LuxCarta uses extensive know-how and cutting edge technology to deliver geo products of the highest quality.
LuxCarta uses a variety of sensors appropriate to the required spatial resolution and spectral requirements of our diverse products. For our Region Planner and Urban Planner databases we make extensive use of Landsat 7 (ETM+), Landsat 5, ASTER, SPOT 4, SPOT 5, IRS and other imagery. These sensors provide multispectral and panchromatic bands from 2.5m to 28.5m.
For our City Planner databases we employ either IKONOS (4m multispectral and 1m panchromatic), GeoEye-1 (0.41-meter panchromatic and 1.65-meter multispectral) or QuickBird (2.4m multispectral and 0.6m panchromatic) satellite imagery, in addition to digital and analogue aerial imagery.
LuxCarta undertakes active R&D in the capabilities of new sensors as these come online in order to better meet our customers’ current and future product requirements.
Orthorectification is the process of removing camera and terrain-related distortions from raw imagery through the use of sensor models, elevation data, and ground control ensuring a constant scale throughout the image. The imagery is now georeferenced and map-accurate. The orthorectified image is known as an orthoimage.
Image mosaics of adjacent orthoimages are important for feature extraction over large areas. Accurate orthorectification and advanced color balancing algorithms produce seamless orthomosaics.
A Digital Terrain Model (DTM) is a continuous model of ground-level land surface, represented by a digital raster grid with each grid cell holding an elevation value.
A DTM is a fundamental data input for radio propagation studies in that terrain blocks and reflects radio waves.
Methods for generating DTMs
1. Interpolation of vector height data from topographical mapping
In this method a DTM is interpolated from contour, spot height and geomorphological data captured from topographical mapping.
Contours, spot heights, hydrologic features and height discontinuities (breaklines) are digitized into vector form from scanned and georeferenced topographic maps. The elevation values for each feature are collected in an associated attribute table.
Using a minimum curvature spline algorithm with drainage enforcement, the attributed vector data are interpolated into a regular grid surface or raster DTM at the appropriate resolution. The DTM vertical accuracy is generally one half the source mapping contour interval.
2. Stereo image autocorrelation
Highly accurate 3D information can be derived from overlapping satellite or aerial imagery acquired from two different locations (stereo imagery). The difference in apparent location between corresponding points in the stereo images (parallax) is produced by terrain relief, which is then converted to x/y/z map coordinates using a 3D space intersection solution (collinearity principle).
Adaptive image correlation algorithms are used to automatically match corresponding points in stereoscopic imagery. The successfully matched points form x/y/z mass points from which a raster elevation surface, or Digital Surface Model (DSM), is created.
Automatic and manual filtering of above ground features such as vegetation and buildings is carried out to transform the DSM into a Digital Terrain Model (DTM) comprising only ground elevation. and advanced color balancing algorithms produce seamless orthomosaics.
Clutter refers to a Land Use/Land Cover classification of surface features which impact on radio wave propagation. These features are classed according to their physical and electrical properties.
Average obstacle height, local absorption power loss, co-efficients of correction and distance to clearing are some of the clutter-specific parameters that can be set and adjusted in propagation modeling. Image classification for RF propagation studies must accurately model clutter in terms of its influence on radio wave propagation.
Clutter is generally produced from multispectral satellite imagery where distinct classes of surface features can be delineated through spectral homogeneity and other characteristics. For certain classes such as water, forest and crop land we can employ supervised classification techniques. This is an automatic yet iterative process, the results of which are checked and rechecked for classification accuracy.
Most features of the built environment, however, are classified through manual photo-interpretation. The final clutter layer is a raster grid where each numeric cell value corresponds to a particular clutter class.
Vectors, in the geodata context, refer to a format where all map data is stored mathematically as points, lines, and areas rather than as a raster grid or image.
These vectors have location and attribute information associated with them.
Linear vectors consist of the transportation network such as streets, main roads, secondary roads, highways and railways as well as other features such as coastlines and watercourses. These features are always digitized from the orthorectified and georeferenced satellite imagery and are classified through photo-interpretation and ancillary data from topographical maps and other sources of information.
The building vectors in our City Planner databases are captured through three-dimensional feature extraction in our photogrammetric systems. These can be output as 3D objects or as 2D vectors with associated height values as attributes.
- A955 (Alcatel)
- Asset (TEOCO)
- Atoll (Forsk)
- CelPlanner (CelPlan)
- ICS Telecom (ATDI)
- NetPlan (Motorola)
- Pathloss (Contract Telecom Engineering)
- Ellipse (InfoVista)
- Planet (InfoVista)
- NetAct (NSN)
- NIR (Hexagon)
- TEMS Cell Planner (InfoVista)
- SignalPro (EDX)
- Sirenet (SGT)
- WaveSight (Wavecall)
- W-Card (NEC)
- Ranplan Professional (Ranplan Wireless)
- and more
- Windows Bitmap (.BMP)
- Generic Band Sequential (.BSQ)
- Intergraph Raster (.COT)
- USGS DEM (.DEM)
- NIMA DTED (.DTD)
- Arc/Info Import/Export (.E00)
- ER Mapper Compressed Raster (.ECW)
- ER Mapper Raster (.ERS)
- ESRI ASCII GRID (.ASC, .GRD)
- Vertical Mapper GRID (.GRD)
- Arc/Info binary GRID (.FLT)
- Erdas Imagine Image (.IMG)
- JPEG JFIF, ESRI World file (.JPG, .JPW)
- JPEG 2000, ESRI World file (.JP2, .JPW)
- ENVI Image Format(.HDR)
- MapInfo GRID (.MIG)
- PCIDSK (.PIX)
- Generic binary (.RAW)
- TIFF 6.0, GeoTIFF, ESRI World file (.TIF, .TFW)
- ASCII XYZ (.XYZ)
- Arc/Info Generate (.AGE)
- Microstation Design (.DGN)
- USGS Digital Line Graph (.DLG)
- AutoCAD DXF 14 (.DXF)
- Arc/Info Import/Export (.E00)
- MapInfo Data Interchange Format (.MIF/MID)
- ESRI Shapefile (.SHP)
- MapInfo Table (.TAB)
- OGC/Google KML (.KML)