"Complementing Snow Cover Estimation with Analysis of Web Public Images"

DIPARTIMENTO DI ELETTRONICA, INFORMAZIONE E BIOINGEGNERIA
SnowWatch
Complementing Snow Cover Estimation
with Analysis of Web Public Images
Roman Fedorov: roman.fedorov@polimi.it

Introduction
Complementing Snow Cover Estimation with Analysis of Web Public Images 2
• Challenge: transform public images
depicting mountain slopes into virtual
snow measurement stations.
• Measurements complement existing
ground and satellite sensor data

UGC
3
Media User Generated Content (UGC) publicly
available on the Web is reaching unprecedented size
nowadays:
• Facebook ~ 350 MM daily photo uploads
• Flickr ~ 8.000 MM photographs with 3.5 MM daily
uploads
• Panoramio reached ~ 75 MM geo-tagged photograph
It contains an enormous amount of latent implicit
knowledge, the challenge is to extract it.
Complementing Snow Cover Estimation with Analysis of Web Public Images

Project
4
SnowWatch aims at:
• improving snow analysis and water management
through the acquisition and analysis of public media
content depicting alpine mountains
• Creating alpine snow media dataset with open
contribution of new media and access to processed
media and its results for any environmental
researchers
The project aims to design, implement and test a new approach to
homeland security that combines the traditional measures of the Public
Administration with the use of social networks as tools for monitoring and
warning the population.

Content Providers
SnowWatch Engine
SnowWatch Architecture
User Photos
5
Flickr Photos
Content
Consumers
Webcams
API Media Storage
Get Image Orientation,
Identify Mountain Peaks
Get Image Snow Pixel-
level classification

Flickr Photographs Crawling
6
Does an image contain a
clear a mountain slope view?
- Heavily depends on the
photograph shooting point altitude

Flickr Crawling Implementation
7
SVM Image Content-based Classifier:
Bag of Visual Words / Fisher Vector
SIFT/SSIM/GIST/HOG2x2 Descriptors
Spatial Histogram, Vertical Bands
Manually annotated 1658 samples (~70%) for training and
validation and 710 samples (~30%) for testing.

Webcam Images Aggregation
8
Identified ~2.000 webcams depicting mountains in Alpine
regions.
Each webcam produces an image every 1’ – 15’.
• 67% of the images are not suitable due to bad weather
• More than 50.000 images and 6 GB of data acquired daily
Through the extraction of the edges we can compute the percentage of the
skyline clearly visible, and discard all images with visibility lower than a certain
threshold, obtaining the following ROC curve varying the threshold:

Webcam Images Aggregation
9
Remaining small moving obstacles (such as low clouds,
persons, vehicles) and varying illumination conditions are
managed through the median aggregation of all daily images

Photo Peak Identification
10
An orientation of a geo-tagged photograph is
needed.
Given a Digital Elevation Model (a 3D terrain
model) we can simulate the terrain
silhouettes that should be seen from the
photographer point of view.
Steps
Panorama
Scale
Edges
Filter
Match

Photo Panorama
11
Steps
Panorama
Scale
Edges
Filter
Match

Photo Scaling
12
Photo must be scaled properly, so the
mountains have the same pixel dimension on
the photograph and on the panorama.
This can be obtained by calculating the Field
Of View (FOV) of the photo:
Steps
Panorama
Scale
Edges
Filter
Match

Photo Edge Extraction
13
Steps
Panorama
Scale
Edges
Filter
Match

Photo Edge Filtering
14
Too many edges on the image that do not
belong to a terrain contour!
• Skyline detection algorithm is employed, and all the edge
pixels above the skyline are removed, being considered
obstacles or clouds.
• Weighting mechanism is applied, which assigns
decreasing weights to the edge pixels as the distance
from the skyline increases.
Steps
Panorama
Scale
Edges
Filter
Match

Photo Alignment
15
Find the best overlap maximizing the vector
cross correlation score:
Steps
Panorama
Scale
Edges
Filter
Match

Photo Peak Alignment
16
A global overlap between the photo and the
panorama is often not enough, a
neighborhood vector cross correlation
adjustment is applied to each identified
peak:
Steps
Panorama
Scale
Edges
Filter
Match
Local Match

Photo Peak Identification
17
Fraction of photographs with orientation correctly identified

Snow Pixels Classification
18
Classifying each pixel as snow covered/terrain is
much more than simply checking bright/dark pixels:

19
SnowGround GroundGroundSnow Snow Snow

20
Random Forest Classifier that considers pixel
and pixel neighbors color, global and local
intensity of the image.
Trained on 59 manually
labeled images
(~ 7 Million pixels),
outperform current
methods:

Environmental Impact Evaluation
21
Environmental consistency evaluation of
extracted snow cover indexes.
No direct ground truth, how do we
evaluate it?
Practical utility w.r.t. an environmental
model
Use case: Como Lake water management
Flooding vs Irrigation trend, what is the
best solution?

SnowWatch 2°°°° year
22
If the physical snow estimations provided
by ARPA (Italian EPA) are not available,
Adding Virtual Snow Measures improves
the performance by ~100%!

SnowWatch 2°°°° year
Roman Fedorov: roman.fedorov@polimi.it 23
With some irrigation <-> flooding target
solutions, using SnowWatch virtual
measurements is even better than using
ARPA snow estimation!

Current and Future Work
24
Current:
• MountainWatch: real time augmented reality mobile
application
• Expand tests on environmental impact
Future:
• Expand area of interest and content providers
• Orthophotos and new snow indexes
SnowWatch: Estimating Snow Cover from Publicly Available Images

"Complementing Snow Cover Estimation with Analysis of Web Public Images"

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