The system has 3 types of clutter which you can use to enhance accuracy, as well as LIDAR. The simplest type, random clutter, needs no demonstration as it simply raises the surrounding terrain by a user defined amount.
Here we have a coastal site next to a large city, San Francisco. Using just the SRTM terrain data, you will see accurate coverage out to sea and to the mountains but the coverage over the city will be overly optimistic due to a lack of clutter within the SRTM data. Concrete buildings stop RF so it would be fatal to not factor them in, especially if the coverage over the city is of interest.
To use the point clutter feature, create a KML overlay in Google earth (or equivalent GIS system) containing either points, lines or a polygon. Each point should have a height which is used by the system to represent a small obstacle like a building or a wind turbine. Upload the KML file (Make sure you save as .kml not .kmz) via the upload button next to the Point clutter option in the 'Environment' menu. Now within the web interface's 'Environment' menu, select 'Point clutter' to use the (private) KML clutter you uploaded to the database. You should see speckles with radio shadows in your coverage caused by your obstacles. The higher the obstacle, the deeper the shadow. In this case, obstacles have been placed in the middle of the bay.
The University of Maryland/NASA Landcover data in the system will raise the terrain according to the terrain's classification. Here the 'Landcover clutter' button is switched to ON and the prediction repeated. Now the coverage is drastically reduced over urban areas which is to be expected as the tower is not very high and the city is dense.
ncols 5000 nrows 5000 xllcorner -3.29575481117924 yllcorner 51.4218401957316 xurcorner -3.15423641576402 yurcorner 51.5132387803528 cellsize 2 NODATA_value 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...If you need help converting LIDAR tiles and would like yours adding to the system please contact support. A fee may be requested if substantial re-formatting is required. Priority will be awarded to tiles which are correctly formatted like the example.
gdalwarp -s_srs epsg:27700 -t_srs epsg:4326 -co "TILED=YES" -co "TFW=YES" -co "COMPRESS=LZW" -te $WEST $SOUTH $EAST $NORTH -ts 5000 5000 myrasterdata.tiff myrasterdata.4326.tiff
gdal_translate -of AAIGrid -ot Int32 myrasterdata.tiff myrasterdata.asc