Estimating snow cover from publicly available images

TL;DR

This paper presents methods to estimate snow cover in mountainous regions using publicly available images, creating a snow cover index that correlates with seasonal temperature trends and achieves high accuracy.

Contribution

It introduces two processing pipelines for user-generated and webcam images to estimate snow cover, supplementing traditional sensor data with visual content.

Findings

Achieved 90% precision and 91.1% recall in snow cover estimation.

The snow cover index captures seasonal temperature trends.

The approach effectively leverages publicly available visual data.

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.