# Updating Large Scale Topographic Databases in Italian Urban Areas with Submeter QuickBird Images

Authors: Marco Gianinetto
Journal: International Journal of Navigation and Observation
 (2008)

---

## Abstract

This paper presents some tests for the updating of the Italian Raster Regional
Technical Map (RRTM) at 1: 10000 scale and of the Italian Vector Technical Map (VTM) at
1: 5000 scale using a submeter Quickbird imagery collected in a near flat urban area of the
Northern Italy. Results show that the updating of the 1: 10000 scale cartography was always
possible, while a rigorous updating of 1: 5000 scale cartography was possible only in few
situations. Results are discussed with reference to the specifications required for the 1: 10000
scale and 1: 5000 scale topographic databases (DBs) production of the Lombardia Region,
which is a reference for metric mapping purposes in Italy.


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## Body

## 1. Introduction

Urban planning using digital aerial photos has become very
popular in the past several years, but
the use of satellite images for urban
mapping and cartographic update was limited by image resolution [1], even if
mapping agencies shown great interest in its possible use for their data
collection programs. From the end of 1990s, with the successful launch of the
first commercial high-resolution satellite, IKONOS, a new source of imagery has
been available to the civilian spatial-data users and many proposals concerning
the use of satellite images for map updating started to appear in literature
[2–5].There are several advantages of updating existing
topographic maps using satellite images instead of aerial photos. First of all,
a single satellite scene covers a large area (from less than 65 km2
for OrbView-3 to more than 270 km2 for QuickBird). Hence, the survey
costs may be reduced. Moreover, with few exceptions (e.g., EROS-B), satellite
data are multispectral, so they can be also used for thematic mapping purposes
(this also applies to the photogrammetric digital cameras such as Leica ADS-40
or Z/I Imaging DMC). Finally, and that is the most important factor, remotely
sensed images of a specific site can be collected with very short revisit time.
Consequently, it is possible to quickly update the existing cartography, also
in areas that were previously too remote or too dangerous to reach using
conventional aerial photography.The geometric correction of such data has been well studied
in past years [6–12] and orthorectified images have proved to be a useful data
source for the creation and the updating of mapping products [3]. However, the high-resolution
images captured from satellites have been mainly used in the mapping and
surveillance of otherwise inaccessible areas, such as areas of military
conflict as Afghanistan or Iraq [13, 14]. Some tests have also been carried on
to generate new maps in parts of the world that do not have a tradition of
detailed mapping [15].In well-mapped countries, such as Western Europe, detailed
topographic databases (DBs) have been (or are being) built using both field
surveys and aerial surveys. Therefore, the main task of the national mapping
agencies is the updating of the existing topographic databases (DBs) rather
than the creation of new topographic maps. The results of an OEEPE (now
EuroSDR) report (2003), undertaken by several European mapping agencies and
institutions about the use of IKONOS data for mapping purposes, suggested that
this kind of images can be potentially used for cartographic updating at scales
between 1 : 50 000 and
1 : 10 000 and a study of
Ordnance Survey (2004), Great Britain’s National Mapping Agency, found that
QuickBird imagery can be used to update maps between 1 : 10 000 scale and  1: 6000 scale
as long as small linear features were excluded from the mapping specifications
[3]. Moreover, only recently high-resolution satellite images have been adopted
as a data source in large projects, such as the IKONOS complete coverage of the
Flemish region (13,512 km2) taken in 2003
for the Flemish Land Agency—Geographical
Information System Flanders Support Centre Department [16].Regarding the extraction of topographic features from high-resolution
images, in past years many tests have been done primarily for road networks extraction and buildings
detection (for more details, see [17]) or for GIS updating [18, 19]. Topographic map and topographic
database updating was also considered [3, 5, 20–22], but very few
literature exists on the comparison between the mapping accuracy which can be
obtained from high-resolution satellite images and the actual requirements for
large scale map production in well-mapped countries.This paper presents some tests for the updating of the
Italian Raster Regional Technical Map (RRTM) at1:10000 scale and of the Italian Vector Technical
Map (VTM) at 1:5000 scale using submeter QuickBird imagery. Results are discussed
with reference to the specifications required for the traditional 1 : 10 000 scale and
1 : 5000 scale
topographic DBs production of the Lombardia Region [23], which is a reference
for metric mapping purposes in Italy.


## 2. Italian Mapping Specifications

Italy is a well-mapped country and the infrastructures
required to capture and process aerial photography are well established. The
basic cartography for the regional planning is the Regional Technical Map (RTM)
at 1 : 10 000 scale and
all the country is mapped at that scale using traditional field surveys and
aerial surveys. The production and the updating of the RTM are in charge of the
regional governments (20 in all), and each one makes its rules and
specifications for its production, even though in a technical roundtable state,
regions and local administrations are trying to define some common
specifications for general purposes topographic DBs [24].Today, aerial digital orthophotos are largely employed in
many Italian Regions as supplementary data to traditional cartography and also
high-resolution satellite data are being increasingly used, however, until now,
no official cartographic products have been produced in Italy from them. The
reason may be found in the lack of rules. In fact (with few exceptions), at the
present time there is no specific legislation in Italy for the creation and the
updating of topographic maps using digital orthophotos or satellite images.The main problem that users must cope with when working
with the RTM is its oldness: the RTM is supposed to be updated approximately
every 10–15 years,
consequently it may be very dated. In this context, high-resolution remote
sensing could be easily used for a quick updating of the existent RTMs and
associated databases of geospatial information.Because no specific rules actually exist, here we refer to
the mapping technical specifications for the traditional1:10000  scale and 1 : 5000 scale
topographic DBs of the Lombardia Region, which is the reference for metric
mapping purposes. In particular, the  tests carried on refer to the technical
specifications version 01/01 [27] that defines the planimetric tolerance
allowed for the positioning of single points in a map as follows:(1)(Nm(p)−Ng(p))2+(Em(p)−Eg(p))2≤2.00m,for1:5000scalemap,(Nm(p)−Ng(p))2+(Em(p)−Eg(p))2≤4.00m,for1:10000scalemap,   where Nm(p) is the north coordinate of
the point  p measured on the map,  Em(p) is the east
coordinate of the point p measured on the map,  Ng(p) is the north coordinate of the point p measured on the ground,  Eg(p) is the east coordinate of the point p measured on the ground, and the planimetric tolerance allowed for the distances between two points in a map as follows:
(2)|Dm(p,q)−Dg(p,q)|≤(2.00+Dg(p,q)1000)m,forDg≤800m,for1:5000scalemap;|Dm(p,q)−Dg(p,q)|≤2.80m,forDg>800m,for1:5000scalemap,|Dm(p,q)−Dg(p,q)|≤(4.00+Dg(p,q)1000)m,forDg≤1500m,for1:10000scalemap,|Dm(p,q)−Dg(p,q)|≤5.50m,forDg>1500m,for1:10000scalemap, where  Dm(p,q) is the distance between the
points  p and  q measured on the map, Dq(p,q) is
the distance between the points p and q measured on the ground.When a map is produced or updated it must be tested. This
operation is carried out by measuring a set of points and distances on the map
and their correspondent on the ground, measured with a higher precision. As far
as the statistical distribution of the residuals is always considered to be a
Gaussian function, a map is approved when 95% of all the testing measures fall
within the tolerances defined with (1)-(2).Because here we refer only to the planimetric updating of
existing maps (not derived from stereo pairs), the tolerances for the elevation
were not taken into consideration.


## 3. Case Study

A case study was carried out in the council area of Cesano
Maderno, one of the municipalities in the surroundings of Milano (Lombardia),
in northern Italy. The territory of Cesano Maderno is almost flat and has a
full surface of 11.46 km2. The elevation range varies from 194 m to
228 m above sea level, increasing from south to north, and the urban structure
is that typical of small Italian towns: a dense old town centre with a sparse surrounding
of detached house and industrial/commercial areas.


### 3.1. Dataset

The source dataset used was composed of the following:(1)
one 0.70 m panchromatic QuickBird image of the surroundings of
Milano (Lombardia, Italy), collected on May 3, 2003 with 15° off-nadir angle (upper
left corner latitude 45.7138° upper left corner longitude 9.3008°);(2)
one 2.80 m multispectral QuickBird image of the surroundings of
Milano, collected on May 3, 2003 with 15° off-nadir angle (upper left corner
latitude 45.7138° upper left corner longitude 9.3008°);(3)
the Raster Regional Technical Map (RRTM) at1:10000  scale, produced in 1994
traditional aerial surveys and supplied by the Cartographic Office of the
Lombardia Regional Government;(4)
a vector cartography of the city of Cesano Maderno at 1 : 2000
scale, produced in 1999 using traditional aerial and topographic surveys and
supplied by the municipality of Cesano Maderno (see
Figure1);(5)
the Vector Technical Map (VTM) of the city of Cesano Maderno at
1 : 5000 scale, produced as generalization of the 1 : 2000 scale vector cartography
and supplied by the municipality of Cesano Maderno.Figure 1
1:2000 scale vector cartography of Cesano
Maderno.Accuracy of source raster and vector cartography is shown as follows.(1)
Vector cartography (1 : 2000 scale): better than 0.80 m in plan
and better than 0.60 m in elevation;(2)
Vector Regional Technical Map (1 : 5000 scale): better than 0.80 m
in plan because it is derived as generalization of the 1 : 2000 scale vector map;(3)
Raster Regional Technical Map (1 : 10 000 scale): better than 4 m in plan.


### 3.2. Data Processing

#### 3.2.1. Pan-Sharpening

Image fusion is the combination of
multiple images into composite products, used to reveal more information than
that of individual input images. Pan-sharpening describes a process of
transforming a set of low spatial resolution multispectral (color) images into
higher spatial resolution color images by fusing a coregistered fine spatial
resolution panchromatic (black and white) image.During the past years, different
pan-sharpening methods have been developed, such as those based on multiresolution
wavelet transforms, on the principal component analysis transforms, or on the intensity-hue-saturation
transforms. Recently, with the increasing use of high-resolution satellite
images for urban analysis, several new pan-sharpening techniques have been
proposed with the aim of preserving the color fidelity in the pan-sharpened
images [25].In urban mapping applications,
pan-sharpening can be successfully used to spectrally enhance the high-resolution
panchromatic satellites data by adding the color information derived from the
multispectral images. As a consequence, pan-sharpened images offer a superior
capability for the human interpretation of the territory.Since in this study the map updating was carried out by
manual digitization, the QuickBird image was preprocessed by fusing together
the 0.70 m panchromatic band
and the 2.80 m multispectral bands using the
Gram-Schmidt spectral sharpening method, invented by Laben and  Brower in 1998
and patented by Eastman Kodak [26]. Further analysis was performed on the
pan-sharpened imagery.


#### 3.2.2. Digital Elevation Model Generation and Ground Control Points Selection

When not using stereo couples, a digital
elevation model (DEM) is required for the geometric correction of data.
Consequently, a 50 m grid spacing DEM was derived by interpolating many
hundreds of ground elevation data extracted from the 1 : 2000 scale cartography (see
Figure2).Figure 2
Digital elevation model derived from the 1 : 2000 scale vector cartography
of Cesano Maderno. The elevation range varies from 194 to 228 meters above sea
level, increasing from south to north.Again from the 1 : 2000 scale cartography, 110 ground points
characterized by an accuracy better than 0.80 m in plan and with a regular
geometric distribution over the whole study area were selected and measured.
Subsequently, these points were divided into two datasets: (1) 80 ground control
points (GCPs) used for the image orthorectification and (2) 30 points used as independent
check points (ICPs) for evaluating the orthorectification accuracy (see
Figure3).Figure 3
Geometric distribution of ground control points (red crosses) and independent
check points (yellow crosses).


#### 3.2.3. Image Orthorectification

Because no metadata or Rational
Polynomial Coefficients (RPCs) were available, the QuickBird image was
geometrically corrected using two different nonparametric techniques: (1) the
Rational Function Model (RFM) with RPCs computed from GCPs and (2) the spline
interpolation computed from GCPs.Several orthoimages were generated both using the RFM and
the spline methods and using an increasing number of GCPs: from 40 to 80. Their
accuracy was checked by manually measuring on the corrected images the
cartographic coordinates of the 30 ICPs previously identified.
Table1 shows a
summary of results.Table 1
Accuracy of the orthoimages generated using the rational function model
and the spline interpolation methods, calculated on the basis of 30 independent
check points.
MethodGCPsRMSE (m)(nr.)EastNorthOverall400.622.142.22500.581.411.52RFM600.551.111.24700.511.121.23800.501.101.21400.601.551.66500.551.451.55Spline600.611.491.61700.601.501.61800.571.511.62In our tests, the RFM method led to superior results than
the spline method. With respect to splines, the accuracy of the orthoimages did
not vary significantly from the use of 40 GCPs to 80 GCPs. In the former case, it was obtained a 0.60 m root mean square error for the East coordinate (RMSEE), 1.55 m root mean square error for the North coordinate (RMSEN), and 1.66 m overall root mean square error (RMSE), while in the latter case it was observed a 0.57 m RMSEE, 1.51 m RMSEN, and 1.62 m overall RMSE.Concerning the RFM method, the best result was obtained
using a full 3rd-order RFM using 80 GCPs (0.50 m RMSEE,
1.10 m RMSEN,
and 1.21 m overall RMSE). With increasing the number of GCPs from 40 to 60, the
accuracy of the geometric correction passed from 0.62 m RMSEE, 2.14 m
RMSEN, and 2.22 m overall RMSE to 0.55 m for RMSEE,
1.11 m RMSEN, and 1.24 m overall RMSE, respectively. No further
significant improvement was instead observed using more GCPs.


#### 3.2.4. Vector Extraction

Vector cartography for three
quarters of Cesano Maderno was manually extracted from the pan-sharpened
orthorectified QuickBird image and then used for simulating a topographic DB
updating of both the 1 : 5000 scale VTM and the 1 : 10 000 scale RRTM. The simulation was carried out by
wiping out some quarters from the existing cartography and replacing them with
the vector layers extracted from the QuickBird image.From the 12 classes defined for buildings in the mapping
technical specifications and from the 11 classes defined for roads, the
simulation took into consideration only the following 8 classes (one for roads
and six for buildings).Class 1:
roads.Class 2:
civilian
buildings.Class 3:
local
government buildings;Class 4:
commercial
buildings.Class 5:
industrial
buildings.Class 6:
churches
and places of cult.Class 7:
shack/roof.Furthermore, buildings were digitized from tops because
footprints were rarely visible due to both the dense urbanization texture and
the presence of long shades caused by the tilted viewing geometry.Finally, results were
assessed by comparing the QuickBird derived vectors with the higher precision
1 : 2000 scale cartography.


## 3.1. Dataset

The source dataset used was composed of the following:(1)
one 0.70 m panchromatic QuickBird image of the surroundings of
Milano (Lombardia, Italy), collected on May 3, 2003 with 15° off-nadir angle (upper
left corner latitude 45.7138° upper left corner longitude 9.3008°);(2)
one 2.80 m multispectral QuickBird image of the surroundings of
Milano, collected on May 3, 2003 with 15° off-nadir angle (upper left corner
latitude 45.7138° upper left corner longitude 9.3008°);(3)
the Raster Regional Technical Map (RRTM) at1:10000  scale, produced in 1994
traditional aerial surveys and supplied by the Cartographic Office of the
Lombardia Regional Government;(4)
a vector cartography of the city of Cesano Maderno at 1 : 2000
scale, produced in 1999 using traditional aerial and topographic surveys and
supplied by the municipality of Cesano Maderno (see
Figure1);(5)
the Vector Technical Map (VTM) of the city of Cesano Maderno at
1 : 5000 scale, produced as generalization of the 1 : 2000 scale vector cartography
and supplied by the municipality of Cesano Maderno.Figure 1
1:2000 scale vector cartography of Cesano
Maderno.Accuracy of source raster and vector cartography is shown as follows.(1)
Vector cartography (1 : 2000 scale): better than 0.80 m in plan
and better than 0.60 m in elevation;(2)
Vector Regional Technical Map (1 : 5000 scale): better than 0.80 m
in plan because it is derived as generalization of the 1 : 2000 scale vector map;(3)
Raster Regional Technical Map (1 : 10 000 scale): better than 4 m in plan.


## 3.2. Data Processing

### 3.2.1. Pan-Sharpening

Image fusion is the combination of
multiple images into composite products, used to reveal more information than
that of individual input images. Pan-sharpening describes a process of
transforming a set of low spatial resolution multispectral (color) images into
higher spatial resolution color images by fusing a coregistered fine spatial
resolution panchromatic (black and white) image.During the past years, different
pan-sharpening methods have been developed, such as those based on multiresolution
wavelet transforms, on the principal component analysis transforms, or on the intensity-hue-saturation
transforms. Recently, with the increasing use of high-resolution satellite
images for urban analysis, several new pan-sharpening techniques have been
proposed with the aim of preserving the color fidelity in the pan-sharpened
images [25].In urban mapping applications,
pan-sharpening can be successfully used to spectrally enhance the high-resolution
panchromatic satellites data by adding the color information derived from the
multispectral images. As a consequence, pan-sharpened images offer a superior
capability for the human interpretation of the territory.Since in this study the map updating was carried out by
manual digitization, the QuickBird image was preprocessed by fusing together
the 0.70 m panchromatic band
and the 2.80 m multispectral bands using the
Gram-Schmidt spectral sharpening method, invented by Laben and  Brower in 1998
and patented by Eastman Kodak [26]. Further analysis was performed on the
pan-sharpened imagery.


### 3.2.2. Digital Elevation Model Generation and Ground Control Points Selection

When not using stereo couples, a digital
elevation model (DEM) is required for the geometric correction of data.
Consequently, a 50 m grid spacing DEM was derived by interpolating many
hundreds of ground elevation data extracted from the 1 : 2000 scale cartography (see
Figure2).Figure 2
Digital elevation model derived from the 1 : 2000 scale vector cartography
of Cesano Maderno. The elevation range varies from 194 to 228 meters above sea
level, increasing from south to north.Again from the 1 : 2000 scale cartography, 110 ground points
characterized by an accuracy better than 0.80 m in plan and with a regular
geometric distribution over the whole study area were selected and measured.
Subsequently, these points were divided into two datasets: (1) 80 ground control
points (GCPs) used for the image orthorectification and (2) 30 points used as independent
check points (ICPs) for evaluating the orthorectification accuracy (see
Figure3).Figure 3
Geometric distribution of ground control points (red crosses) and independent
check points (yellow crosses).


### 3.2.3. Image Orthorectification

Because no metadata or Rational
Polynomial Coefficients (RPCs) were available, the QuickBird image was
geometrically corrected using two different nonparametric techniques: (1) the
Rational Function Model (RFM) with RPCs computed from GCPs and (2) the spline
interpolation computed from GCPs.Several orthoimages were generated both using the RFM and
the spline methods and using an increasing number of GCPs: from 40 to 80. Their
accuracy was checked by manually measuring on the corrected images the
cartographic coordinates of the 30 ICPs previously identified.
Table1 shows a
summary of results.Table 1
Accuracy of the orthoimages generated using the rational function model
and the spline interpolation methods, calculated on the basis of 30 independent
check points.
MethodGCPsRMSE (m)(nr.)EastNorthOverall400.622.142.22500.581.411.52RFM600.551.111.24700.511.121.23800.501.101.21400.601.551.66500.551.451.55Spline600.611.491.61700.601.501.61800.571.511.62In our tests, the RFM method led to superior results than
the spline method. With respect to splines, the accuracy of the orthoimages did
not vary significantly from the use of 40 GCPs to 80 GCPs. In the former case, it was obtained a 0.60 m root mean square error for the East coordinate (RMSEE), 1.55 m root mean square error for the North coordinate (RMSEN), and 1.66 m overall root mean square error (RMSE), while in the latter case it was observed a 0.57 m RMSEE, 1.51 m RMSEN, and 1.62 m overall RMSE.Concerning the RFM method, the best result was obtained
using a full 3rd-order RFM using 80 GCPs (0.50 m RMSEE,
1.10 m RMSEN,
and 1.21 m overall RMSE). With increasing the number of GCPs from 40 to 60, the
accuracy of the geometric correction passed from 0.62 m RMSEE, 2.14 m
RMSEN, and 2.22 m overall RMSE to 0.55 m for RMSEE,
1.11 m RMSEN, and 1.24 m overall RMSE, respectively. No further
significant improvement was instead observed using more GCPs.


### 3.2.4. Vector Extraction

Vector cartography for three
quarters of Cesano Maderno was manually extracted from the pan-sharpened
orthorectified QuickBird image and then used for simulating a topographic DB
updating of both the 1 : 5000 scale VTM and the 1 : 10 000 scale RRTM. The simulation was carried out by
wiping out some quarters from the existing cartography and replacing them with
the vector layers extracted from the QuickBird image.From the 12 classes defined for buildings in the mapping
technical specifications and from the 11 classes defined for roads, the
simulation took into consideration only the following 8 classes (one for roads
and six for buildings).Class 1:
roads.Class 2:
civilian
buildings.Class 3:
local
government buildings;Class 4:
commercial
buildings.Class 5:
industrial
buildings.Class 6:
churches
and places of cult.Class 7:
shack/roof.Furthermore, buildings were digitized from tops because
footprints were rarely visible due to both the dense urbanization texture and
the presence of long shades caused by the tilted viewing geometry.Finally, results were
assessed by comparing the QuickBird derived vectors with the higher precision
1 : 2000 scale cartography.


## 3.2.1. Pan-Sharpening

Image fusion is the combination of
multiple images into composite products, used to reveal more information than
that of individual input images. Pan-sharpening describes a process of
transforming a set of low spatial resolution multispectral (color) images into
higher spatial resolution color images by fusing a coregistered fine spatial
resolution panchromatic (black and white) image.During the past years, different
pan-sharpening methods have been developed, such as those based on multiresolution
wavelet transforms, on the principal component analysis transforms, or on the intensity-hue-saturation
transforms. Recently, with the increasing use of high-resolution satellite
images for urban analysis, several new pan-sharpening techniques have been
proposed with the aim of preserving the color fidelity in the pan-sharpened
images [25].In urban mapping applications,
pan-sharpening can be successfully used to spectrally enhance the high-resolution
panchromatic satellites data by adding the color information derived from the
multispectral images. As a consequence, pan-sharpened images offer a superior
capability for the human interpretation of the territory.Since in this study the map updating was carried out by
manual digitization, the QuickBird image was preprocessed by fusing together
the 0.70 m panchromatic band
and the 2.80 m multispectral bands using the
Gram-Schmidt spectral sharpening method, invented by Laben and  Brower in 1998
and patented by Eastman Kodak [26]. Further analysis was performed on the
pan-sharpened imagery.


## 3.2.2. Digital Elevation Model Generation and Ground Control Points Selection

When not using stereo couples, a digital
elevation model (DEM) is required for the geometric correction of data.
Consequently, a 50 m grid spacing DEM was derived by interpolating many
hundreds of ground elevation data extracted from the 1 : 2000 scale cartography (see
Figure2).Figure 2
Digital elevation model derived from the 1 : 2000 scale vector cartography
of Cesano Maderno. The elevation range varies from 194 to 228 meters above sea
level, increasing from south to north.Again from the 1 : 2000 scale cartography, 110 ground points
characterized by an accuracy better than 0.80 m in plan and with a regular
geometric distribution over the whole study area were selected and measured.
Subsequently, these points were divided into two datasets: (1) 80 ground control
points (GCPs) used for the image orthorectification and (2) 30 points used as independent
check points (ICPs) for evaluating the orthorectification accuracy (see
Figure3).Figure 3
Geometric distribution of ground control points (red crosses) and independent
check points (yellow crosses).


## 3.2.3. Image Orthorectification

Because no metadata or Rational
Polynomial Coefficients (RPCs) were available, the QuickBird image was
geometrically corrected using two different nonparametric techniques: (1) the
Rational Function Model (RFM) with RPCs computed from GCPs and (2) the spline
interpolation computed from GCPs.Several orthoimages were generated both using the RFM and
the spline methods and using an increasing number of GCPs: from 40 to 80. Their
accuracy was checked by manually measuring on the corrected images the
cartographic coordinates of the 30 ICPs previously identified.
Table1 shows a
summary of results.Table 1
Accuracy of the orthoimages generated using the rational function model
and the spline interpolation methods, calculated on the basis of 30 independent
check points.
MethodGCPsRMSE (m)(nr.)EastNorthOverall400.622.142.22500.581.411.52RFM600.551.111.24700.511.121.23800.501.101.21400.601.551.66500.551.451.55Spline600.611.491.61700.601.501.61800.571.511.62In our tests, the RFM method led to superior results than
the spline method. With respect to splines, the accuracy of the orthoimages did
not vary significantly from the use of 40 GCPs to 80 GCPs. In the former case, it was obtained a 0.60 m root mean square error for the East coordinate (RMSEE), 1.55 m root mean square error for the North coordinate (RMSEN), and 1.66 m overall root mean square error (RMSE), while in the latter case it was observed a 0.57 m RMSEE, 1.51 m RMSEN, and 1.62 m overall RMSE.Concerning the RFM method, the best result was obtained
using a full 3rd-order RFM using 80 GCPs (0.50 m RMSEE,
1.10 m RMSEN,
and 1.21 m overall RMSE). With increasing the number of GCPs from 40 to 60, the
accuracy of the geometric correction passed from 0.62 m RMSEE, 2.14 m
RMSEN, and 2.22 m overall RMSE to 0.55 m for RMSEE,
1.11 m RMSEN, and 1.24 m overall RMSE, respectively. No further
significant improvement was instead observed using more GCPs.


## 3.2.4. Vector Extraction

Vector cartography for three
quarters of Cesano Maderno was manually extracted from the pan-sharpened
orthorectified QuickBird image and then used for simulating a topographic DB
updating of both the 1 : 5000 scale VTM and the 1 : 10 000 scale RRTM. The simulation was carried out by
wiping out some quarters from the existing cartography and replacing them with
the vector layers extracted from the QuickBird image.From the 12 classes defined for buildings in the mapping
technical specifications and from the 11 classes defined for roads, the
simulation took into consideration only the following 8 classes (one for roads
and six for buildings).Class 1:
roads.Class 2:
civilian
buildings.Class 3:
local
government buildings;Class 4:
commercial
buildings.Class 5:
industrial
buildings.Class 6:
churches
and places of cult.Class 7:
shack/roof.Furthermore, buildings were digitized from tops because
footprints were rarely visible due to both the dense urbanization texture and
the presence of long shades caused by the tilted viewing geometry.Finally, results were
assessed by comparing the QuickBird derived vectors with the higher precision
1 : 2000 scale cartography.


## 4. Results and Discussion

Because the planimetric position of objects in the images
is influenced by the relief displacement due to differences in the relative
elevation of objects, and the taller the object the greater the relief
displacement, buildings usually show an incorrect positioning in relation to
their height. Besides, when using off-nadir collected images such as high-resolution
satellite data, the satellite’s tilt angle introduces a further source of
error. For that reason, the mapping accuracy was tested separately analyzing
three different types of urban texture, characterized by a different elevation
range of buildings: (1) a mixture of industrial/commercial area and residential
area with buildings with very different heights (between 2 and 7/8 floors),
called category A; (2) a dense urban area typical of Italian cities (about 3/4
floors), called category B, and (3) an urban area of detached houses with
small, regular, and similar buildings (single storey or 2 floors), called
category C. For each category, the tolerances required for the 1 : 10 000 scale and
1 : 5000 scale topographic DB of the Lombardia Region were
tested, as defined in (1)-(2).


### 4.1. Positioning Accuracy

Regarding the positioning accuracy of single points, the
coordinates of 1,158 points were checked: 240 for category A, 319 for category
B, and 599 for category C. The different numbers of point measures for the
different categories were representative of the number of buildings digitized
for each category (see Figure4).Figure 4
Example comparison between the buildings digitized from the QuickBird
image (red lines) and homologous buildings from the 1 : 2000 vector cartography
(blue lines).For the 1 : 5000 map scale update, 13% of measurements were
outside the tolerance limit for category A, 8% were outside the tolerance limit
for category B, and only 2% were outside the tolerance limit for category C.
Thus, the test was passed only for urban area of detached houses (see
Table2).Table 2
Results of the cartographic tests for the positioning of single points.
Urban typologyCategory ACategory BCategory CMap scale1 : 50001 : 10 0001 : 50001 : 10 0001 : 50001 : 10 000Testing measures (nr.)240240319319599599Within tolerance limit87%100%92%100%98%100%Outside tolerance limit13%0%8%0%2%0%Test passNoYesNoYesYesYesFor the 1 : 10 000 map scale update, all the measures were within the tolerance
limit, regardless of the urban typology. Thus, the test was passed for all
urban areas (see Table2).


### 4.2. Distances Accuracy

Regarding the distances between two points, they were
calculated from pairs of points used in the previous tests. However, after having verified that points very close to each other (typically points belonging to the same building) had very small distance errors, a limited number of measures belonging to both the shorter range (< 800 m) and the longer range (800/1500 m) were selected, resulting in a fewer number of measures. Thus, 187 distance measures were checked: 41 for
category A, 82 for category B, and 64 for category C (see
Figure4).For the 1 : 5000 map scale update, 7% of measurements were
outside the tolerance limit for category A, 1% were outside the tolerance limit
for category B, and all were within the tolerance limit for category C.
Therefore, the test was passed both for the dense urban area and for the urban
area of detached houses (see Table3).Table 3
Results of the cartographic tests for the mutual distances between two
points.
Urban typologyCategory ACategory BCategory CMap scale1 : 50001 : 10 0001 : 50001 : 10 0001 :  50001 : 10 000Testing measures (nr.)414182826464Within tolerance limit93%100%99%100%100%100%Outside tolerance limit7%0%1%0%0%0%Test passNoYesYesYesYesYesFor the 1 : 10 000 map scale update all the measures were within the tolerance
limit, regardless of the urban typology. Thus, the test was passed for all
urban areas (see Table3).


### 4.3. Final Editing

A final editing is always needed to give the cartographic
dressing and to correct any little inaccuracies.
Figure5 shows some little
overlaps between the building and the road classes which were removed in
editing.Figure 5
Example of overlaps between the buildings (red) and the road (blue)
classes before the final editing.


### 4.4. Overall Results

Overall results show the following.(1)
For the mixture of industrial/commercial area and residential area
(category A), it was not possible to update the 1 : 5000 scale VTM because both
the positioning accuracy and the distances accuracy were outside the admitted
limits, while it was possible to update the 1 : 10 000 scale RRTM.(2)
For the dense urban area (category B), it was not possible to
update the 1 : 5000 scale VTM because the positioning accuracy was outside the
admitted limit, while it was possible to update the 1 : 10 000 scale RRTM.(3)
For the urban area of detached houses (category C), it was
always possible to update both the 1 : 5000 scale VTM and the 1 : 10 000 scale RRTM.It is to be said, however, that both the accuracy of the image orthorectification and the
tolerances for the positioning and distances were checked against ICPs derived
from 1 : 2000 scale cartography, so with an accuracy better than 0.80 m in plan.
If compared to the 1 : 10 000
scale RRTM requirements, the ICPs may be assumed as high precision control
points, but if compared to the higher requirements for the 1 : 5000 scale VTM
they may be not as much accurate as needed. In fact, when looking at the
positioning tests (see Table2), for category
B only 3% of measures are outside
the admitted limits and for category A only 8% of measures are outside the
admitted limits. Furthermore, looking at the distances tests, category B is
within tolerances and category A has only 2% of measures outside the admitted
limits.Regarding the presence of systematic errors in the
digitized vectors, no
error was observed. The mean value of the absolute residuals calculated
for the 1,158 point measures showed that the worse case was obtained for
category A, followed by category B, and finally called category C. Moreover,
even for the worse case the mean value of the absolute residuals (1.26 m) was
of the same magnitude of the orthorectification accuracy (1.21 m), thus
confirming the absence of systematic gross errors in data. Instead, the increase
in the mapping accuracy from category A to category C may be explained by
analyzing the errors introduced by the tilted viewing geometry of the imagery
used. With a 15° tilt angle, buildings have a planar displacement of about 2.60 m every
10 m of high above the compensation plan used, and these errors are
added (in sign) to the residuals of the orthorectification process (1.21 m
overall RMSE).Figures6 and 7
show some simulations of the map updating,
respectively, for the 1 : 5000 scale VTM and for the 1:10000  RRTM.Simulation of 1 : 5000 scale Vector Technical Map updating in a dense
urban area (category B): (a) 0.70 m QuickBird pan-sharpened orthorectified
image, (b) digitization of buildings and roads, (c) 1 : 5000 scale Vector
Technical Map of Cesano Maderno with some neighborhoods wiped out, (d) final
map updating.
(a)(b)(c)(d)Simulation of 1 : 10 000
scale Raster Regional Technical Map updating in a quarter of detached houses
(category C): (a) 10 000
scale Raster Regional Technical Map of Cesano Maderno with some neighborhoods
wiped out, (b) final map updating.
(a)(b)


## 4.1. Positioning Accuracy

Regarding the positioning accuracy of single points, the
coordinates of 1,158 points were checked: 240 for category A, 319 for category
B, and 599 for category C. The different numbers of point measures for the
different categories were representative of the number of buildings digitized
for each category (see Figure4).Figure 4
Example comparison between the buildings digitized from the QuickBird
image (red lines) and homologous buildings from the 1 : 2000 vector cartography
(blue lines).For the 1 : 5000 map scale update, 13% of measurements were
outside the tolerance limit for category A, 8% were outside the tolerance limit
for category B, and only 2% were outside the tolerance limit for category C.
Thus, the test was passed only for urban area of detached houses (see
Table2).Table 2
Results of the cartographic tests for the positioning of single points.
Urban typologyCategory ACategory BCategory CMap scale1 : 50001 : 10 0001 : 50001 : 10 0001 : 50001 : 10 000Testing measures (nr.)240240319319599599Within tolerance limit87%100%92%100%98%100%Outside tolerance limit13%0%8%0%2%0%Test passNoYesNoYesYesYesFor the 1 : 10 000 map scale update, all the measures were within the tolerance
limit, regardless of the urban typology. Thus, the test was passed for all
urban areas (see Table2).


## 4.2. Distances Accuracy

Regarding the distances between two points, they were
calculated from pairs of points used in the previous tests. However, after having verified that points very close to each other (typically points belonging to the same building) had very small distance errors, a limited number of measures belonging to both the shorter range (< 800 m) and the longer range (800/1500 m) were selected, resulting in a fewer number of measures. Thus, 187 distance measures were checked: 41 for
category A, 82 for category B, and 64 for category C (see
Figure4).For the 1 : 5000 map scale update, 7% of measurements were
outside the tolerance limit for category A, 1% were outside the tolerance limit
for category B, and all were within the tolerance limit for category C.
Therefore, the test was passed both for the dense urban area and for the urban
area of detached houses (see Table3).Table 3
Results of the cartographic tests for the mutual distances between two
points.
Urban typologyCategory ACategory BCategory CMap scale1 : 50001 : 10 0001 : 50001 : 10 0001 :  50001 : 10 000Testing measures (nr.)414182826464Within tolerance limit93%100%99%100%100%100%Outside tolerance limit7%0%1%0%0%0%Test passNoYesYesYesYesYesFor the 1 : 10 000 map scale update all the measures were within the tolerance
limit, regardless of the urban typology. Thus, the test was passed for all
urban areas (see Table3).


## 4.3. Final Editing

A final editing is always needed to give the cartographic
dressing and to correct any little inaccuracies.
Figure5 shows some little
overlaps between the building and the road classes which were removed in
editing.Figure 5
Example of overlaps between the buildings (red) and the road (blue)
classes before the final editing.


## 4.4. Overall Results

Overall results show the following.(1)
For the mixture of industrial/commercial area and residential area
(category A), it was not possible to update the 1 : 5000 scale VTM because both
the positioning accuracy and the distances accuracy were outside the admitted
limits, while it was possible to update the 1 : 10 000 scale RRTM.(2)
For the dense urban area (category B), it was not possible to
update the 1 : 5000 scale VTM because the positioning accuracy was outside the
admitted limit, while it was possible to update the 1 : 10 000 scale RRTM.(3)
For the urban area of detached houses (category C), it was
always possible to update both the 1 : 5000 scale VTM and the 1 : 10 000 scale RRTM.It is to be said, however, that both the accuracy of the image orthorectification and the
tolerances for the positioning and distances were checked against ICPs derived
from 1 : 2000 scale cartography, so with an accuracy better than 0.80 m in plan.
If compared to the 1 : 10 000
scale RRTM requirements, the ICPs may be assumed as high precision control
points, but if compared to the higher requirements for the 1 : 5000 scale VTM
they may be not as much accurate as needed. In fact, when looking at the
positioning tests (see Table2), for category
B only 3% of measures are outside
the admitted limits and for category A only 8% of measures are outside the
admitted limits. Furthermore, looking at the distances tests, category B is
within tolerances and category A has only 2% of measures outside the admitted
limits.Regarding the presence of systematic errors in the
digitized vectors, no
error was observed. The mean value of the absolute residuals calculated
for the 1,158 point measures showed that the worse case was obtained for
category A, followed by category B, and finally called category C. Moreover,
even for the worse case the mean value of the absolute residuals (1.26 m) was
of the same magnitude of the orthorectification accuracy (1.21 m), thus
confirming the absence of systematic gross errors in data. Instead, the increase
in the mapping accuracy from category A to category C may be explained by
analyzing the errors introduced by the tilted viewing geometry of the imagery
used. With a 15° tilt angle, buildings have a planar displacement of about 2.60 m every
10 m of high above the compensation plan used, and these errors are
added (in sign) to the residuals of the orthorectification process (1.21 m
overall RMSE).Figures6 and 7
show some simulations of the map updating,
respectively, for the 1 : 5000 scale VTM and for the 1:10000  RRTM.Simulation of 1 : 5000 scale Vector Technical Map updating in a dense
urban area (category B): (a) 0.70 m QuickBird pan-sharpened orthorectified
image, (b) digitization of buildings and roads, (c) 1 : 5000 scale Vector
Technical Map of Cesano Maderno with some neighborhoods wiped out, (d) final
map updating.
(a)(b)(c)(d)Simulation of 1 : 10 000
scale Raster Regional Technical Map updating in a quarter of detached houses
(category C): (a) 10 000
scale Raster Regional Technical Map of Cesano Maderno with some neighborhoods
wiped out, (b) final map updating.
(a)(b)


## 5. Conclusions

This study demonstrates the potentiality of high-resolution
satellite images for updating existing technical cartography in developed and
well-mapped countries. The map updating was discussed with reference to the
legislation and the technical specifications for the traditional 1 : 10 000 scale and1:5000 scale
topographic DB of the Lombardia Region, which is a reference for metric mapping
purposes in Italy. Results show that the updating of the 1 : 10 000 RRTM was always
possible, while the rigorous updating of the 1  : 5000 scale VTM was possible only
where buildings showed limited differences in the relative elevation.Findings are in accordance with similar studies undertaken
by several European mapping agencies and institutions about the use of IKONOS
data for mapping purposes, suggesting the use of such kind of imagery for
cartographic updating until 1 : 10 000 scale map. Also Ordnance Survey, Great Britain’s National Mapping
Agency, found that QuickBird imagery can be used to update maps between 1 : 10 000 scale and  1: 6000 scale,
as long as small linear features were excluded from the mapping specifications.



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*Source: 725429-2008-03-18.xml*