Participatory integration of live webcams into GIS

Abstract

Global satellite imagery provides nearly ubiquitous views of the Earth's surface, and the tens of thousands of webcams provide live views from near Earth viewpoints. Combining these into a single application creates live views in the global context, where cars move through intersections, trees sway in the wind, and students walk across campus in realtime. This integration of the camera requires registration, which takes time, effort, and expertise. Here we report on two participatory interfaces that simplify this registration by providing applications which allow anyone to use live webcam streams to create virtual overhead views or to map live texture onto 3D models. We highlight system design issues that affect the scalability of such a service, and offer a casestudy of how we overcame these in building a system which is publicly available and integrated with Google Maps and the Google Earth Plug-in. Imagery registered to features in GIS applications can be considered as richly geotagged, and we discuss opportunities for this rich geotagging.

Cloud Motion as a Calibration Cue

“…We introduced automated methods for estimating the radial distortion, horizon line, focal length and geo-orientation of a static outdoor camera from video captured over many days. These methods enable calibration in scenes which do not contain sufficient static geometric information for more traditional calibration techniques, such as orthogonal vanishing points [3] or registration with known geometry [1].…”

“…For high-framerate cameras, we resample the videos to 1Hz before computing motion aggregates to reduce numerical issues. Our implementation 1 , running on a standard desktop PC, takes between 15 to 30 minutes to fully geo-calibrate each scene.…”

“…We performed an experiment to explore the effect of changing the number of pixels on the resulting 1 …”

“…For each of the scenes we consider, we know the geographic location and we manually estimate the orientation and field of view by matching image points to world points using ProjectLive3D [1]. For each video, we know the timestamp; if possible we use the location and time-stamp to archive upper-level wind data from the FAA 2 ; otherwise we use ground-level wind data from Weather Underground 3 .…”

Webcams in context

“…This eliminates the need for any central organization to be responsible for geo-calibrating cameras, and makes the system scalable to the large number of cameras by not requiring a central server to touch the live video data. Previous work [1] defined a start to this process; showing a web interface which allows one to map polygons in a webcam image to polygons in either Google Maps or Google Earth, to infer the camera calibration, and to show live views with those polygons updating from the live feed.…”