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Evaluating SPOT-5 Satellite Imagery for National Mapping Division’s Topographic Mapping Program

Alan Forghani Manager of Remote Sensing Products*

Shanti Reddy Director of Operations Unit**

Craig Smith Director of Applications Unit**

Product Management Group*

Australian Centre for Remote Sensing** National Mapping Division, Geoscience Australia

GPO Box 378, Canberra, ACT 2601 Australia Phone: +61-2-6249 9653, Fax: +61-2-6249 9937

E-mail: alan.forghani@ga.gov.au

ABSTRACT The availability of high-resolution satellite imagery, such as SPOT-5, has opened new possibilities for topographic mapping. This paper evaluates the use of SPOT-5 imagery for topographic map revision at 1:100K and 1:250K scales over Katherine, NT, and Derby, WA. There were three reasons for selection of these sites: the recent revision of GEODATA TOPO 250K, the Defence map revision program and scale flexibility (50K- 250K). The investigation focused on the suitability of SPOT-5 imagery for the identification of features such as tracks, roads, fences, landing grounds, homesteads and drainage, that are not readily identified in SPOT 2/4 Pan and ETM+ Multi-spectral image data for revision of Geoscience Australia's GEODATA TOPO 250K and 100K mapping. This work follows on from the previous imagery evaluation project on KOMPSAT-1, EROS-A1 data over Naracoorte (SA), Berrigan (NSW) and Laverton (VIC). The visual interpretation made over these study sites supplements the results of the previous project that evaluated simulated SPOT-5 imagery over France. This paper outlines the methodology used in evaluating SPOT-5, SPOT-2 and ETM+ imagery. The objective was to ascertain what features could be identified and mapped using visual interpretation techniques currently used by National Mapping Division (NMD), and map and spatial data producers. Examination of SPOT-5 imagery (Pan & XS) shows that the higher resolution imagery improved the ability to identify significant additional features when compared with the imagery used to produce GEODATA TOPO 250K. The findings and conclusions that have emerged from this investigation will contribute to more productive debate between policy makers in central government and executives of National Mapping Agencies (NMA) to consider the usefulness of SPOT-5 for future topographic map revision programs. 1.0 Relevant Studies The main objective of our research was to assess the topographic information content of SPOT-5, SPOT 2/4 and Landsat ETM+ satellite imagery with particular emphasis on 1:250K and smaller scales. A description of some of the future satellites relevant to topographic mapping is presented in Table 1. The topographic information content of remotely sensed imagery is a result of many factors. These factors include: • The spatial spectral and radiometric resolution of the imagery • Terrain relief • Seasonal conditions and sun/sensor geometry at the time of image acquisition.

It is generally accepted that the spatial resolution of imagery is usually the most important of these factors. Studies demonstrated that a ground pixel size of 10m meets most requirements for updating of 1:00K to 1:250K (eg Konecncy, 1990; Konecncy, 1999; Petzold, 1999). Mapping at certain scales demands appropriate geometrical accuracies for determination of positions and heights (Konecncy and Schiewe,

SpatialSciences2003

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1996). At the pixel size of 5–10m, experiments with feature delectability of current satellites meet the topographic map contents at 1:100K–1:250K (eg Konecncy and Schiewe, 1996; Schiewe, 2001).

Satellite

Operators Brief Description Year More Information

ALOS-1 NASDA 2.5m PAN, 10m MS, & 10-100m SAR 10

2004 www.nasda.go.jp

EROS-B1 ImageSat International

0.5m PAN & 2m MS 2003 www.imagesatintl.com

Cartosat-1

ISRO

2.5m PAN & 0.5m PAN 2003-4 www.isro.org

KOMPSAT-2 KARI 1m PAN & 4m MS 2004 www.kari.re.kr

IKONOS-3 Space Imaging 0.50m PAN & 1m MS 2004-5 www.spaceimaging.com

Quickbird-3 Digital Globe 0.50m PAN & 1m MS 2004-5 www.digitalglobe.com

RADARSAT-2 SOAR 3m PAN SAR 2005 www.space.gc.ca

Orbview-3 Orbital Imaging 1m PAN & 4m MS 2003 www.orbimage.com Table 1. Launch Schedule for new high resolution remote sensing satellites suitable for topographic mapping. The Canadian Land Survey utilised Landsat TM imagery only to detect major changes in the country at 1:250K and used aerial photography for larger scale map revision certain areas. Libya compiled a National Landsat Photomap series at 1:250K scale. These countries used the Landsat data for their basemaps (cited by Forghani, 2001). In Norway SPOT imagery is used to update 1:50K maps in a semiautomatic manner, (Solberg, 1992). The SPOT data was used in UK to extract 80% of the information required for a 1:50K scale mapping (Dowman and Peacegood, 1988). In addition, SPOT images have been used in Kenya to map out roads and landcover at 1:25K scale with an accuracy of 87%-92% feature detection. Experience has shown that SPOT Pan alone is not sufficient to detect all changes, but if SPOT XS is also used, most of changes can be detected efficiently (cited by Forghani, 2001). Studies in Canada and France shows that Landsat (MSS and TM) and SPOT imagery reduced the cost of production about 50% for base maps. Certainly in a vast country such as Canada and Australia the aerial imagery is very expensive. In contrast, Iran is currently employing aerial photography for production of 1:500-1:40K topographic and cadastral map at Iranian National Cartography Centre and Cadastral Survey of Iran respectively (Forghani, 2001). In an investigation by Walter (2000) merged KVR-1000 high resolution space photographs and SPOT XS data was successfully used for generating satellite image maps and satellite topoimage maps as well as for map updating up to the scale of 1:25K in Poland. It is important to explore what other mapping agencies do in order to gain an appreciation of the use of remote sensing technology in map revision activities. A review of the literature shows that mapping agencies in Europe, USA and Canada produce topographic maps at scales between 1:100K to 1:250K using both aerial and satellite imagery (Forghani, 2002). For example, United States Geological Survey (USGS) produces topographic (base) maps in scales ranging from 1:10K to 1:100K solely using aerial photography (Moore, 2000). In contrast, current SPOT 1-4 and Landsat TM & ETM+ imagery has been the primary source of image data for the revision of the National Mapping Division (NMD) medium scale topographic maps for the last 8 – 10 years (Forghani, 2002).

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2.0 Methodology This project continues on from the previous imagery evaluation project based on KOMPSAT-1, EROS-A1 data over Naracoorte, Berrigan and Laverton, and included the evaluation of simulated SPOT-5 imagery over France (Forghani, 2002). This project adopted a similar methodology to the above project (Figure 1):

i. The methodology used ETM+ and SPOT-2 imagery as a geometric base to be compared with SPOT-5 over the study sites.

ii. The new imagery from the SPOT-5 sensor was geocoded to SPOT-2 imagery. iii. The SPOT-5 Pan data was merged with ETM+ Multi-spectral (MS) imagery, to assist with the

identification of vegetation and water features. iv. Visual interpretation supported by supplementary data was undertaken. Supplementary data,

including aerial photographs at 1:50K scale, GEODATA TOPO 250K, town planning maps and land tenure/ownership maps were also acquired. Imagery information content was subjectively assessed to identify problematic features from SPOT-5, SPOT-2 and ETM+ data. Emphasis was placed on documenting all the topographic features that could be discriminated and/or identified in each of the data sets. In order to achieve the best results, feature identification and mapping were performed interactively using SPOT-5 imagery as a backdrop. For example, polygons and lines were created for buildings and roads. The features identified were cross-referenced against the relevant map specifications. Also, comment was made on the seasonal conditions at the time of image acquisition and the effect of those conditions on feature interpretability.

v. A semi-automated road detection algorithm was tested on 2.5m Pan data. vi. Cost benefits and potential problems were highlighted, and a set of recommendations for the

suitability of SPOT-5 for topographic map revision was provided.

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Figure 1. Flowchart of the SPOT-5 imagery evaluation

Formulation of Methodology

User Needs IdentificationConsultation

Image Enhancement

Data Collection

Image Geocoding

Resolution Merge

Visual Interpretation

Research Findings

Project Initiation

On-Screen Digitising

Feature Extraction

Qualitative Assessment Quantitative Assessment

Semi-automated road detection, tabulation of captured additional features

from SPOT-5

Cost Analysis

Data Analysis

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2.1 Study Areas Katherine NT, and Derby WA were chosen as the study sites for this project. The main reasons for the choice of these sites were: 1. The availability of recently revised GEODATA TOPO 250K data 2. The range of scales of topographic mapping for these areas, 1:50K – 1:250K, 3. The intention to revise the 1:50K maps of these areas in the near future.

The study areas contain mixed rural and urban features (Figure 2). Scrubland vegetation is the predominant landcover type with features such as roads, tracks, residential buildings, industrial and commercial buildings, fences, airbases and drainage patterns present in the study areas. Katherine Site This site has three major geographic zones: 1. In the upper-right portion of the image, urban land use dominates the image. 2. Most of the remaining image is dominated by open dry Eucalyptus forest, savanna woodland, and

grassland with scattered trees. 3. The Katherine River extends from the upper-right corner to the lower-left corner, crossing the town of

Katherine. Main roads also follow the same direction, although there are some roads that cross the river from upper-left and extend to Katherine. In the Katherine town plan map of 1981, indicates that there are both rural and urban land use classes, including residential, industrial and commercial, recreational and open rural land use, distributed throughout the study area.

Derby Site In contrast to Katherine, this site has a predominantly rural base, and consists of flat open plains that exhibit seasonally arid conditions and are occupied by vast expanses of pastoral lands. It also includes the Curtin Air Base, the most prominent cultural feature, which was closely investigated and mapped in detail.

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Figure 2. Shows the location of the project areas within WA and NT and the SPOT-5 images over the two sites. The road network is superimposed on SPOT-5 2.5m Pan imagery. The built-up areas are shown as red polygons. The extent of the urban area over Katherine is located within the purple box, with the green box highlighting a typical rural area. The Curtin Air Base near Derby is located within the yellow box.

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2.2 Data sets and Pre-processing The following describes the of processes for the acquiring and processing of raster images: 2.2.1 Imagery used in this project An attempt was made to acquire ETM+ and SPOT-2 data as near as possible to the acquisition date of SPOT-5 to minimise temporal differences that could otherwise affect interpretation. These datasets are detailed in Table 2. Images acquired at the start of the spring period have minimal shadow effects and a maximum contrast between vegetation cover types. The ETM+ and SPOT-2 images were selected by searching the Australian Centre for Remote Sensing (ACRES) archives for cloud-free images acquired around the date of the SPOT-5 acquisition.

Imagery Pixel Size (m)Mode Spectral Range (µm) Date of Acquisition

ETM+ 15 30

Pan MS

0.52 – 0.90 http://www.auslig.gov.au/acreprod_ser/landdata.htm

6 September 2002 (NT) 9 September 2002 (WA)

SPOT-2 10 Pan 0.51 – 0.73

16 October 2002 (NT) 13 July 2002 (WA)

SPOT-5 5 and 2.5 Pan 0.51 – 0.73

28 September 2002 (NT) 16 September 2002 (WA)

Table 2. Data set specification.

2.2.2 Image geo-referencing Each of the SPOT-5 images was geo-referenced to the MGA projection of SPOT-2 imagery. About 10 well-distributed ground control points (GCPs) were selected from the images. The Root Mean Square (RMS) residual for GCPs over Katherine and Derby was 5.5 metres. An additional qualitative check was made on the accuracy of the geo-coded images by importing them into the GIS and overlaying the road vector centre lines on top of the images. Visual checks of the images revealed that the geo-coding results were acceptable. All images were contrast stretched before image interpretation. 2.2.3 Image fusion There are several techniques including the Multiplicative, Principal Components, and Brovey Transform methods for merging high spatial resolution data with lower resolution multispectral data (ERDAS, 1997). In this study, the Principal Components method was used to integrate the ETM+ (30m) Multi-spectral and SPOT-5 (5m) Pan imagery to produce high resolution, multi-spectral imagery for this project. This improved the interpretability of the data, as high-spatial resolution multispectral imagery was available to assess for topographic features. A Bilinear Interpolation re-sampling technique was used in the image merging.

2.3 Visual Interpretation The features most difficult to identify in ETM+ and SPOT-2 imagery were identified through staff involved in the map revision process and map users. All these features were investigated using supplementary information such as aerial photography at 1:50K scale, GEODATA TOPO 250K digital data, town planning maps and land tenure/ownership maps. There was no ground truth information available to confirm some of the features identified from the imagery. An assessment was carried out over small sample areas, where testing involved the comparison of SPOT-5 with SPOT Pan and ETM+ imagery, for the purpose of visually identifying features. The test images consisted of two sites measuring 30 km x 30 km (Katherine) and 50 km x 50 km (Derby). The digital GEODATA TOPO 250K Series 2 data was overlaid with satellite imagery. This enabled the operator to capture those features (eg tracks and fences) from SPOT-5, which were not included in the GEODATA TOPO 250K data. On-screen digitising allowed the operator to verify feature interpretation against published maps and the current GEODATA TOPO 250K. This technique was used to trace features from SPOT-5 2.5 imagery as a reference background in the editing process, and to accurately add and create new features overlaid on the GEODATA TOPO 250K using on-screen digitising. In GIS editing process, tracks, access roads, drainage, fence lines are drawn as a background in separate colours.

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3.0 Feature Identification Analysis Not all features identified for inclusion in the GEODATA TOPO 250K can be captured from satellite imagery. Features such as bores are not identifiable on current imagery sources used in NMD. The location of these features may sometimes be inferred from the location of associated tracks, supported by other source material supplied; otherwise they are captured from the previous mapping and remain unrevised. In practice, a feature should not be captured solely from imagery unless there is some additional supporting information, which confirms the existence of the feature, and provides its attributes. Satellite imagery is generally used to position new features, and other information is used to attribute the features. Table 3 details the cumulative results of the topographic themes identified over both sites. Examples are provided in Figures 3-6, which depict additional features captured from SPOT-5 imagery. The following issues were considered in the process of identification and mapping of the above features (see Table 3): 1. The date of GEODATA TOPO 250K: both Katherine and Derby work units have recently been

revised. Therefore, the age of the GEODATA did not impact on the additional features that were mapped in this study.

2. Generalisation: due to generalisation constraints, not all features may be captured for 250K mapping everywhere. Consultation with staff involved with map revision identified that the GIS operators try to capture as much information as possible in remote areas like Katherine and Derby. By contrast, in urban areas the generalisation principle limits capture to more important features such as roads, but not all the streets, in populated areas.

3. Lack of supportive evidence: at times GIS operators may not capture some features because of the lack of supportive evidence.

4. Detectability: the resolution of the imagery may prevent identification of all the features that the GIS operators wish to capture. This is particularly relevant in the identification of those problematic features recorded in customer feedback.

With reference to the information in Table 3, GEODATA TOPO 250K Series 2 data was overlaid onto SPOT-5 satellite imagery. It was observed that temporal change was not significant as the maps over both the study areas have recently been revised. Therefore, it allowed the capture of those features from SPOT-5 that were not included in the GEODATA TOPO 250K.

Additional features derived from SPOT-5 imagery

Feature Name

Katherine Derby

Minor roads and streets 42 km 6 km Track 48 km 17 km Fence 94 km 66 km Drainage 25 km 38 km Bridge 3 1 Locality 7 2 Air Base Not applicable 1

Table 3. Feature identification results from the evaluation of SPOT-5 2.5m imagery over Derby and Katherine.

3.1 Feature Identification Examples Roads Approximately 48 kilometres of additional minor roads and streets were digitised from SPOT-5 2.5m imagery (eg Figures 3 and 4). There are some distinct characteristics that can be applied in the identification of roads/tracks/fences. The following criteria were applied in the identification and mapping of roads: 1. Minor roads are often maintained by Local Government Authorities (LGAs) and usually connect to a

feature such as building, park, etc. It is often difficult to identify specific features at the ends on minor tracks.

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2. The degree of formation of roads can aid the GIS operator or image interpreter to separate minor roads from tracks. At the same visibility, resolution and scale, the operator can easily recognise roads versus tracks and fences.

Figure 3. This image map shows features extracted from SPOT-5 2.5m imagery. Extracted road and street networks are shown (red lines), as well as existing road networks (yellow lines) from GEODATA. The banks of the Katherine River are easily identified and shown in pale blue across the centre of the map. This indicates the potential applicability of this imagery for the production of 1:100K and larger scale maps. In addition, individual buildings are identifiable in this imagery, as can be seen on the right and lower sides of the image.

Figure 4. Shows a major unsealed road and the visual clarity of a building alongside the road, as well as a digitised track on SPOT-5 2.5m imagery (Derby area). The right image is an oblique aerial photo of a minor unsealed road and drainage taken in a field audit (Mathews et al., 2002), and the left image shows drainage on major unsealed road on SPOT-5 2.5m. It is usually difficult to distinguish between tracks and fence lines on ETM+ and SPOT-2 images as they often closely follow each other. Mathews et al (2002) in a recent Field Audit report noted that a minor unsealed road might provide access to a number of homesteads from a principal sealed road. The importance of this road in terms of functioning in a variety of weather conditions, such as the wet season, is evident due

Digitised track

Digitised fence

Digitised river banks Digitised Roads

Drainage on major unsealed road

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to the numerous drainage constructions built along the length of the road. These constructions are also visible on imagery, and allow NMD researchers to gauge the function and importance of a road, if they are required to classify it without additional source material. The topographic map specifications do not allow for roads to be classified from imagery alone. In the higher spatial resolution SPOT-5 Pan data, the narrower features such as tracks are well defined and accurately identified. About 95% of tracks were identified and mapped. About 65 kilometres of additional tracks were identified and mapped from the SPOT-5 2.5m imagery. The distinction between tracks and fences in other imagery is sometimes problematic, whereas SPOT-5 provides a clear distinction between narrow linear features such as tracks, fence lines and firebreaks (Figures 3-6). Fences Landowners and LGAs are keen for the inclusion of external property fences in NMD maps, as reported by Mathews et al (2002). Fences are extremely useful for navigation purposes, since there are often few cultural features in very remote areas. Therefore, it is crucial to maintain the revision of fences and tracks even at the scale at which NMD is currently producing maps. In practice, it is difficult to identify fences from SPOT-5 imagery reliably, but often fence lines can be inferred from differential grazing or remnant vegetation patterns associated with areas adjacent to fencelines. The presence of relatively straight tracks associated with the ground conditions specified above may give additional evidence to the presence of fences.

Figure 5. Digitised fences, tracks and drainage networks from SPOT-5 2.5m imagery in the Tindal area (NT). Drainage and Vegetation Water bodies were equally well identified in SPOT-2 and SPOT-5 5m data SPOT-5 data was merged with ETM+ Multi-spectral data, provided superior identification and delineation of vegetation alignments, drainage and water bodies (eg Figure 5). Significant additional drainage networks could be extracted from SPOT-5 imagery. Around 90 kilometres of additional drainage were identified and mapped from the SPOT-5 2.5m imagery compared with the revised GEODATA TOPO 250K that was derived from ETM+ and SPOT-2 imagery. The importance of extracting higher stream orders is significant when considering high-resolution imagery for the production of larger scale mapping. Drainage and water point features in remote outback areas were considered during the investigation. Seasonal conditions at the time of image acquisition and strongly influences the discrimination and identification of topographic features. As the study areas were experiencing a drought at the time of image acquisition, few water-containing streams and other water

Digitised drainage

Digitised track Digitised fence

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features could be identified. However, the morphology of the drainage was a guide to digitising additional drainage features from SPOT-5 2.5m data. The delineation of vegetation and farm boundaries is much clearer in SPOT-5 imagery. Orchards especially are easily identifiable on SPOT-5 images, where rows of trees are clearly defined. Previous studies (eg Forghani, 2002) highlighted the need to merge higher resolution imagery such as SPOT-5 2.5m with ETM+ 30m Multi-spectral imagery to extract features such as orchards, other vegetation boundaries, watercourses, swamps, lakes and drainage networks. Sparse vegetation and individual trees are more easily distinguishable on SPOT-5 Pan than on SPOT-2 and ETM+ Multi-spectral imagery. Localities Experience shows that many homesteads shown on the NMD 250K maps have been unrevised for between 20-30 years (Mathews et al 2002). Landowners consulted would like to see the correct naming of their homesteads. SPOT-5 2.5m data, along with local knowledge, is a good way of verifying homestead names and their true representation on the map. Observations shows that individual farm buildings are better defined and more visible on SPOT-5 Pan than on SPOT-2 and ETM+ imagery. Man-made structures (buildings and roads) are sharper on SPOT-5 than on ETM+ and SPOT-2. The correct identification of such features is particularly important in remote rural areas. Airport Infrastructure Airports usually are easily identified on SPOT-2 and ETM+ imagery. Figure 6 is of Curtin Air Base, depicting the fine features that are easily identified on the SPOT-5 2.5m imagery. Approximately 90 per cent of Curtin Air Base infrastructure was identified and mapped from the SPOT-5 2.5m imagery. A map of Curtin Air Base at 1:10 000 scale was obtained from the Department of Defence to compare with the features identified from SPOT-5 imagery. There was good agreement between the features observed on the SPOT 5 imagery and the reference map of the Curtin Air Base. However, the attributes of the buildings on the Air Base may not be accurate since there was no independent information to verify the features.

Figure 6. Part of the Curtin Air Base area near Derby is shown on this SPOT-5 2.5m image. The image map shows the high degree of detail that is available from SPOT-5 2.5m imagery data. On this image, smooth surface features, such as roads, taxiways and airport runways appear bright. Rougher surface features, such

Runway

Taxiway

Firebreak

Buildings Buildings

Buildings

Track

Roads

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as buildings, also appear bright, and trees appear dark. The majority of airport infrastructure was easily digitised and extracted from the imagery. Within the data set over the Katherine test site, there were no landing grounds or heliports. However, these types of features can usually be identified in SPOT-5 imagery since, in the above example, airstrips and taxiways were accurately identified and mapped. 4.0 Cost Comparison The relative cost of different sources of imagery is presented in Table 4. Although SPOT-5 imagery is relatively more expensive than most other datasets identified in this project, it provides greater feature identification capabilities that lead to improved quality topographic maps.

Imagery (Ortho-rectified

Image)

Costs ($) (Retail Price)

$ / km² Scene Size and Comments

ETM+ 1,800 0.05 Full Scene (185 km by 185 km) http://www.auslig.gov.au/acres/prod_ser/etmprice.htm;1 2 August 2002

SPOT-2/4 2,270 0.63 Full Scene (60 km by 60 km) http://www.auslig.gov.au/acres/prod_ser/spotprice.htm; 12 August 2002

SPOT-5 10m XS 5m Pan

2.5m Suppermo

5,700 5.700 11,400

1.58 1.58 2.78

Full Scene (60 km by 60 km) www.spotimage.com.au/home/spot5/pricelist/pricelist/htm; 1August 2002

Table 4. Cost of different sources of imagery. Cost comparison demonstrates that it is quite feasible and practical for NMA to collect and use SPOT-5 high-resolution satellite imagery for large-scale topographic mapping applications.

5.0 Will High Resolution Images Solve All Map Revision Problems? There may be an impression that the high-resolution imagery available can solve all feature identification problems. Research within the Mapping Program of the NMD indicates that using higher resolution imagery may not dramatically reduce problems associated with the identification of problematic features such as bores towers and minor roads. It is estimated that the total cost of resolving the identification of problematic features in a typical 1:250K map tile is about 5% or less of the total cost of the revising the map. This is a small proportion of the total cost of production of a 250K map and accompanying data. However, having access to high-resolution imagery would help to resolve many of these problems in an effective way to maintain the quality of the maps. Interviews with key map revision staff in relation to the discrimination, identification and mapping of problematic features reveal that, even using aerial photography, they would still not be able to identify all features. The major issue is accurate differentiation of, as well as naming/labelling of feature attributes. Typical queries ask what the feature is, what its name is and what its structure is. Examples include: does this bore also have a wind pump? Is this a fence or a track? Greater resolution imagery will not greatly help such problems. The general consensus is that it is extremely difficult or even impossible to map features such as powerlines of less than 110 kv capacity, windmills and telecommunication towers from SPOT-2/4 and ETM+ imagery. This does not necessarily mean that powerlines below 110 kv capacity are of lesser importance to map users, or of significantly lower landmark value in different parts of Australia. Mathews et al (2002) states that towers were easily identified during aerial surveys, due both to their height and the reflectance of sheds that are typically constructed at their bases; in this investigation SPOT-5 2.5m

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imagery did not assist in identifying communication towers. The way forward is to build stronger relationships with key industry and federal/state/local government agencies that maintain these types of datasets. These agencies include the RAAF, Telstra, Optus and Spectrum Management authorities. The findings from this investigation demonstrate that high-resolution imagery such as SPOT-5 data improves the interpretability of topographic features, enhances the quality of maps as demonstrated in Figures 3-6. 6.0 Semi-Automated Feature Extraction The project tested a semi-automated road feature extraction system (RoadMap software) over SPOT-5 2.5 imagery of Katherine site. This was done in collaboration with the Digital Mapping Laboratory (Professor David McKeown and Dr Wilson Harvey) in the Department of Computer Science, Carnegie Mellon University, to test semi-automated feature extraction tools that may potentially be used in NMD's map production process. In RoadMap, “…the user explores the utility of integrating automated extraction tools in a graphical user interface to aid an operator in the feature extraction task. Also prominent in RoadMap is the use of cooperative techniques using complementary methods to extract image features. This technique, pioneered at the Digital Mapping Laboratory, is a common theme in this feature extraction research” as noted by McKeown and Harvey (2003, personal communication). The results of road extraction by RoadMap are impressive. There is a good correlation between manual road digitisation and the semi-automated road extraction of RoadMap software (Figure 7). Based on the first author's personal experience and judgement, most road detection algorithms do not perform well over urban (complex) scenes (Forghani, 2000). RoadMap worked very well in the extraction of streets and main roads over the Katherine area. However, RoadMap did not perform well over the rural area in extracting tracks and access roads. The reason may be that the operator did not direct the extraction of roads over the rural roads. In RoadMap the operator primarily directs extraction and, when the automated road extractor fails, the user can fall back on a manual editor incorporated into the process. Another reason may be that the background contrast of roads in rural areas is often not as strong as in urban areas, and semi-automated tracking performance depends on a number of factors, including background contrast, as stated earlier. Although the authors consider that the RoadMap software currently does an effective job, the system still requires operators who can tightly integrate their knowledge and skills into the extraction loop. It is still very much a research system. Hopefully the software will be available for commercial use in the near future.

Figure 7. Comparison of mapped roads by RoadMap semi-automated feature extraction (pale blue lines), manual digitisation through visual interpretation (thick red lines) and NMD GEODATA TOPO 250K (yellow lines).

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If the results appear very clean in this report, it is due to the quality of the road tracker, where RoadMap was used interactively. It is not a reflection of what an automatic road detection/tracking/network generation system would produce for this imagery. McKeown and Harvey (2003, personal communication) states “…we've learned over the years is that full automation feature extraction is likely to be too hard, and if it isn't nearly 100% there will always be a role for a human to perform corrections. If that's the case, then it is probably best to put that person in the midst of the feature extraction process, but to leverage his work in areas of high value”. One of the issues with RoadMap is that of editing corrections, as well as finding situations where high accuracy (road width, road material type) and other automatically derived sets of information are both useful and valuable. Currently, human’s monoplotting from ortho-imagery is difficult to compete with, according to McKeown and Harvey (2003, personal communication). 7.0 Conclusions Comparative assessment of SPOT-5 with ETM+ and SPOT-2 revealed the following key findings:

SPOT-5 imagery improved the identification and delineation of problematic features such as tracks, fences, access roads, individual buildings and trees, airstrips and airport taxiways, drainage and water bodies over the two study sites. This research shows that high-resolution imagery enhances the interpretability of the imagery (as illustrated by Figures 3-6) and hence improves the quality of maps.

Technical examination of SPOT-5 reveals that the imagery provides an opportunity for NMA to consider this data for larger scale mapping applications, such as for the 50K and 25K scales. The 50K and 25K Specifications allow the capture and mapping of many features that are not captured in GEODATA TOPO 250K due to map generalisation constraints.

Semi-automated feature extraction technology is now maturing. The testing of automatic road extraction algorithms on SPOT-5 2.5m data demonstrated that map and spatial data producers could potentially incorporate this technology into the map production environment in future.

Acknowledgments The authors wish to thank NMD reviewers: Mr Ian O'Donnell, Mr Ian Shepherd, Mr Alister Nairn and Mr Daniel Jaksa for their support and revision of the paper. We would particularly like to thank Professor David McKeown and Dr Wilson Harvey of the Digital Mapping Laboratory at the Department of Computer Science, Carnegie Mellon University for testing their RoadMap software on SPOT-5 2.5m imagery. We would also like to thank colleagues who involved in the project for their support and assistance. The views expressed in this report are the authors' and not necessarily the views of Geoscience Australia. References Dowman, I. J. and Peacegood, G., 1988. Information Content of High Resolution Satellite Imagery, Photogrammetria, Vol. 43, pp. 295-310. ERDAS, 1997. ERDAS Field Guide, Fourth Edition, Atlanta, Georgia, USA, pp. 143-145. Forghani, A., 2002. Evaluation of KOMPSAT-1 versus SPOT-2/4 Pan for Maintenance of Geoscience Australia Topographic Databases. Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference, September 2 - 6, 2002, Brisbane, Queensland, pp. 1-12. Forghani, A., 2001. Evaluation of New Satellite Imagery Applications for Maintenance of AUSLIG Spatial Databases. Technical Report, Research and Development Section, Mapping and Maritime Boundaries Program, National Mapping Division, Geoscience Australia, August 2001, pp. 1-135. Forghani, A., 2000. Decision Trees for Mapping of Roads from Aerial Photography Employing a GIS-Guided Technique. Proceedings of the 10th Australasian Remote Sensing and Photogrammetry Conference, Adelaide, Australia 21-25 August 2000, pp.1-12. Konecncy, G., 1990. Mapping from Space. Review of Latest Technology in Satellite Mapping. Interim Representation International Mapping Remote Sensing Satellite Systems. Institute of Photogrammetry, University of Hannover, pp. 11-22. Konecncy, G., Mapping from Space, Workshop on International Cooperation and Technology Transfer, ISPRS Com VI, Parma 1999, 286-290. Konecncy, G. and Schiewe, J., 1996. Mapping from Digital Satellite Image Data with Special Reference to MOMS-02. ISPRS Journal of Photogrammetry & Remote Sensing, no. 51, pp. 173-181.

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Mathews, R., Barwick M. and Larkin G., 2002. Field Audit 2 Report, Central Western Queensland, NATMAP Field Audit Project, Mapping and Maritime Boundaries Program, National Mapping Division, Geoscience Australia, July 2002, pp. 1-40. McKeown, D., and Harvey W., 2002. Personal Communication. Moore, L., 2000. The US Geological Survey’s Revision Program for 7.5 Minute Topographic Naps, US Geological Survey, Mid-Continent Mapping Centre, Missouri, USA, Map 2000, pp. 1-7. Muller, F., Kaczybski, D., 1994. Evaluation of high resolution Russian satellite photographs for map revision up to the scale 1:25,000. Proceedings of the ISPRS Commission IV meeting, Georgia, USA, pp. 304-310. Petzod, B., 2001. Revision of Topographic Databases by Satellite Images, Accessed Online March 2001: www.ipi-uni-hannover.de Schiewe, J., 2001. Experiences from the MOMS-02 Project for Future Development. Accessed Online March 2001: www.ivw.uni-vechtech.del/personal/geoinf/jorchen.htm Solberg, R., 1992. Semi-automatic Revision of Topographic Naps from Satellite Imagery, XVI ISPRS, Washington D. C., USA, 1992. Walter, V., 2000. Automatic Change Detection in GIS Databases Based on Classification of Multispectral Data. IARPS, Vol. XXXIII, Amsterdam, 2000, pp. 1-8.