Visualization of uncertain catchment boundaries and its influence on decision making

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.In this poster, we introduce an on-going project where uncertainty-aware drainage divides were calculated, visualized, and tested as background data for the decision-making process

Use of Naturally Available Reference Targets to Calibrate Airborne Laser Scanning Intensity Data

We have studied the possibility of calibrating airborne laser scanning (ALS) intensity data, using land targets typically available in urban areas. For this purpose, a test area around Espoonlahti Harbor, Espoo, Finland, for which a long time series of ALS campaigns is available, was selected. Different target samples (beach sand, concrete, asphalt, different types of gravel) were collected and measured in the laboratory. Using tarps, which have certain backscattering properties, the natural samples were calibrated and studied, taking into account the atmospheric effect, incidence angle and flying height. Using data from different flights and altitudes, a time series for the natural samples was generated. Studying the stability of the samples, we could obtain information on the most ideal types of natural targets for ALS radiometric calibration. Using the selected natural samples as reference, the ALS points of typical land targets were calibrated again and examined. Results showed the need for more accurate ground reference data, before using natural samples in ALS intensity data calibration. Also, the NIR camera-based field system was used for collecting ground reference data. This system proved to be a good means for collecting in situ reference data, especially for targets with inhomogeneous surface reflection properties

A neural classification method for supporting the creation of BioVerbNet

Abstract Background VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data. Results Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet. Conclusion This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine

Absolute Radiometric Calibration of ALS Intensity Data: Effects on Accuracy and Target Classification

Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data

Metsäalueen korkeusmallin muodostaminen laserkeilaimella mitatusta kolmiulotteisesta pistejoukosta

Laserkeilain on lentokoneesta tai helikopterista käytettävä kaukokartoitusinstrumentti, joka lähettää laserpulsseja ja vastaanottaa heijastuneiden pulssien kaiut. Mittauksen aikana mittaussuuntaa poikkeutetaan keilaimella kohtisuorassa suunnassa lentosuuntaa vastaan, jolloin pulssit hajautuvat kaistalle lentolinjan alapuolelle. Pulssin heijastaneelle maastonkohteelle voidaan laskea kolmiulotteiset koordinaatit mittaussuunnan, laserkeilaimen sijainnin sekä pulssin kulkuajasta lasketun etäisyyden perusteella. Laserkeilaimella voidaan mitata myös metsäalueita. Tällöin suuri osa pulsseista heijastuu puiden latvoista, mutta osa läpäisee lehvästön puiden välissä ja antaa siten tietoa maan pinnasta. Diplomityössä kehitettiin menetelmä metsäalueen korkeusmallin luomiseksi laserkeilaimella mitatusta aineistosta. Menetelmässä korkeusmalli toteutettiin säännöllisenä ruutuverkkomallina, jossa yhden maastonalkion alueelle osuneista useista mittauspisteistä tuotettiin vain yksi korkeusarvo. Kasvillisuudesta, kuten puiden latvoista tai pensaista tulleet heijastukset suodatettiin pois korkeusmallin pintaa muokkaamalla. Lopullinen korkeusmalli laskettiin keskiarvona maanpinnan tuntumasta tulleista heijastuksista. Menetelmällä luodun korkeusmallin tarkkuutta arvioitiin vertaamalla korkeusmallin pintaa n. 750:en takymetrillä mitatun pisteen koordinaatteihin. Tutkimuksen tuloksena arvioitiin korkeusmallin tarkkuus ja tarkkuuteen vaikuttavat osatekijät. Lisäksi arvioitiin yleisesti laserkeilaimella mitatun aineiston laatua ja instrumentin käyttökelpoisuutta metsäalueen kartoituksessa

A neural classification method for supporting the creation of BioVerbNet

Abstract Background VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data. Results Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet. Conclusion This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine