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Digitizing / Non Spatial to Spatial
EarthRome can potentially convert your Non Spatial Data to Spatial Raster and or vector data to use in a GIS application. (Photographs or Art are not included in our Services)
Convert scanned images into vector-based feature layers can be used in a GIS or Mapping software to convert into various formats such as SHP, DWG, KML, KMZ, GPX, DGN and more.
Common GIS Items to Convert from Raster to Vector:
c) Roads or access on an image
e) PDF/PNG/TIFF/JPEG etc.
Vectorization can involve a series of procedures to achieve an acceptable raster-to-vector conversion
1. Preprocessing the raster
2. Defining the scope
3. Determining optimal settings
4. Generating vector features
Various amount of geographic information may still exist on hard-copy maps, integrating this data into a GIS is important.
For bulk conversion, it may be more cost effective and less time consuming for EarthRome to help you than to do it yourself, leading to better time management and efficiency of your data conversion needs.
1. It has a simple data structure.
2. Overlay operations are easily and efficiently implemented.
3. Scanning technologies can supply huge quantities of data cheaply.
4. Image processing techniques produce data for integration to GIS in a raster format.
5. Area and polygon analysis is simple.
6. Overlaying and merging are easily performed.
7. The technology is cheap and in the future it will have greater cost advantages.
8. It is well suited to subdividing spatially continuous variables.
1. The sheer volume of data to be stored and handled can be very high.
2. There can be a serious loss of detail with larger pixel sizes (poor resolution).
3. Final maps can be fairly crude, especially those produced on cheaper GIS software.
4. Linear type analysis is more difficult.
5. Topological relationships are difficult to represent.
1. It has a relatively compact data structure so storage requirements are less.
2. Features can be accurately located.
3. The topology can be completely described with network linkages.
4. Very small features can be shown and all features can be accurately drawn.
5. Data about individual features can easily be retrieved for updating or correction.
6. Linear type analyses are easily performed.
1. It has a more complex data structure.
2. Overlay operations are difficult to implement.
3. The representation of high spatial variability is inefficient.
4. Manipulation and enhancement of digital images cannot be effectively performed.
5. Data capture can be very slow.
6. Area or polygon analyses are difficult.
7. This is generally a more expensive data structure in terms of data capture and software purchase.