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G. Mandlburger:
"3D Remote Sensing Sensors - Mapping the Earth in 3D";
Keynote Lecture: Hengstberger Symposium "Towards Digital Earth - 3D Spatial Data Infrastructures", Heidelberg (invited); 2011-09-07 - 2011-09-08.

Capturing and reconstructing the Earth´s surface and artificial objects is of prime
importance for many applications in our everyday world; from transport infrastructure
to telecommunication, from disaster management to ecological issues,
from agricultural measures to city planning and many more. The magic triangle
is: Sensors - Algorithms - Applications. In other words, the raw sensor data is
transformed using a set of algorithms to a final model, be it a 1D cross section,
a 2d map, a 3d virtual reality computer model. Capturing 3d data was long restricted
to a handful of mapping experts. Today however, with the tremendous
progress in sensor (gps, umts, digital consumer cameras) as well as computer
(mobile devices) and internet technology (Google, Virtual globe), this field is also
open to a wider community of non-experts (i.e., collaborative crowdsourcing).
This contribution, therefore, reviews well established and uprising 3d remote
sens ing sensors. Instruments enabling high geometric and radiometric quality
will be equally discussed along with low price consumer devices. The principles
of both, passive sensors (photometric frame cameras, line scanners, hyperspectral
scanners) and active systems like radar, lidar and range cameras are introduced
and their pros and cons are confronted. As 3d data capturing is, nowadays, often
carried out in a multi-sensor environment, fusion of data from different sources
becomes more and more important. This applies to specific sensor systems like
full waveform Airborne Laser Scanning (als), where precise point clouds are obtained
by combining data from Global Navigation Satellite System (sensor position),
inertial measurement units (sensor alignment), the laser scanner (range
and beam deflection) and a waveform processing unit as well as for the integration
of models with different levels of detail. Embedding local Google SketchUp
3d photo models into precise, countrywide 2.5D als dtms may serve as an example
of the latter. One of the challenges of tomorrows geo-data infrastructure is to
combine the high accuracy level of modern 3d sensors with the potentially high
up-to-dateness of crowd source data.