Abstract-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.

Abstract. Snow is a key component of the hydrologic cycle in many regions of the world. Despite recent advances in environmental monitoring that are making a wide range of data available, continuous snow monitoring systems that can collect data at high spatial and temporal resolution are not well established yet, especially in inaccessible high-latitude or mountainous regions. The unprecedented availability of user-generated data on the web is opening new opportunities for enhancing real-time monitoring and modeling of environmental systems based on data that are public, low-cost, and spatiotemporally dense. In this paper, we contribute a novel crowdsourcing procedure for extracting snow-related information from public web images, either produced by users or generated by touristic webcams. A fully automated process fetches mountain images from multiple sources, identifies the peaks present therein, and estimates virtual snow indexes representing a proxy of the snow-covered area. Our procedure has the potential for complementing traditional snow-related information, minimizing costs and efforts for obtaining the virtual snow indexes and, at the same time, maximizing the portability of the procedure to several locations where such public images are available. The operational value of the obtained virtual snow indexes is assessed for a real-world water-management problem, the regulation of Lake Como, where we use these indexes for informing the daily operations of the lake. Numerical results show that such information is effective in extending the anticipation capacity of the lake operations, ultimately improving the system performance.

Abstract. Outdoor augmented reality applications project information of interest onto views of the world in real-time. Their core challenge is recognizing the meaningful objects present in the current view and retrieving and overlaying pertinent information onto such objects. In this paper we report on the development of a framework for mobile outdoor augmented reality application, applied to the overlay of peak information onto views of mountain landscapes. The resulting app operates by estimating the virtual panorama visible from the viewpoint of the user, using an online Digital Terrain Model (DEM), and by matching such panorama to the actual image framed by the camera. When a good match is found, meta-data from the DEM (e.g, peak name, altitude, distance) are projected in real time onto the view. The application, besides providing a nice experience to the user, can be employed to crowdsource the collection of annotated mountain images for environmental applications.

We present a method for the identification of mountain peaks in geo-tagged photos. The key tenet is to perform an edgebased matching between the visual content of each photo and a terrain view synthesized from a Digital Elevation Model (DEM). The latter is generated as if a virtual observer is located at the coordinates indicated by the geo-tag. The key property of the method is the ability to reach a highly accurate estimation of the position of mountain peaks with a coarse resolution DEM available in the corresponding geographical area, which is sampled at a spatial resolution between 30 m and 90 m. This is the case for publicly available DEMs that cover almost the totality of the Earth surface (such as SRTM CGIAR [4] and ASTER GDEM [10]). The method is fully unsupervised, thus it can be applied to the analysis of massive amounts of user generated content available, e.g., on Flickr and Panoramio. We evaluated our method on a dataset of manually annotated images of mountain landscapes, containing peaks of the Italian and Swiss Alps. Our results show that it is possible to accurately identify the peaks in 75.0% of the cases. This result increases to 81.6% when considering only photos with mountain slopes far from the observer.

We propose a method for the environmental monitoring through the publicly available media User Generated Content (UGC). In particular we address the problem of the snow cover and level estimation by analyzing the social media data such as geo-tagged photographs and public webcams installed in mountain regions. The entire pipeline of the process is presented to the audience: from the data crawling and automatic relevance classification (does or does not the photograph contain a significant mountain profile) to the image content analysis and environmental models (identification of the snow covered area on the photograph). Each presented component is self-contained and can be inspected individually, the connections between the components however are strongly highlighted allowing the viewer to understand intuitively the entire pipeline structure.