linking objects of different spatial data sets by integration and aggregation

Linking objects of different spatial data sets by

integration and Aggregation

An article by Monika Sester, Karl-Heinrich Andres and

Volker Walter

Lecture by Gil Zellner

What is a map?

wikipedia: A map is a visual representation of an area—a symbolic depiction highlighting relationships between elements of that space such as objects, regions, and themes.

What is a map (cont’d) A map is not just a 2d image:

• List of objects• Partitions of areas• Linking information• Different versions of the same area• Aerial Photo• Satellite Image

Outline The article discusses ways of

integrating different maps onto a single easily accessible database, without losing information.

What is the problem with unification ?

Satellite images are not always available, often outdated, and more expensive.

What is the problem with unification? (cont’d)

Aerial photo limits• Aerial reconnaissance photos are taken

as “strips” of a larger whole. • Even the slightest (and with current

technology, unavoidable) shift in angle, connecting them is difficult

What is the problem with unification? (cont’d)

Even if we still had all the data:• Inaccuracies prevent us from matching

objects• Terrain is not flat, angle of

photography…• Information is not Absolute

Motivation Many maps today exist in many

different formats, each containing :• some correlating information• some different information

The TRUE potential of this information is when it is integrated and we can see all of it at once…

Motivation- examples Multi-national forces in IRAQ\

Afghanistan use non-stanag equipment, which uses arcane map formats, maps are essential for efficient cooperation!

- STANAG is a family of NATO standards for military equipment.

Motivation- examples (cont’d) Information from freely available

maps on web sites can be used to see trends in demographics, economy etc…

What is the closest chinese restaurant ?

Motivation- examples (cont’d)

Motivation- examples (cont’d)

Motivation- examples (cont’d)

Problem Many formats exist, integrating them

can be quite difficult without losing information

DLM = digital landscape modelCadastre = bordered maps

Solution? Conversion into a single format ?

Not a viable option, since data can become bloated and hard to decipher, also – some data STILL will be lost!

Solution – take 2 We keep all the original data, and

simply link the objects together, choosing when to use one format or another.

This article focuses on the linking aspects.

Our formats GDF – specifically designed for road

network data – vehicle navigation

Our formats (cont’d) ATKIS – Topographic data system

Our formats (cont’d)

Since the common data between system is roads, they are the matching primitives

Matching at object level The usual system for matching

information

This is not possible here!

What is geometric matching?

Matching at geometry level This we CAN do!

The different Approaches

Examples of geometric matching

Matching examples (cont’d)

Matching examples (cont’d)

How do we efficiently match these objects?

Cardinality of the matching pairs

Efficient matching (cont’d)

Normal Machine vision is clunky and difficult Solution: use noise margins, and Map the matching problem onto a communication system!

Noise margins

Series10

1

2

3

4

5

6

upper boundlower boundsample

Matching problem mapped onto a communication system

Matching function

2

| .

|; log

i i

i j j i

i ji j

i

P a is the probability that a is sent fromthetransmitter

P a b is theconditional probability that b was received whena was sent

P a bI a b

P a

Matching function (cont’d) In order to calculate the mutual

information I(D1,D2), the 2 data sets are seen as

messages which consist of symbols represented by our match primitives – the centerlines of streets.

Matching function (cont’d) For the matching of GDF and ATKIS

data we take account the length, shape, and position of start and end points

Matching function (cont’d) Our final function:

Results

Medium scale object from large scale data through Aggregation

Now that we know how to establish connections between objects of the same scale, we have another problem:

Multi-scale data objects

Multi scale data objects How do we match objects of different

scale ?• First we transform them to a similar

scale (data aggregation problem)

Scaling

Our formats: German ALK (1:500) ATKIS DLM25 (1:25000)

The process Classification

• Based on usage• Relations are check by combination

Aggregation• Adjoining parcels are aggregated• Separated areas are merged accordingly

Learning Aggregation rules Usage of “typical” machine learning

can be used here• What to group• Why group• When to group

Learning Objects and Semantic relations

1) Object Types2) Classification is derived from the

data set3) Classes created

Learning Objects and Semantic relations (cont’d)

Learning Objects and Semantic relations (cont’d)

Learning Objects and Semantic relations (cont’d)

1st phase Classification

Final Classification

Structural Description of knowledge acquired

Summary Linkage of objects based on

geometry Linkage of different scaled objects

Article Criticism Lack of proper explanation

Not self contained

Addresses problems without proper explanation of “Train of thought”

Questions?