Estimating snow cover from publicly available images

In this paper we study the problem of estimating snow cover in mountainous regions, that is, the spatial extent of the earth surface covered by snow. We argue that publicly available visual content, in the form of user generated photographs and image feeds from outdoor webcams, can both be leveraged as additional measurement sources, complementing existing ground, satellite and airborne sensor data. To this end, we describe two content acquisition and processing pipelines that are tailored to such sources, addressing the specific challenges posed by each of them, e.g., identifying the mountain peaks, filtering out images taken in bad weather conditions, handling varying illumination conditions. The final outcome is summarized in a snow cover index, which indicates for a specific mountain and day of the year, the fraction of visible area covered by snow, possibly at different elevations. We created a manually labelled dataset to assess the accuracy of the image snow covered area estimation, achieving 90.0% precision at 91.1% recall. In addition, we show that seasonal trends related to air temperature are captured by the snow cover index.Comment: submitted to IEEE Transactions on Multimedi

SnowWatch: A multi-modal citizen science application

The demo presents Snow Watch, a citizen science system that supports the acquisition and processing of mountain images for the purpose of extracting snow information, predicting the amount of water available in the dry season, and supporting a multi-objective lake regulation problem. We discuss how the proposed architecture has been rapidly prototyped using a general-purpose architecture to collect sensor and user-generated Web content from heterogeneous sources, process it for knowledge extraction, relying on the contribution of voluntary crowds, engaged and retained with gamification techniques

Compressing web Geodata for real-time environmental applications

The advent of connected mobile devices has caused an unprecedented availability of geo-referenced user-generated content, which can be exploited for environment monitoring. In particular, Augmented Reality (AR) mobile applications can be designed to enable citizens collect observations, by overlaying relevant meta-data on their current view. This class of applications rely on multiple meta-data, which must be properly compressed for transmission and real-time usage. This paper presents a two-stage approach for the compression of Digital Elevation Model (DEM) data and geographic entities for a mountain environment monitoring mobile AR application. The proposed method is generic and could be applied to other types of geographical data

Estimating Regional Snow Line Elevation Using Public Webcam Images

Snow cover is of high relevance for the Earth’s climate system, and its variability plays a key role in alpine hydrology, ecology, and socioeconomic systems. Measurements obtained by optical satellite remote sensing are an essential source for quantifying snow cover variability from a local to global scale. However, the temporal resolution of such measurements is often affected by persistent cloud coverage, limiting the application of high resolution snow cover mapping. In this study, we derive the regional snow line elevation in an alpine catchment area using public webcams. We compare our results to the snow line information derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 snow cover products and find our results to be in good agreement therewith. Between October 2017 and the end of June 2018, snow lines derived from webcams lie on average 55.8 m below and 33.7 m above MODIS snow lines using a normalized-difference snow index (NDSI) of 0.4 and 0.1, respectively, and are on average 53.1 m below snow lines derived from Sentinel-2. We further analyze the superior temporal resolution of webcam-based snow cover information and demonstrate its effectiveness in filling temporal gaps in satellite-based measurements caused by cloud cover. Our findings show the ability of webcam-based snow line elevation retrieval to complement and improve satellite-based measurements

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