. Tutkielmassa käytettiin internetsivuja, sairaalajätehuoltoon liittyvien kansainvälisten konferenssien ja kenttätutkimusten raportteja sekä humanitääristen organisaatioiden suosituksia ja lehdistötiedotteita. Tulokseksi saatiin kymmenen tarvittavaa tukevien elementtien ryhmää: lisälaitteet, astiat jätteen keräilyyn ja tilapäiseen varastointiin, polttoaine, sähkö, logistiset ratkaisut, asennus ja käyttöönotto, huolto- ja korjauspalvelut, koulutus, help-desk –palvelu ja rahoitus. Lisäksi tarvitaan imago, joka konkretisoi tarjotun ratkaisun hyödyt. Yksi toimivan imagotyylin perusta voisi olla vastuullisuus.

Building the map of innovation for membrane materials (Ceramic membranes)

This work proposes a method of visualizing the trend of research in the field of ceramic membranes from 1999 to 2006. The presented approach involves identifying problems encountered during research in the field of ceramic membranes. Patents from US patent database and articles from Science Direct(& by ELSEVIER was analyzed for this work. The identification of problems was achieved with software Knowledgist which focuses on the semantic nature of a sentence to generate series of subject action object structures. The identified problems are classified into major research issues. This classification was used for the visualization of the intensity of research. The image produced gives the relation between the number of patents, with time and the major research issues. The identification of the most cited papers which strongly influence the research of the previously identified major issues in the given field was also carried out. The relations between these papers are presented using the metaphor of social network. The final result of this work are two figures, a diagram showing the change in the studied problems a specified period of time and a figure showing the relations between the major papers and groups of the problems

Construction Plan Image Service for Mobile Devices

The forthcoming media revolution of exchanging paper documents to digital media in construction engineering requires new tools to be developed. The basis of this bachelor’s thesis was to explore the preliminary possibilities of exporting imagery from a Building Information Modelling –software to a mobile phone on a construction yard. This was done by producing a Web Service which uses the design software’s Application Programming Interface to interact with a structures model in order to produce the requested imagery. While mobile phones were found lacking as client devices, because of limited processing power and small displays, the implementation showed that the Tekla Structures API can be used to automatically produce various types of imagery. Web Services can be used to transfer this data to the client. Before further development the needs of the contractor, benefits for the building master and inspector and the full potential of the BIM-software need to be mapped out with surveys.