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Measuring Co-Seismic deformation from SPOT satellite and Aerial images
By Lurker,
Interesting articles :
North-South displacement field - 1999 Hector-Mine earthquake, California
In complement to seismological records, the knowledge of the ruptured fault geometry and co-seismic ground displacements are key data to investigate the mechanics of seismic rupture. This information can be retrieved from sub-pixel correlation of optical images. We are investigating the use of SPOT (Satellite pour l'Observation de la Terre) satellites images.
Movevis
By MORABIT,
Hello,
I am trying learning use of moveVis for visualization of movement data. Please, can someone tell me how can I use my RS data with moveVis to create video animation.
Thanks!
'Best practice workflow' to process UAV survey datasets with highly accurate ground control points and Agisoft
By Lurker,
just found this interesting articles on Agisoft forum :
source:
https://www.agisoft.com/forum/index.php?topic=7851.0
Feature extraction - tree locations and canopies
By imiltopnem,
Friend's, does anyone use or know a software or methodology where tree canopies can be extracted and a dot representing the tree placed in the canopy centre from high resolution raster images? This can be done interactively or automatically. Here is an example using R but when the data is big, it crashes and a solution could not be found. Any help to achieve a result like this would be really appreciated.
Many thanks in advance
Imiltopnem
Tips to get high quality surveying results with UAV
By Lurker,
The first thing to do before mapping is to set up the camera parameters.
Before to set up camera parameters, recommended resetting the all parameters on camera first.
To set camera parameters manually need to set to manual mode.
Image quality: Extra fine
Shutter speed: to remove blur from photo shutter speed should be set for higher value. 1200–1600 is recommended. Higher the shutter speed reduce image quality
.
if there is blur in the image incr
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GPS is an incredible piece of modern technology. Not only does it allow for locating objects precisely anywhere on the planet, but it also enables the turn-by-turn directions we take for granted these days — all without needing anything more than a radio receiver and some software to decode the signals constantly being sent down from space. [Chris] took that last bit bit as somewhat of a challenge and set off to write a software-defined GPS receiver from the ground up. As GPS started as a military technology, the level of precision needed for things like turn-by-turn navigation wasn’t always available to civilians. The “coarse” positioning is only capable of accuracy within a few hundred meters, so this legacy capability is the first thing that [Chris] tackles here. It is pretty fast, though, with the system able to resolve a location in 24 seconds from cold start and then displaying its information in a browser window. Everything in this build is done in Python as well, meaning that it’s a great starting point for investigating how GPS works and for building other projects from there. The other thing that makes this project accessible is that the only other hardware needed besides a computer that runs Python is an RTL-SDR dongle. These inexpensive TV dongles ushered in a software-defined radio revolution about a decade ago when it was found that they could receive a wide array of radio signals beyond just TV. source: Hackaday and GitHub - chrisdoble/gps-receiver
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