Raster Datasets Research Articles

Multi-scaled identification of landscape character types and areas in Lushan National Park and its fringes, China

China’s national parks adopt a resource-oriented protection and planning approach that cannot restrain the continuous landscape fragmentation and deterioration, whereas, we propose to characterise the landscape in order to protect its integrity. This paper described a hierarchical identification of landscape character types and areas in Lushan National Park and its fringes according to a refined combination of the parametric and the holistic methods in a multiscalar approach. In terms of the functional hierarchy of landscape character, we decided to order the available data sources in ‘downscaling’. At the broad scale, landscape typologies were delimited by raster datasets of four natural attributes: land cover, soil, vegetation, and altitude. At the intermediate scale, landscape typologies were determined by raster datasets of six natural and cultural attributes: aspect, slope, relief amplitude, heritage density, geology and land use. At these two scales, we adopted the principal component analysis (PCA) and two-step cluster analysis in SPSS software to visualise landscape types, to modify and integrate the results obtained in the eCognition software, as well as to rectify the visualisation with manual identifications. At the detailed scale, landscape typologies were demarcated by two raster and one vector datasets of cultural attributes: building density, visual influence and time depth. We performed the visualisation and integration with a similar method except for the PCA step. This multi-scaled identification will provide a nested framework facilitating the integration of the broad Lushan region in both spatial and administrative dimensions.

Assessing Impacts on the Natural Resource Soil in Life Cycle Assessment: Methods for Compaction and Water Erosion.

There are currently limited life cycle impact assessment methods existing for assessing impacts on the natural resource soil. In this paper, we develop methods for the assessment of compaction and water erosion impacts within one framework, which can be expanded with additional degradation processes in the future. Our methods assess potential long-term impacts from agricultural activities on the production capacity of soils and are able to distinguish between different management choices such as machinery selection and tillage practices. Characterization factors are provided as global raster data sets at high spatial resolution (∼1 km) and for larger geographic units including uncertainties of spatial aggregation. Uncertainties due to variability of climate and weather are provided where possible. The application of the methods is demonstrated and discussed in a simplified case study. Results show that in a highly mechanized scenario of global agriculture without any conservation measures, long-term yearly soil productivity losses due to compaction and water erosion can amount to up to double-digit percentages for major crops. This confirms the relevance of compaction and water erosion impacts for agricultural LCAs.

Detection of Levee Damage Based on UAS Data—Optical Imagery and LiDAR Point Clouds

This paper presents a methodology for levee damage detection based on Unmanned Aerial System (UAS) data. In this experiment, the data were acquired from the UAS platform, which was equipped with a laser scanner and a digital RGB (Red, Green, Blue) camera. Airborne laser scanning (ALS) point clouds were used for the generation of the Digital Terrain Model (DTM), and images were used to produce the RGB orthophoto. The main aim of the paper was to present a methodology based on ALS and vegetation index from RGB orthophoto which helps in finding potential places of levee failure. Both types of multi-temporal data collected from the UAS platform are applied separately: elevation and optical data. Two DTM models from different time periods were compared: the first one was generated from the ALS point cloud and the second DTM was delivered from the UAS Laser Scanning (ULS) data. Archival and new orthophotos were converted to Green-Red Vegetation Index (GRVI) raster datasets. From the GRVI raster, change detection for unvegetation ground areas was analysed using a dynamically indicated threshold. The result of this approach is the localisation of places, for which the change in height correlates with the appearance of unvegetation ground. This simple, automatic method provides a tool for specialist monitoring of levees, the critical objects protecting against floods.

Application of airborne hyperspectral data for mapping of invasive alien Spiraea tomentosa L.: a serious threat to peat bog plant communities

Remote sensing is increasingly widely used in nature conservation management. The research focuses on developing an optimal set of airborne raster data for the identification of the invasive alien species Spiraea tomentosa L. The plant species selected for the purposes of this study poses a serious threat to peat bog plant communities, moist coniferous forests, and meadows in Central Europe. The impact of the data acquisition time on the accuracy of classification and the percentage cover limit required for correct identification of a target species using the developed method were also investigated. The study area is located in the Lower Silesian forests in Poland and is protected as a Natura 2000 site. Airborne hyperspectral and laser scanning data were simultaneously acquired two times in the growing season (August and September 2016) parallel to on-ground reference data collection. The 1 m resolution HySpex images with spectral range of 0.4–2.5 μm were corrected atmospherically, radiometrically and geometrically. Airborne Laser Scanning (ALS) data acquired at 7 points/m2 were used to generate several products, e.g. Canopy Height Model (CHM), rasters representing morphometric features of the area (Multiresolution Index of the Ridge Top Flatness or Valley Bottom Flatness—MRRTF, MRVBF), wetness relations (Topographic Wetness Index—TWI) and the availability of light (Total Insolation—TI), intensity of laser pulse reflection and geometric relations of vegetation points (i.a. Vegetation Cover, Vegetation Mean Intensity). The Random Forest (RF) classification and different raster datasets were used to identify the target species. As a result, the highest accuracy was obtained for the scenario based on HySpex images acquired in September. The accuracy (f1 score) for the target species achieved 83%. The developed method for the identification of Spiraea tomentosa has a great potential for application and can be used for monitoring peat bogs threatened by invasion of alien plants.

Indexing Mixed Aperture Icosahedral Hexagonal Discrete Global Grid Systems

Discrete global grid systems (DGGSs) are an emerging multiresolution 3D model used to integrate and analyze big earth data. The characteristic of multiresolution is usually realized by hierarchically subdividing cells on the sphere using certain refinement. This paper introduces mixed aperture three- and four- icosahedral hexagonal DGGSs using two types of refinement, the various combinations of which can provide more resolutions compared with pure aperture hexagonal DGGSs and can flexibly design the aperture sequence according to the target resolutions. A general hierarchy-based indexing method is first designed, and related indexing arithmetics and algorithm are developed based on the indexing method. Then, the grid structure on the surface of the icosahedron is described and by projection spherical grids are obtained. Experiments show that the proposed scheme is superior to pure aperture schemes in choosing grid resolutions and can reduce the data volume by 38.5% in representing 1-km resolution raster dataset; using the proposed indexing arithmetics to replace spherical geometry operations in generating discrete spherical vector lines based on hexagonal cells can improve the generation efficiency.

Sea-Level Rise Induced Multi-Mechanism Flooding and Contribution to Urban Infrastructure Failure

Sea-level rise (SLR) induced flooding is often envisioned as solely originating from a direct marine source. This results in alternate sources such as groundwater inundation and storm-drain backflow being overlooked in studies that inform planning. Here a method is developed that identifies flooding extents and infrastructure vulnerabilities that are likely to result from alternate flood sources over coming decades. The method includes simulation of flood scenarios consisting of high-resolution raster datasets featuring flood-water depth generated by three mechanisms: (1) direct marine flooding, (2) storm-drain backflow, and (3) groundwater inundation. We apply the method to Honolulu’s primary urban center based on its high density of vulnerable assets and present-day tidal flooding issues. Annual exceedance frequencies of simulated flood thresholds are established using a statistical model that considers predicted tide and projections of SLR. Through assessment of multi-mechanism flooding, we find that approaching decades will likely feature large and increasing percentages of flooded area impacted simultaneously by the three flood mechanisms, in which groundwater inundation and direct marine flooding represent the most and least substantial single-mechanism flood source, respectively. These results illustrate the need to reevaluate main sources of SLR induced flooding to promote the development of effective flood management strategies.

A two-level storage strategy for map-reduce enabled computation of local map algebra

In the big data era, high-resolution raster-based geocomputation has been widely employed in geospatial studies. The algorithms used in local map algebra operations are data-intensive and require a large memory space and massive computing power. Simply employing distributed computing framework such as Hadoop to serve such applications incurs storage and performance issues. In this paper, we present a two-level storage strategy specially for map-reduce implementation of local map algebra algorithms under Hadoop. This approach implements efficient storage and manipulation of large raster data sets through three processes: (1) partitioning a raster file into square tile sets, (2) compressing and reorganizing these tile sets to prevent tile overlap across data divisions, and (3) improving MapReduce’s I/O interfaces for data exchange of parallel computation of map algebra. Experiments with real-world datasets show that the proposed strategy can achieve high speedup and efficiency for raster-based spatial analysis applications. The results also show that the strategy has satisfactory scalability as the number of data nodes in clusters or the raster data volume is increased.

Archaeological Remote Sensing Using Multi-Temporal, Drone-Acquired Thermal and Near Infrared (NIR) Imagery: A Case Study at the Enfield Shaker Village, New Hampshire

While archaeologists have long understood that thermal and multi-spectral imagery can potentially reveal a wide range of ancient cultural landscape features, only recently have advances in drone and sensor technology enabled us to collect these data at sufficiently high spatial and temporal resolution for archaeological field settings. This paper presents results of a study at the Enfield Shaker Village, New Hampshire (USA), in which we collect a time-series of multi-spectral visible light, near-infrared (NIR), and thermal imagery in order to better understand the optimal contexts and environmental conditions for various sensors. We present new methods to remove noise from imagery and to combine multiple raster datasets in order to improve archaeological feature visibility. Analysis compares results of aerial imaging with ground-penetrating radar and magnetic gradiometry surveys, illustrating the complementary nature of these distinct remote sensing methods. Results demonstrate the value of high-resolution thermal and NIR imagery, as well as of multi-temporal image analysis, for the detection of archaeological features on and below the ground surface, offering an improved set of methods for the integration of these emerging technologies into archaeological field investigations.

Estimating exposure of residential assets to natural hazards in Europe using open data

Abstract. Natural hazards affect many types of tangible assets, the most valuable of which are often residential assets, comprising buildings and household contents. Yet, information necessary to derive exposure in terms of monetary value at the level of individual houses is often not available. This includes building type, size, quality, or age. In this study, we provide a universal method for estimating exposure of residential assets using only publicly available or open data. Using building footprints (polygons) from OpenStreetMap as a starting point, we utilized high-resolution elevation models of 30 European capitals and pan-European raster datasets to construct a Bayesian-network-based model that is able to predict building height. The model was then validated with a dataset of (1) buildings in Poland endangered by sea level rise, for which the number of floors is known, and (2) a sample of Dutch and German houses affected in the past by fluvial and pluvial floods, for which usable floor space area is known. Floor space of buildings is an important basis for approximating their economic value, including household contents. Here, we provide average national-level gross replacement costs of the stock of residential assets in 30 European countries, in nominal and real prices, covering the years 2000–2017. We either relied on existing estimates of the total stock of assets or made new calculations using the perpetual inventory method, which were then translated into exposure per square metre of floor space using data on countries' dwelling stocks. The study shows that the resulting standardized residential exposure values provide much better coverage and consistency compared to previous studies.

Evaluation of Water Yield and Soil Erosion in the Three-River-Source Region under Different Land-Climate Scenarios

Exploring the water yield and soil conservation in the Three-River-Source region is of great significance for evaluating both the ecological stability of the Qinghai-Tibet Plateau, Yellow River basin, Yangtze River basin and Lancang River basin and the sustainable development of human society. The data sources for this study were land use/cover data from four phases (2000, 2005, 2010 and 2015), daily precipitation and temperature datasets, and the 1:1000000 Chinese soil database. These data were combined with vector data, such as data on settlements, roads, and rivers, along with population, economic raster datasets and CCSM4 common climate model prediction results. The Three-River-Source region was taken as the study area, and four land use/cover development scenarios and two climate change scenarios were designed based on the FLUS model and the downscaling correction method. The InVEST model was used to quantitatively simulate the water yield and soil erosion under different scenarios in the study area in 2030. The results showed the following: (1) Under different land use/cover development scenarios, grassland remained the dominant land use/cover type in the Three-River-Source region, and the area ratio was always greater than 67%. (2) Under the RCP4.5 climate scenario, the annual water yield and soil erosion increased by more than 7% and 3.9%, respectively. Under the RCP8.5 climate scenario, the annual water yield and soil erosion decreased by more than 3.3% and 1.3%, respectively. (3) Climate change played a leading role in the changes in water yield and soil erosion. Climate change contributed as much as 89.97%–98.00% to the change in water yield and 60.49%–95.64% to the change in the soil erosion modulus. However, the contribution of land use/cover changes to the change in regional water yield was only 2.00%–10.03%, and the contribution of the soil erosion modulus change was 4.36%–39.91%. Therefore, the land use development strategy in the Three-River-Source region should comprehensively consider issues such as regional development, the input of returning farmland to forest and grassland, and the resulting ecological benefits.

The response of dust emission sources to climate change: Current and future simulation for southwest of Iran

This study recognizes present dust emission sources (DESs) and their future projections in the southwest of Iran (2050 and 2070) through simulations performed by distribution models. The sites observation, raster dataset of climate layers and statistical models in the MaxEnt software were used to predict the current and future dust coverage and distribution, and their response to climate change using representative concentration pathways (RCPs) scenarios of +2.6, +4.5, +6.0, and +8.5 W m−2 projections. Statistical machine learning analysis show that 40.8% of the study area covering 25,810 km2 are susceptible to emit dust at the present time, and its projections will increase up to 28,839, 26,002, 26,071 and 26,124 km2 for RCP scenarios of +2.6, +4.5, +6.0, and +8.5 W m−2, respectively, by the year 2070. Temperature and precipitation assessments show that the most effective parameters determine future changes in DES coverage and distribution. The area under the curve (AUC) for DESs was 0.919, and results of Jackknife analyses show high sensitivity of dust sources to climate variables. The results illustrate that the present DESs are mainly driven by a combination of temperature, precipitation and land-use management, and the effects of nature are comparable to the anthropogenic activities, as humans continue to shape the DESs through energy, water and land use. The predicted increase of DESs may substantially worsen dust storms in the future, thereby affecting the functioning of ecosystems as well as human health. The outcomes of this study may support biocrust restoration technologies as a suitable option in sustainable management of arid lands and dust emission sources.

Efficient processing of raster and vector data.

In this work, we propose a framework to store and manage spatial data, which includes new efficient algorithms to perform operations accepting as input a raster dataset and a vector dataset. More concretely, we present algorithms for solving a spatial join between a raster and a vector dataset imposing a restriction on the values of the cells of the raster; and an algorithm for retrieving K objects of a vector dataset that overlap cells of a raster dataset, such that the K objects are those overlapping the highest (or lowest) cell values among all objects. The raster data is stored using a compact data structure, which can directly manipulate compressed data without the need for prior decompression. This leads to better running times and lower memory consumption. In our experimental evaluation comparing our solution to other baselines, we obtain the best space/time trade-offs.

IDENTIFYING SUITABLE AREAS FOR SMALL FARM RESERVOIR IN AGUSAN DEL NORTE USING GEOGRAPHIC INFORMATION SYSTEM

Abstract. The Philippines, as a part of Southeast Asia, falls into the zone categorized with agricultural areas prone to drought. Mitigating the concerns due to such vulnerability and also catering the needs of small-time farmers, rainfall harvesting with the use of agricultural structures called Small Farm Reservoirs (SFRs) used for storing water for irrigation are being used and deployed. This study aims to identify ideal locations for SFR which is essential due to the concerns of its geographical appropriateness and its vulnerability to environmental and other concerns such as seepage, sustainability, and optimizing yield. Geographic Information System through the ArcMap software is a modern tool for decision making, especially in the concerns of suitability and vulnerability assessment and was used in creating the suitability map for SFR in this study. The suitability map was created with the use of pre-processed raster data sets for precipitation, soil texture, slope, irrigation status. Weights for the weighted overlay were acquired from the standardized protocol for Small Scale Irrigation Project (SSIP) standardized protocol for site suitability identification from the Department of Agriculture - Bureau of Soil and Water Management (DA-BSWM) Philippines. Existing SFRs in the study area were validated through site visitation and were evaluated for usability and performance. The suitability map created accurately identified suitable zones for establishing SFRs with a result of 7 out of 7 existing SFRs are within the suitable zones, six (6) sites in the moderately suitable and one (1) on marginally suitable.

Surface Reconstruction for Three-Dimensional Rockfall Volumetric Analysis

Laser scanning is routinely being used for the characterization and management of rockfall hazards. A key component of many studies is the ability to use the high-resolution topographic datasets for detailed volume estimates. 2.5-Dimensional (2.5D) approaches exist to estimate the volume of rockfall events; however these approaches require rasterization of the point cloud. These 2.5D volume estimates are therefore sensitive to picking an appropriate cell size to preserve resolution while minimizing interpolation, especially for lower volume rockfall events. To overcome the limitations of working with 2.5D raster datasets, surface reconstruction methods originating from the field of computational geometry can be implemented to assess the volume of rockfalls in 3D. In this technical note, the authors address the methods and implications of how the surface of 3D rockfall objects, derived from sequential terrestrial laser scans (TLS), are reconstructed for volumetric analysis. The Power Crust, Convex Hull and Alpha-shape algorithms are implemented to reconstruct a synthetic rockfall object generated in Houdini, a procedural modeling and animation software package. The reconstruction algorithms are also implemented for a selection of three rockfall cases studies which occurred in the White Canyon, British Columbia, Canada. The authors find that there is a trade-off between accurate surface topology reconstruction and ensuring the mesh is watertight manifold; which is required for accurate volumetric estimates. Power Crust is shown to be the most robust algorithm, however, the iterative Alpha-shape approach introduced in the study is also shown to find a balance between hole-filling and loss of detail.

AN IMPROVED PIXEL COUNTING METHOD FOR ARBITRARY ZONAL STATISTICS ON GLOBELAND30

Abstract. Global Land cover is essential for ecological study, climate and environmental changes, resources management, human activities as well as sustainable development. With the finer resolution products arising, e.g. GlobeLand30, quantitative analysis of the earth dynamics based on raster data sets is becoming possible. For arbitrary zonal statistics, pixel counting method is an operational practice widely used for area estimation by counting the number of classified pixels and multiplying by the area represented by each pixel, though uncertainty. For large statistical regions, the error of the projection can not be neglect. Thus an rectified PC method is developed for arbitrary zonal statistics of all raster data sets. The test result shows that the method performance is stable and easy to zonal statistics.

Assessing the influence of DEM source on derived streamline and catchment boundary accuracy

Accurate DEM-derived streamlines and catchment boundaries are essential for hydrological modelling. Due to the popularity of hydrological parameters derived mainly from free DEMs, it is essential to investigate the accuracy of these parameters. This study compared the spatial accuracy of streamlines and catchment boundaries derived from available digital elevation models in South Africa. Two versions of Stellenbosch University DEMs (SUDEM5 and DEMSA2), the second version of the 30 m advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM2), the 30 and 90 m shuttle radar topography mission (SRTM30 and SRTM90 DEM), and the 90 m Water Research Commission DEM (WRC DEM) were considered. As a reference, a 1 m GEOEYE DEM was generated from GeoEye stereo images. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module. A reference catchment boundary was generated from the GEOEYE DEM and verified during field visits. Reference streamlines were digitised at a scale of 1:10 000 from the 1 m orthorectified GeoEye images. Visual inspection, as well as quantitative measures such as correctness index, mean absolute error, root mean squares error and figure of merit index were used to validate the results. The study affirmed that high resolution (<30 m) DEMs produce more accurate parameters and that DEM source and resampling techniques also play a role. However, if high resolution DEMs are not available, the 30 m SRTM DEM is recommended as its vertical accuracy was relatively high and the quality of the streamlines and catchment boundary was good. In addition, it was found that the novel Euclidean distance-based MAE and RMSE proposed in this study to compare reference and DEM-extracted raster datasets of different resolutions is a more reliable indicator of geometrical accuracy than the correctness and figure of merit indices.

Quantitative spatial upscaling of categorical information: The multi‐dimensional grid‐point scaling algorithm

Abstract Categorical raster datasets often require upscaling to a lower spatial resolution to make them compatible with the scale of ecological analysis. When aggregating categorical data, two critical issues arise: (a) ignoring compositional information present in the high‐resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (b) restricting classes to those present in the high‐resolution dataset assumes validity of the classification scheme at the lower, aggregated resolution. I introduce a new scaling algorithm that aggregates categorical data while simultaneously controlling for information loss by generating a non‐hierarchical, representative, classification system for the aggregated scale. The Multi‐Dimensional Grid‐Point (MDGP) scaling algorithm acknowledges the statistical constraints of compositional count data. In a neutral‐landscape simulation study implementing a full‐factorial design for landscape characteristics, scale factors and algorithm parameters, I evaluated consistency and sensitivity of the scaling algorithm. Consistency and sensitivity were assessed for compositional information retention (IRcmp) and class‐label fidelity (CLF, the probability of recurring scaled class labels) for neutral random landscapes with the same properties. The MDGP‐scaling algorithm consistently preserved information at a significantly higher rate than other commonly used algorithms. Consistency of the algorithm was high for IRcmp and CLF, but coefficients of variation of both metrics across landscapes varied most with class‐abundance distribution. A diminishing return for IRcmp was observed with increasing class‐label precision. Mean class‐label recurrence probability was consistently above 75% for all simulated landscape types, scale factors and class‐label precisions. The MDGP‐scaling algorithm is the first algorithm that generates data‐driven, scale‐specific classification schemes while conducting spatial data aggregation. Consistent gain in IRcmp and the associated reproducibility of classification systems strongly suggest that the increased precision of scaled maps will improve ecological models that rely on upscaling of high‐resolution categorical raster data.

MULTI-SCALE AND SCALE DIMENSION PROPERTIES IN SPATIAL RASTER MODELLING – CONCEPT AND CURRENT IMPLEMENTATION

Abstract. Various users and applications required different abstraction details of spatial model either in vector or/and raster data types/models. Generating different model abstraction details (e.g. Level of Detail/LOD) produces various drawbacks especially for data model sharing among stakeholders or publics. Different abstraction detail or LOD means different details in geometry, semantic information, attributes as well as different accuracy provided within the vector model (e.g. a certain LOD). On the other hand, raster dataset with different resolutions on certain information or layer (e.g. elevation, land cover, spatial imagery, soil type, thematic raster map and others) could also be considered as multi-scale raster modelling which produces similar drawbacks with additional storage redundancy/consumption and updating works. There are some solutions for vector scale modelling such as CityGML (3D) and multi-scale or vario-scale (2D) modelling induce good solutions for vector; however, there are no solution for raster data type (or model) yet. Thus, a concept description in categorizing and defining multi-scale for multi-resolution raster dataset should be introduced. This paper basically highlights the similarity of spatial 2D vector and raster type GIS dataset, some introduction and properties of raster dataset which able to be defined it as the same level of vector LoD in scale modelling. This paper basically tries to kick off a new multi-scale domain in supporting spatial raster dataset (new idea), which will be then be extend/expand by related researchers near the future. Discussion on successful implementation of vector multi-scale model will be in the paper as well as existing multi-scale approach in storing raster dataset as the main content of the paper. Some potential analysis on related multi-scale raster and validation are also discussed to give brief idea on what is spatial raster capable of; especially to those who are new/not yet engage with this multi-scale spatial raster dataset.

PySizer: resizing raster in eigenspace using Python

A common practice in spatial analysis, data fusing, and geophysical interpretation is the need to handle raster datasets of different spatial resolution. Often, raster data are resampled through interpolation for better spatial representation and feature identification at both local and regional scales, but interpolation can result in artifact such as fewer blocks or mosaics, boundary blurring, edge halos, rings, and signal aliasing. In this study, we present the PySizer program developed using the Python programming language for data interpolation, with advanced performance. First, it has high interpolation accuracy by using the inverse spatial principal analysis (isPCA) in the eigenspace. Secondly, it has high calculation performance by using functions inside scientific Python modules, such as matrix operations of the NumPy. Thirdly, it supports most of currently available raster data formats and can manage spatial references information. PySizer is applicable to many fields, such as geophysical or other scientific data processing, remote sensing, (internet) video stream interpolation, and high quality image printing, to name a few. The tool was tested in this study using a potential-field data and bathymetric data, and the results were evaluated through visual inspection and statistical analysis, demonstrating high performance in accuracy and efficiency in resizing raster datasets. The PySizer source codes are freely available from public website or by contacting the authors for the latest version.

A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System

The use of geographic data from early maps is a common approach to understanding urban geography as well as to study the evolution of cities over time. The specific goal of this paper is to provide a means for the integration of the first 1:500 urban map of the city of València (Spain) on a tile-based geospatial system. We developed a workflow consisting of three stages: the digitization of the original 421 map sheets, the transformation to the European Terrestrial Reference System of 1989 (ETRS89), and the conversion to a tile-based file format, where the second stage is clearly the most mathematically involved. The second stage actually consists of two steps, one transformation from the pixel reference system to the 1929 local reference system followed by a second transformation from the 1929 local to the ETRS89 system. The last stage comprises a map reprojection to adapt to tile-based geospatial standards. The paper describes a pilot study of one map sheet and results showed that the affine and bilinear transformations performed well in both transformations with average residuals under 6 and 3 cm respectively. The online viewer developed in this study shows that the derived tile-based map conforms to common standards and lines up well with other raster and vector datasets.