International Archives of the Photogrammetry, Remote S 
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ensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 Intern 
E ean Ba. Istanbus 2004 Ao fi 
Environmental Agency was examined. The purpose was to Figure 5. The IFSAR DSM and DTM are shown in yellow and 
check the IFSAR performance according to the definitions red respectively. The lidar DSM is shown in green. It has 
established for testing Core products (as described in the partially penetrated the wooded area. 
previously referenced product handbook) and essentially T 
provides a baseline performance level. The method 3.1.2 Economic Model: The possibility to reduce costs to the y 
encompassed DSMs and DTMs. DSM data were segmented, end-user are enhanced in these national programs. Because they th 
using the ORI, to exclude forests, structures, etc., from the test are ‘seamless’ data sets there is a demand in the first instance by mr 
statistics so that the DSM data would reflect bare earth entities who wish to have a single nation-wide consistent data a 
performance. Somewhat lower mean RMSE values were set for their applications. In this instance, the first customer was h 
obtained for the DTMs than in the Duncan study, probably Norwich Union, a major insurance company whose specific > 
reflecting the fact that no urban data were included. We show a interest is the application of the DSM, DTM and ORI in 9 
single example of a 2km test site in Figures 4 and 5. support of flood modelling which in turn is part of their flood S 
3 x = risk analysis activities (Saunders, et al, 2001). By retaining h 
license rights, Intermap has able to resell the data from its data p 
base at a price that is considerably lower than the cost would fk 
have been for a single customer/owner. The price impact is 
illustrated in Figure 6, where unit price (USS/kmsq) is T 
contrasted for different technologies addressing levels of e 
vertical accuracy as noted in section ] of this paper. For T 
example, a customer now can license a 10,000kmsq DTM in N 
Britain for about US $6/kmsq. h 
I'¢ 
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Kilometers i NRE 
Figure 4. Counter-clockwise from upper left, IFSAR ORI, som t [nie ua qe Pr ma D 
IFSAR DSM, (IFSAR — Lidar) DSM difference surface with Figure 6. Unit cost comparison of DEMSs as a function of 
scale bar, Lidar DSM (Mercer, et. al.. 2003). typical vertical accuracies addressed by .. various x 
technologies. The impact of the NextMap type of program 
The relative noise difference (sigma ~41 cm) between the two is shown to reduce cost to i end user by B factor 9f 5-4 
DSMs is clear in the difference surface as is the response to the compared i the custom project approach: The example } 
forests. A profile across one of the wooded areas of Figure 4 is shown here is based upon à requirement: for 10,000kmsq 
anti Es & ; ; : Tre (smaller areas would cost more, larger areas less). The range 2 
provided in Figure 5. After masking out the non-bare areas, the uh = > 1 
mean offset between IFSAR and lidar was 21cm in this example, of values encompasses other 2 Nextmap amas and | 
with total RMSE = 50 cm. specifications as well as the specifics of license type (see 
www.intermaptechnologies.com for details). 
Elevation Prafite 3.2 The Next ‘NextMap’ Program ; 
Intermap announced at the ASPRS meeting in Charlston, SC, ( 
2003, that it would be undertaking a NextMap USA program, s 
roughly modelled on the NextMap Britain experience. This is a 
e major challenge given the size of the USA (approximately 9 | 
3 million kmsq). Since that date it has begun the effort and is | 
scheduling it to occur over a period of 4-5 years. New sensors : 
are being built to facilitate the effort and to be able to continue ‘ 
to address other programs. 
Also underway is a NextMap program in Indonesia with Type I 
0 50 100, +190, 2000 3260 900. 3850 400 ITI product specifications (Table 2). 
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