What’s Large Scale Mapping?
Concept of large scale mapping keep changing with time and technology; In the early 1990s it was 1:10K, in the early 2000 it is better than 1:5K. in early 2010; it could be better than 1:1K!

What are the essential inputs / data for Large Scale Mapping?
The essential inputs for large scale mapping are: High accuracy image pixels of 5-40 cm; 10-50 cm height information, Corresponding Ground control points; related to the each defined scales.

How do we get these essential inputs/ data?
Conventional Ground Surveys, Aerial surveys and Satellite platforms are the prime data sources.

Current Status on LIDAR. Digital Imaging Cameras:
With the recent technology developments in Satellite imaging it is a proven fact that from a polar orbit of about 600 KM altitude a 60cm GSD imagery in panchromatic band and a 2 M GSD in MSS mode are being used by the mapping and GIS community. The next generation satellite slated for launch in 2007 is likely to provide with 40-cm PAN GSD imagery and 1.6M GSD in MSS will be available to the global users. The satellite data has proven its worth in large scale map updating and specially the Thematic Applications.

However, for a conventional base map preparation and compliance with ASPRS standards, the Aerial imaging, DEM and DTM supplemented with the Ground Control Point (GCP) with a very high accuracy are essential to prepare large scale maps.

Aerial Digital Cameras:
The invention of aerial Digital Imaging camera with CCD technology has resulted ground sample distance of 3cm-6cm imaging in pan and MSS mode. This is the state of art technology available to meet the very large scale mapping requirements.

While Satellite CCD imaging is based on push broom line scanning, aerial imaging adapts frame scanning for Photogrammetric applications.

Typical Aerial Digital Frame Camera consists of eight telescopes imaging onto corresponding CCDs. The center four telescopes image onto 9 CCDs that are assembled in processing into a single image frame. The surrounding four telescopes image corresponding four color bands onto a single CCD in each telescope. All CCDs are positionally calibrated to ensure sub-pixel band-to-band registration. All color bands collected simultaneously with panchromatic at a ground sample ratio of 3.5 to 1. To create color images at the same GSD as the panchromatic, color images are fused with the corresponding panchromatic image. There are no fringing effects due to mis-registration since color arrays are fixed and calibrated. The result is a color image experiencing no artifacts such as fringing due to imaging of moving vehicles.

The camera has a high signal-to-noise ratio due to its large dynamic panchromatic dynamic range of 14 bits and the ability to adjust exposure time and f-number to lighting conditions. The radiometric resolution or dynamic range of each color band is 12 bits or 4096 levels. This resolution is maintained through processing until the final deliverable is prepared where conversion into 3-band 24 bit images is accomplished.

The camera imaging platform is supported with and IMU and GPS unit to get a very high accuracy attitude and coordinate information.

The digital image data along with the IMU and GPS data are recorded onboard a high density recording systems such as magnetic disks, tapes and then the data is processed for map preparations through Aerial Triangulation, compilation, Ortho corrections, Stereo and GIS map preparation.

Aerial LIDAR ( Light Detection And Ranging)
To measure the accurate height information and derive the DEMS and DTMS, LIDAR is the state of art technology. Light Detection And Ranging (LIDAR) is a powerful technology aerial survey sensor becoming popular in deriving Digital Elevation Models (DEM) and Digital Terrain Model (DTM ) with a stunning accuracy of 10-15 Cm. LASER beam is the light source employed hence, LIDAR is monochromatic and has no radiometric imaging advantage.

LIDAR collection system uses a powerful laser sensor comprised of a transmitter and receiver, a geodetic-quality Global Positioning System (GPS) receiver and an Inertial Navigation System (INS) unit. The laser sensor is precision mounted to the underside of an aircraft. Once airborne, the sensor emits rapid pulses of infrared laser light, which are used to determine ranges to points on the terrain below. The time difference between the transmit pulse and the receive pulse is a measure of height.

Most LIDAR systems use a scanning mirror to generate a swath of light pulses. Swath width depends on the mirror’s angle of oscillation, and ground-point density depends on factors such as aircraft speed and mirror oscillation rate. Ranges are determined by computing the amount of time it takes light to leave an airplane, travel to the ground and return to the sensor. A sensing unit’s precise position and attitude, instantaneous mirror angle and the collected ranges are used to calculate 3-D positions of terrain points as many as 100,000 positions or “mass points” can be captured every second. This ability of LIDAR systems to capture accurate spot heights at an extremely rapid rate is the principle reason behind LIDAR's success.

However, a LIDAR system can discriminate among multiple returns from each Pulse, simultaneously surveying the canopy top and terrain. Multiple returns also can be used to determine intermediate surfaces such as treetops and power lines. In a treed area, for example, the first return may locate the top of a tree while the last return ideally locates the ground beneath the tree canopy. Multiple returns in between may represent branches, etc. All manmade and natural ground features are surveyed, including trees, buildings, cars, etc.

A color video camera records the area being scanned by the laser, and the video image is annotated with date and time. The top image features raw, unclassified LIDAR point data contours; the middle image is an example of traditional, computer-generated contours from stereo-compiled mass points and break lines; and the bottom image features

enhanced LIDAR contours with surface estimates using LIDAR points and break lines. After each day’s work, the data are downloaded and post processing commences. GPS data from the aircraft and multiple ground stations are processed together using sophisticated kinematic GPS post-processing software. The use of two or more ground stations provides quality control and improves the accuracy of the kinematic trajectory. As a result, the position (x, y, z) of an airborne GPS antenna at an interval of 0.5 or one second is calculated.

Subsequent to GPS processing, raw INS data and GPS trajectory are combined using advanced Kalman Filtering techniques. The outcome is a complete set of exterior orientation (EO) data (x, y, z, w, j, k) for sensor origin and output at a rate of 50Hz. Mass points (first and last return positions) then are computed using a combination of measured ranges, mirror scan angles and EO data. Various calibration parameters are input at this stage.

Accuracy Verification and Adjustment
Many applications, for example, contouring, require a bald-earth DTM. Unfortunately, the raw data points captured by LIDAR do not constitute a bald-earth DTM. Even though most LIDAR systems can measure "last return" data points, these "last-return" points often measure ground clutter like shrubbery, cars, buildings, and even the canopy of dense foliage. Consequently, raw LIDAR points must be post-processed to remove these undesirable returns. The degree to which this post processing is successful is critical in determining whether LIDAR is cost effective for large-scale mapping applications.

Processing of LIDAR data is quite complicated and need a number of corrections and artifact removals. There are different mathematical models to process the LIDAR data to refine the data and get accurate results. Filtering and Aerial triangulation techniques are part of LIDAR processing. Regression models and other data processing techniques are also being employed.

There are several commercial packages available for the post processing of LIDAR measurements. The (Optech, 2001) LIDAR system comes with a post-processing package. The parameter set for the algorithm and the artifacts observed in the processed data suggest that the algorithm is based on a morphological filter. (TerraSolid, 2001) offers a variety of LIDAR processing modules, including TerraScan for the filtering and thinning of LIDAR data. This package includes different methods for

slope-based filtering and thinning of LIDAR data. (INPHO GmbH, 2001) offers a product called SCOP for the derivation of DTM’s and contours from various sources, including LIDAR data. The approach for the LIDAR data processing is based on the method described in (Kraus, Pfeifer, 1998).

Sanborn has developed software packages e.g.; Filtering And Surface Estimation, (FASE); Process for Lidar Aerial Triangulation (PLAT) to improve the LIDAR data product accuracy.

The US FEMA has prescribed certain formats and standards for LIDAR products. Following are some of the Commercial LIDAR product specifications available to the global users:

High Density LiDAR
0.7 meter average point spacing ; Collection within 40 km baseline Scan frequency and angles based on project specification;¦ Vertical Accuracy: Bare Earth: 15 cm RMSE ; Vegetation: 27 cm RMSE ; Horizontal Accuracy:0.5 meter RMSE ; for bare earth elevation models: 95% of artifacts or more removed depending on terrain and vegetation 98% of all outliers removed ; 97% of all vegetation removed

99% of all buildings removed

Standard LiDAR:
1.4 meter average point spacing ; Scan frequency and angles based on project area terrain and land cover ; Vertical Accuracy: Bare Earth: 18.5 cm RMSE Vegetation: 37 cm RMSE ; Horizontal Accuracy: 1 meter RMSE ; for bare earth elevation models: 89% of artifacts or more removed ;depending on terrain and vegetation ; 90% of all outliers removed ; 90% of all vegetation removed 93% of all buildings removed

Filtering processes for FEMA LiDAR vary from basic LiDAR. When filtering FEMA LiDAR, users can expect artifacts removal at 90 percent or better. About 95 percent of LiDAR collection outliers will be removed. The resulting bare earth model yields 95 percent of all vegetation removed and 98 percent of all buildings removed. Data quality requires the use of cross-flight verification and a single mission calibration as outlined in FEMA’s guidelines and specifications.

LIDAR Application Potentials:
Liar advances and a better understanding of the technology have greatly improved the usefulness of LiDAR as a valuable mapping tool. Potential applications include, but are not limited to, the following:

Flood Mapping and Planning: The high-resolution elevation data provided by LiDAR technology provides valuable insight into flood-prone areas and how

to mitigate flood damage. The Federal Emergency Management Agency (FEMA) has initiated a modernization of floodplain maps throughout the United States using LiDAR data in its Map Modernization initiative.

• Disaster Management: LiDAR is used to assess damage and support infrastructure rebuilding after a disaster.
  
• Coastal Erosion: LiDAR data help detect coastal changes to study and remediate coastal erosion.
  
• Aviation Safety: LiDAR is used to identify vertical airfield obstructions to aviation.
  
• Forestry: LiDAR can detect forest canopy change to support forest management.
  
• Nautical Charting: LiDAR is used to measure seabed depths and topographic elevations for navigation, nautical chartering, rapid environmental assessment and regional costal zone mapping.