This paper merges multimedia and environmental research to verify the utility of public web images for improving water management in periods of water scarcity, an increasingly critical event due to climate change. A multimedia processing pipeline fetches mountain images from multiple sources and extracts virtual snow indexes correlated to the amount of water accumulated in the snow pack. Such indexes are used to predict water availability and design the operating policy of Lake Como, Italy. The performance of this informed policy is contrasted, via simulation, with the current operation, which depends only on lake water level and day of the year, and with a policy that exploits official Snow Water Equivalent (SWE) estimated from ground stations data and satellite imagery. Virtual snow indexes allow improving the system performance by 11.6% w.r.t. the baseline operation, and yield further improvement when coupled with official SWE information, showing that the two data sources are complementary. The proposed approach exemplifies the opportunities and challenges of applying multimedia content analysis methods to complex environmental problems.

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, and 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 labeled 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.

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 geotagged 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.