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15

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 ...


15

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 ...


13

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 ...


12

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 ...


11

If you can, use GIS software, which is designed with this problem in mind: instead of reading the entire dataset into memory, it will only sample the image to create a display and no more. Something like QGIS should allow you to visualize the data, and provides ways of exporting the view, as one approach to creating a downscaled version. Another option is ...


10

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: SPOT5: 2,5 m spatial resolution 3 spectral bands (Green, Red, Near Infra-red) about 2,64€ per sqkm for new acquisitions good revisit time biggest ...


8

Imagery is expensive to capture and produce, which effectively limits its production to commercial entities and governments. Most of the commercial satellites don't carry consistent global coverage and generate income by selling access to scenes, including their resale to the providers you listed. That leaves government options in the free category: perhaps ...


8

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 ...


8

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 ...


7

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 ...


7

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 ...


7

The first is "Create Vector". The bitmap [Raster] is translated to vector notation as soon as possible. That is, each single bit is converted into four directional vectors, joined as a square. The second is "Simplify Vector". The vector field is simplified by checking for duplicates and removing the vectors that are lying on top of each other (these would ...


6

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).


6

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 ...


5

You are better off using a remote sensing application. Of course, you need to have the raster imagery on your computer. There are tons of methods that can help you determine woodland areas, such as: using Neural Networks, trained patches of imagery, supervised/unsupervised segmentation and classification. I'm not sure if this solves your problem, but it's a ...


5

Is is possible that LIDAR has been flown recently in the area? You can extract buildings this way... LIDAR would most likely be too expensive to fly yourself, probably at least 8-12k for an area that size. http://knol.google.com/k/aerial-extraction-of-roof-surfaces-for-solar-analysis# Found that article, may be of some help.


5

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 ...


4

The OpenAerialMap project was abandoned, but has recently been restarted. It will be an amalgamation of free datasets from different areas of the planet, with global datasets for areas with nothing more detailed. The global datasets used are i-cubed, and the NASA imagery. The current list of datasources used by the projects can be found here. The source ...


4

Here's a page that discusses several models of GPS that are known to work in balloon applications: http://ukhas.org.uk/guides:gps_modules It looks like the COCOM limits are imposed differently by manufacturers - some use an altitude 'OR' speed limit check, the others use 'AND'. My next question would be just how much 3D accuracy you would get out of a GPS ...


4

You can buy high resolution satellite images from DigitalGlobe or GeoEye. You are not limited to these two map providers. There are too many options outside of these. You can also try Google Earth Pro for exporting images and with your license you can use Google Earth Pro images and data for marketing purposes as long as this data is not sold to any third ...


4

The following code will take an input raster, get it's extent, and insert that extent into a polygon featureclass: import arcpy 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: ...


4

In this digital era there TONS of data out there. Some will be useful for you other useful for others. - Define what you trying to measure, and what you need to measure it. Check http://reverb.echo.nasa.gov , and its products. There are tons of scientific data expanding all over the globe. Just to expand the answer a bit: You can find a list of the ...


4

Earth Explorer would be a great place to look for satellite and aerial imagery along with data such as "Forest Carbon Sites". A really interesting project would be to look at canopy cover change over the last 30 years using Landsat imagery.


4

GPS receivers do not transmit any information they are built for receiving information. The encompassing device then uses said information depending on what the device was built for. You would be hard pressed to find a 'regular device' on the market which transmits data back to 'the satellites'. 'Talking' to 'the satellites' is not a necessary part of GPS ...


4

Google's licensing terms for the maps API is laid out here. There is a limitation on the number of map views, 25,000 according to this FAQ, but that's quite large even in the free version. That page shows you the restrictions based on the two options:: Google Maps API: "Your service must be freely and publicly accessible to end users." Google Maps API for ...


4

The cubic mode is incorporated in QGIS Master. Looks like noone had thought of adding it to the dropdownbox earlier. You can use gisinternals' GDAL standalone version to do all things that current QGIS Lisboa does not offer yet. Or simply try to insert -r cubic in the command box of QGIS.


3

Well from one image only, you can do supervised or unsupervised classification. Try a few times and see if results are good. Better way, the way I did it, was making orthophotos from images. Then I had footprint of the building so i filtered terrain from the image. Then I did classification of the pixels and created vector objects. If you have DEMs, or ...


3

In QGIS, use the Clipper raster tool, under the Raster-> Extraction menu. See: How to clip a raster with vector boundaries? You may have issues trying to clip data in a plugin layer (e.g. Openlayers) because the Clipper function requires a input graphic file (raster) from disk. Your best bet is to get the satellite imagery directly from the source and ...


3

To avoid the licensing pitfalls, you can grab plenty of Landsat TM5/ETM7 data from GLOVIS. Then, using eg bands 3 and 4 (red and near-infrarred), and possibly others, you can try to classify the image, export as a polygon and then tweak the polygon to your hearts' content. For forests, using the spatial correlation between pixels is often very useful (in ...


3

Perhaps try different source imagery. With OnEarth you can pick and choose among different band combinations. The pseudo or false colour ones highlight the differences between vegetated and non-vegetated areas better than the "natural" or "visual" colour combo (scroll down to WMS Global Mosaic use examples). The OnEarth data is available via TiledWMS, KML, ...



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