1. Group 3 Akash Agrawal and Atanu Roy 1 Raster Database

2. Chapter Organization 1.1 Raster Data 1.2 Raster Data in GIS – 1.2.1 Spatio-Temporal Data – 1.2.2 Field Operations – 1.2.3 Storage – 1.2.4 Retrieval Techniques 1.3 Concluding Remarks 2

3. Learning Objectives Learning Objectives (LO) – LO1 : Learn about Raster Data – LO2 : Learn about GIS Raster Database Why use Raster data in GIS? How Spatio-temporal data is represented? What are different Field operations? What are different Storage techniques? What are different Retrieval Techniques? Mapping Sections to learning objectives – LO1- 1.1 – LO2-1.2 3

4. Raster Data A raster image is rows and columns of cells organized in a rectangular grid. Each cell is called a Pixel. Each pixel stores a singular color/attribute value. Resolution of rater image is denoted by #pixels in row X #column of the grid. – 800X600 resolution denotes that the raster image contains 600 rows of 800 pixel each. 4

5. Learning Objectives Learning Objectives (LO) – LO1 : Learn about Raster Data – LO2 : Learn about GIS Raster Database Why use Raster data in GIS? How Spatio-temporal data is represented? What are different Field operations? What are different Storage techniques? What are different Retrieval Techniques? Mapping Sections to learning objectives – LO1- 1.1 – LO2-1.2 5

6. Raster Data in GIS The primary purpose is to display the detailed image on a map area or render its identifiable objects by digitization. Raster maps are ideally suited for mathematical modeling and quantitative analysis. Data storage techniques data are easy to program and gives good performance for data retrieval. Commonly used form of raster data in the field of GIS – aerial photographs of some area. Other raster datasets used in GIS – a digital elevation model – Map of reflectance of a particular wavelength of light. – Landsat – Electromagnetic spectrum indicators 6

7. Learning Objectives Learning Objectives (LO) – LO1 : Learn about Raster Data – LO2 : Learn about GIS Raster Database Why use Raster data in GIS? How Spatio-temporal data is represented? What are different Field operations? What are different Storage techniques? What are different Retrieval Techniques? Mapping Sections to learning objectives – LO1- 1.1 – LO2-1.2 7

8. How Spatio-Temporal data is represented? The ST data has become crucial – to understand cause and effect scenarios – development of dynamic models for the analysis of it. The Snapshot Model – Every layer in the snapshot model shows the state of geographic distribution at one time stamp. – Time intervals between any two layers may vary – There is no explicit implication for changes within the time lag of any two layers. 8

9. Learning Objectives Learning Objectives (LO) – LO1 : Learn about Raster Data – LO2 : Learn about GIS Raster Database Why use Raster data in GIS? How Spatio-temporal data is represented? What are different Field operations? What are different Storage techniques? What are different Retrieval Techniques? Mapping Sections to learning objectives – LO1- 1.1 – LO2-1.2 9

10. Field data Field data are an essential part of GIS systems. – give most up-to-date information about current events – Needed for creating/updating digital maps – Help in validating the available data sets. Field data source – Satellites – Geo-registered sensor networks etc. Field data set example – Satellite images, aerial photographs – Digitized paper maps – Earth Science data-sets, e.g. rainfall, temperature maps 10

11. Field operations Field data can be manipulated using – Map algebra – Image algebra Map algebra vs. Image algebra – Similarity: Operand: raster data – Difference: Image algebra deals with image properties such as color information, number of pixel, pixel size etc. Example trim/crop, zoom in/out etc. Map algebra deals with attribute maps such as temperature map, vegetation map etc. Example thresholding, gradient etc. 11

12. Map Algebra Map algebra – Operand: raster data – Operation: classified in four groups Local, focal, global and zonal Local operation: – The value of a cell in the new raster is computed only using the value of that cell in the original raster. – Example thresholding, point wise addition etc. 12 Figure: An example thresholding with threshold value of 4

13. Map Algebra (Cont…) Focal operation: – The value of a cell in the new raster is computed using the value of that cell and its neighboring cells in the original raster. – Example focal sum, gradient etc. 13 Figure: An example of focal operation. (a) Rook neighborehood. (b) Bishop neighborehood. (c) Queen neighborehood. (d) Focal sum using queen neighborehood.

14. Map Algebra (Cont…) Global operation: – The value of a cell in the new raster is computed using the location or values of all cells in the original raster data. – Example: global sum, global average etc. Zonal operation – the value of a cell in the new raster is a function of the value of that cell in the original raster and the values of other cells which appear in the same zone specified in another raster. – Example distance from nearest facility. 14

15. Image Algebra Map algebra – Operand: raster data/ Image – Operation: ignores the absolute location of pixels. come from image processing literature. used for display or rendering the image for manual analysis of demonstration purpose. Example: trim/crop, zoom in/out, rotate etc. 15 Figure: An example trim operation.

16. Learning Objectives Learning Objectives (LO) – LO1 : Learn about Raster Data – LO2 : Learn about GIS Raster Database Why use Raster data in GIS? How Spatio-temporal data is represented? What are different Field operations? What are different Storage techniques? What are different Retrieval Techniques? Mapping Sections to learning objectives – LO1- 1.1 – LO2-1.2 16

17. Storage Techniques Traditional Approach – standard file-based structure of TIF, JPEG, etc. – use custom software to retrieve data-items of interest – Pros: provide good compression and require less storage space. – Cons: difficult to index the data and hence has slower retrieval operation. Database Approach – stores the raster data items attributes such as geo-location, time-stamp, various properties etc. in database tables. – Use database query language such as SQL to retrieve data-item of interest. – Pros: allows quicker retrieval of the raster data. allows user defined attributes and support for ad-hoc queries. – Cons: require storage of millions of significantly sized records. 17

18. Learning Objectives Learning Objectives (LO) – LO1 : Learn about Raster Data – LO2 : Learn about GIS Raster Database Why use Raster data in GIS? How Spatio-temporal data is represented? What are different Field operations? What are different Storage techniques? What are different Retrieval Techniques? Mapping Sections to learning objectives – LO1- 1.1 – LO2-1.2 18

19. Retrieval Techniques Raster data sets are very rich in content Retrieval approaches – Meta-data approach (database approach) – Content based retrieval (image processing technique) Meta-data approach – stores values of descriptive attributes for each raster data item. – uses simpler SQL data types such as numeric, string, date etc. – queries to select a set of descriptive attributes such as location, time-stamp, subject etc. – Pros: Simpler to implement gives accurate answers for queries to select a set of descriptive attributes. – Cons: Queries are limited to descriptive attributes. does not support “similarity” based queries 19

20. Retrieval Techniques (Cont…) Content based retrieval or content based image retrieval (CBIR) – content of an image is represented by extracted primitive visual features such as representing color, shape and texture. – Similar image queries are answered based on some combination of these primitive features. – CBIR is a two step approach Step 1: compute a feature vector or attribute relation graph (ARG) for each image in the database. Step 2: given a query image, compute its ARG and compare to the ARGs in the database for the image most similar to the query image. – The success of this approach depends on efficiency of feature and similarity measure, used to compare two ARGs. 20

21. 21