GPS was built with military uses in mind during the Cold War. In 1983, Korean Air flight 007 was shot down by Soviet interceptors over Kamchatka when it went off-course. All passengers and crew aboard the civilian flight, including a sitting US congressman, were killed. Amid the ensuing controversy, President Reagan announced that GPS would be made available ...
You can get Sentinel-1 data from scihub.esa. Requires only
registration (And most likely, non-commercial use). As Sentinel-1
has just become operational the archive is not very extensive but
should grow quite quickly.
You can set request data-access propospal on Alaska Satellite
Facility. Some data open access. For ALOS-PALSAR you must be a resident of the ...
I have used OpenCV in the past to train for object detection for geo. Orfeo Toolbox is a good open source choice as Vascobnunes pointed out. For a closed-source version, you can take a look at Feature Analyst (that also has an ArcGIS extension).
At the end, it boils down to training a support vector machine. There are several libraries that you can use for ...
I agree with @vascobnunes opinion but if you want to define certain objects you have to use LANDSAT TM because more classification needs more bands as (R, G, B, NIR, MIR, TIR, FIR)... and my choice is that you should use LANDSAT TM (I gave same information in the following explanation) for vegetation.
The important thing in this case is that you should look ...
Unfortunately I can't view that video from Canada but based on the screen shot I believe something like that could be rendered in Pov-Ray.
A while back I asked a question about how to generate a high resolution rendering of the globe and @scw suggested I try Pov-Ray.
Using this guide I was able to create custom globes with a combination of my own inputs ...
GPS is a public service made free to access so that the country can collectively improve its knowledge of the technology. As in the case of the internet, this presents an opportunity for the more industrious among us to diversify its application at a faster rate. And when someone succeeds in finding a new and useful purpose for GPS, money is circulated. In ...
IMAGE REVERSE SEARCH WITH GOOGLE IMAGES
Doing a reverse search using images.google.com I found this link from wikimedia commons:
Which states it is a File called "München Geiselgasteig Filmstadt Aerial.jpg"
Which was posted a few later than you question (2012) by http:/...
If you only have SPOT 5 and Landsat TM to choose from, money is not a problem and for a small area of 30 000ha, I would agree that SPOT5 is the best choice, although Landsat would have some strong advantages:
2,5 m spatial resolution
3 spectral bands (Green, Red, Near Infra-red)
about 2,64€ per sqkm for new acquisitions
good revisit time
I am afraid satisfying roof detection cannot be achieved with only one single satellite image. You should try to use other sources of information.
The following article describes a method using a DEM + aerial image pairs + cadastral data:
M. Durupt, F. Taillandier. Automatic Building Reconstruction from a Digital Elevation Model and Cadastral Data: An ...
NASA's "Black Marble" is not one satellite image, but a mosaic gathered over several weeks and heavily edited:
The data was acquired over nine days in April 2012 and thirteen days in October 2012. It took satellite 312 orbits and 2.5 terabytes of data to get a clear shot of every parcel of Earth's land surface and islands. This new data was then mapped ...
Perhaps the following links might be useful:
The images below were taken from the data from the last link which gave some details on geology of the moon. But all datasets in the links are ...
Some things to consider:
1) Is the aerial imagery going to come stitched together already or are you going to have to manually stitch and post-process each image. You'll probably have to post process the satellite imagery.
2) When was the imagery acquired? For many features (e.g. rock outcrops) you're going to want leaf-off imagery.
3) Was the imagery (...
The following code will take an input raster, get it's extent, and insert that extent into a polygon featureclass:
r = arcpy.Raster(in_raster)
point = arcpy.Point()
array = arcpy.Array()
corners = ["lowerLeft", "lowerRight", "upperRight", "upperLeft"]
cursor = arcpy.InsertCursor(fc)
feat = cursor.newRow()
for corner in corners:
Think of the geometry. The incidence angle refers to the angle from nadir, or directly beneath the satellite, which would be 0°. As the sensor looks out to the sides from this nadir, the angle of incidence increases as does the fov (field of view). This is why the resolution decreases with increase in incidence angle. This illustration from the Sentinel ...
Glovis is one of the best places to start compiling free satellite imagery. For a new user, LANDSAT imagery is a great place to start - you will be able to find data covering the 1970's to present day. There is also a wealth of information available for working with this data. For example, if you are using ArcGIS you can quickly learn how to develop a ...
There is no satellite with a 5cm resolution. The best one that is available is 30cm (worldview 3, panchromatic). There are rumors that the military/secret service still have better ones in orbit (11. june this year there was the biggest available rocket starting with confidential payload if you like conspiracy). But the physics is a problem here. The optics ...
EDIT as 2019-06-18: HRO (High Resolution Orthoimagery) is no longer available on https://viewer.nationalmap.gov/basic/. Instead, visit https://earthexplorer.usgs.gov/. You'll need an account (free to register) to download the TIF files.
The team of OpenMapTiles.org project works on a downloadable global satellite / aerial layer which is ready to be used ...
As others have shown interest for this question I'll answer it using the information I've been able to gather so far:
There might be more than one archive or old Soviet Union imagery. As there were both military and civil missions (in contrast, film-recovery mission were forbidden in the US for non-military proposes).
The archive I know of so far is ...
You can use gdaltindex for this: http://www.gdal.org/gdaltindex.html
It will however still create rectangles (eg 4+1 points) in the same reference system as the images. But I wonder whether that really is a problem: how large are your images?
Assuming you have the distance from the observer to the satellite--without which the problem has no definite solution--then this amounts to solving three subproblems, using the strategy of computing the satellite's geocentric Cartesian (x,y,z) coordinates.
In the following, a is the semimajor axis (6,378,137.0 meters in WGS 84) and b is the semiminor axis (...
There are published coefficients available for MSS, TM5 ETM+7, QuickBird and IKONOS but I do not believe that anybody has derived coefficients for Rapid Eye. Here is a paper that describes how the authors derived the coefficients for Quickbird (http://www.asprs.org/a/publications/proceedings/pecora16/Yarbrough_L.pdf).
According to https://www.digitalglobe.com/sites/default/files/ISD_External.pdf you should have a field called satellite with a mnemonic like:
“QB02”, “WV01”, “WV02”, “WV03”, “GE01”, “Aerial”
Which I assume correspond to the satellites operated by DigitalGlobe:
QuickBird, WorldView 1/2/3, GeoEye-1, IKONOS
You can try TinyEye, a reverse image search. It will take your image and find any instances of it existing elsewhere on the web. This probably isn't the best bet for most satellite imagery, but searching could yield something if you didn't source the image yourself.
Alternatively, you can trying viewing the image metadata, which may tell you something about ...
One of the most simple ways to characterize vegetation from imagery is to utilize NDVI. In short, NDVI takes the difference from the spectral band with the highest EMR reflectance (nIR) and the spectral band with the lowest reflectance (red) and normalizes this value by dividing by the sum of the highest reflectance (nIR) band and the lowest reflectance ...
You can use r.patch for that (see help file)
You probably want to set the region first to encompass all raster layers, after which you can use r.patch to 'mosaic' the layers. The following example is from the helpfile:
export MAPS=`g.mlist type=rast sep=, pat="map_*"`
g.region rast=$MAPS -p
r.patch in=$MAPS out=mosaic
Use the keyword export when you are ...
The effects you are seeing are atmospheric effects due to differences in atmospheric aerosols, sun angle, and Rayleigh scattering. Since you have two scenes of the same location, though at different time periods, I would recommend using a technique called Dark Object Subtraction (DOS) (Song et al. 2001). From the ENVI web site:
Dark object subtraction ...
Landsat is now available via Amazon Web Services
Landsat 8 data is available for anyone to use via Amazon S3. All
Landsat 8 scenes from 2015 are available along with a selection of
cloud-free scenes from 2013 and 2014. All new Landsat 8 scenes are
made available each day, often within hours of production.
Only managed to find a couple of sources for SAR images and data:
You can download SAR images from here which are mostly focused on ecological sites such as forests:
You can download SAR samples from here which contain fairly large datasets (note: the last 4 links at the bottom of the SAR section are dead)