Softening the blow of complex Geodata. Striving ourselves to ease up the understanding and trying to make easier those complex procedures we usually get stuck in!
I guess we all can make a DTM out of many sources but SRTM is one of the most common ones, right?. Then let’s learn from this very simple approach how close we are from the SRTM raw data.
Selecting a not very big representative area to be able to handle it,
exporting raster to polygon (from SRTM 3 arcsec/90m) dataset 1
exporting raster to polygon 30m (our DTM dataset) dataset 2
exporting to POIs 30m (our DTM dataset) dataset 2b
Spatial join POIs dataset 2b vs dataset 1
RMSE
visualizing delta using a density map/gaussian kernell +appropriate symbolization
In yellow we see theres a full correspondence between SRTM and our DTM dataset and in blue there’s a ‘hole’ and in red there’s a ‘mountain’, this means it’s in here where the shift is more important.
This way we can highlight if sources are OK.
It’s simple but it works. How do you like it?. Please feel free to send some feedbak.
(Software used: ArcGIS 10.1, Global Mapper 13.2)
Cheers,
Alberto Concejal
MSc GIS, QC
density maps parameters
Spatial join between both DTM datasets
Density map for highlighting differences between both datasets (ours and SRTM’s)
RMSE. It’s not too big so there’s need to visualize to find potential bizarre spots
The height accuracy of the collected LiDAR data can be verified by comparing with independently surveyed ground control points on hard, flat, open surfaces. It is essentially just calculating the height differences for all the control points and then determining the height root mean squared error (RMSE) or differences. Most LiDAR processing software have the reporting function built-in. However, plain Microsoft Excel can also do the job (except for extracting the elevation from the LiDAR data).
Assuming that you are able to calculate the height differences for all the control points and place in a spreadsheet as shown in the figure below. I have a column of delta Z values in column A.
Then to calculate the RMS value for the elevation differences, I can do the following.
In a cell, type in the formula:= SQRT(SUMSQ(A2:A18)/COUNTA(A2:A18))where A2:A18 are the values from cell A2 to A18 in the spreadsheet. Simply replace these with the actual locations on your spreadsheet.
My very good friend Fernando (a spanish/english like friend of mine:-)) introduced me this magnificent route planner he was using at that moment (three years ago!!!!)… map24.com had a very interesting 3D engine, very easy to use and very reliable… I’ve been using it since but it has never been one of the most famous among route planners in the market…
I strongly recommend it and i really like it a lot! enjoy!
Please, dont forget to turn on your speakers. Even if i first used a different score and Youtube’s elfs told me not to use it (because of copyright authoring) I have chosen this music from their stuff and this is what I finally got…
Hope you like it.
Alberto
BA Geography
MSc GIS and Remote Sensing
GIS Technician and Multimedia Designer
albertoconcejal -at -gmail.com
Barcelona (Spain), Winnipeg (Canada), Las Vegas (USA), Moscow (Russia), Durban (South Africa), Vancouver (Canada) and Tokyo (Japan)… these were some of my Terrain view’s 3D scenarios published by Computamaps, a South African company I worked in not so long ago. By the way it was one of my best professional experiences ever… I enjoyed joining them a lot and It was very difficult leaving them (mainly because of Cynthia’s fruit salads;-)… Miss you guys a lot!!!
Hope you liked them.
Alberto
BA Geography
MSc GIS and Remote Sensing
GIS Technician
albertoconcejal -at -gmail.com
I used to have lunch every thrusday there, at the ‘Restaurante Asiático SHENG’. I strongly recommend you ‘entremeses’ (In China you will find them as ‘dim-sum’) and Cantonese duck or Hong Kong duck (this was slightly spicy thou). Very good food, very fast service and pretty cheap menu: 10,7 €… ideal for an IT worker!.
These views were rendered using ‘V-RAY’ for Sketch-up.
And now, Let’s go to Google Earth!
(I have modified my kml using a extruded placemark we talked about a few posts ago).
Now Let’s have our business in 3D!!!!!!!!!!!!!!!.
Hope you like it.
Alberto
BA Geography
MSc GIS and Remote Sensing
GIS Technician
albertoconcejal -at -gmail.com
First of all, what is KML?. KML is a file format used to display geographic data in an Earth browser, such as Google Earth, Google Maps, and Google Maps for mobile. You can create KML files to pinpoint locations, add image overlays, and expose rich data in new ways. KML is an international standard maintained by the Open Geospatial Consortium, Inc. (OGC). You can choose wether authoring directly from Google Earth itself or you can try to understand the code and doing it by yourself… You can draw placemarks (using descriptive HTML to personalize them), ground overlays, paths, polygons… Let’s start with the placemark:
-> Simple placemark
<?xml version=”1.0″ encoding=”UTF-8″?>
<kml xmlns=”http://www.opengis.net/kml/2.2″>
<Placemark>
<name>Simple placemark</name>
<description>Attached to the ground. Intelligently places itself
at the height of the underlying terrain.</description>
<Point>
<coordinates>-122.0822035425683,37.42228990140251,0</coordinates>
</Point>
</Placemark>
</kml>
An XML header. This is line 1 in every KML file. No spaces or other characters can appear before this line.
A KML namespace declaration. This is line 2 in every KML 2.2 file.
A Placemark object that contains the following elements:
A name that is used as the label for the Placemark
A description that appears in the “balloon” attached to the Placemark
A Point that specifies the position of the Placemark on the Earth’s surface (longitude, latitude, and optional altitude)
<description>Attached to the ground. Intelligently places itself
at the height of the underlying terrain.</description>
you use the CDATA element, you can write HTML and avoiding Google Earth from parsing the code incorrectly:
<description>
<![CDATA[
<h1>CDATA Tags are useful!</h1>
<p><font color=”red”>Text is <i>more readable</i> and
<b>easier to write</b> when you can avoid using entity
references.</font></p>
]]>
</description>
How differences in population count could have implications for service provision, allocation of funds, & political representation. Source: https://www.caliper.com/census-differential-privacy-maps/ The U.S. Census Bureau has changed the way it ensures privacy for the 2020 Census. The new method is called Differential Privacy (DP). To help people assess s […]
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