Temporally Consistent Snow Cover Estimation from Noisy, Irregularly Sampled Measurements

We propose a method for accurate and temporally consistent surface classification in the presence of noisy, irregularly sampled measurements, and apply it to the estimation of snow coverage over time. The input imagery is extremely challenging, with large variations in lighting and weather distorting the measurements. Initial snow cover estimations are obtained using a Gaussian Mixture Model of color. To achieve a temporally consistent snow cover estimation, we use a Markov Random Field that penalizes rapid fluctuations in the snow state, and show that the penalty term needs to be quite large, resulting in slow reactivity to changes. We thus propose a classifier to separate good from uninformative images, which allows to use a smaller penalty term. We show that the incorporation of domain knowledge to discard uninformative images leads to better reactivity to changes in snow coverage as well as more accurate snow cover estimations

Snow cover detection from webcam images

Práce zkoumá možnosti využití webových kamer jakožto zdroje prostorových dat o výskytu sněhu. Cílem práce je navržení vhodné metody detekce sněhové pokrývky ze snímků webových kamer. Na vzorku snímků 6 webových kamer ČHMÚ je provedena detekce sněhové pokrývky metodami pixelové klasifikace. Je zkoumán vliv velikosti trénovacího souboru na přesnost klasifikace. Celková přesnost dosažená metodou SVM je 97,46 %. Dále je cílem navrhnout systém pro určení podílu sněhem pokrytého území. Vytvořený algoritmus se skládá z několika dílčích kroků: třídění a registrace snímků, detekce sněhu, zavedení souřadnicového systému, výpočtu velikosti zkoumané plochy a podílu sněhem pokrytého území. Navržený model je možné použít pro automatizované zpracování snímků různých webových kamer. Ze získaných denních hodnot podílu sněhem pokrytého území jsou vytvořeny křivky tání sněhové pokrývky. Výsledky jsou validovány pomocí dat vybraných stanic ČHMÚ. Navržený a parametrizovaný model potvrzuje možnost úspěšně využít webové kamery jako doplněk pozemního měření meteorologických stanic a pro validaci produktů dálkového průzkumu Země.This thesis deals with the possibility of using webcams as a source of spatial data for snow occurrence. The aim of this study is to propose a suitable method of snow cover detection from web camera images. From a sample of 6 webcams of the Czech Hydrometeorological Institute (CHMI) the snow cover is detected by pixel classification methods. The effect of training file size on the accuracy of classification is examined and the overall accuracy achieved by the SVM method was shown to be 97.46%. This study also aims to propose a system for determining the proportion of snow-covered areas. The algorithm consists of several sub-steps: filtering and registration of images, detection of snow, introduction of a coordinate system, calculation of the size of the surveyed area and the proportion of snow-covered area. The designed model can be used for automatic processing of images for various webcams. The melting curves of the snow cover are generated from the obtained daily values of the snow covered area. The results are validated using data from selected CHMI stations. The proposed and parameterized model confirms the possibility of successful use of webcams as a complement to ground measurement of meteorological stations and for the validation of remote sensing products.Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Leveraging Overhead Imagery for Localization, Mapping, and Understanding

Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis is a general framework for estimating geospatial functions, such as land cover or land use, which integrates visual evidence from both ground-level and overhead image viewpoints