Università degli Studi di Padova Dipartimento di Ingegneria dell’Informazione Scuola di Dottorato...

Università degli Studi di PadovaUniversità degli Studi di PadovaDipartimento di Ingegneria dell’InformazioneDipartimento di Ingegneria dell’Informazione

Scuola di Dottorato in Scienze e Tecnologie dell’InformazioneScuola di Dottorato in Scienze e Tecnologie dell’Informazione

Sandro Savino

Who i am

What i did in Italy

What i can do in Holland

Delft, 10 June 2009

Sandro Savino, Delft 2009Sandro Savino, Delft 2009 2

Who i am• Name: Sandro

• Surname: Savino

• Second name: we rarely use them

• Born: 6th of July, 1981

• Where: Milan

• Resident in: Padova

• Working at: University of Padova


Padova• City: Padova• Position: 30 km west of Venice• Population: around 250.000• Very old settlement (long before the Roman

age), after the Roman age due to its vicinity its story was always bound to Venice’s

• Famous for: – A church with no name– A field with no grass– A cafè with no doors– Its University


University of Padova• Estabilished: 1222 (second eldest university of Italy after

Bologna)• Population: 63000 students, 2350 teachers (2007)• Fields of study: almost all of them, but some as

architecture and oriental languages (that are in Venice)• Famous personalities: Galileo Galilei, Copernico…• Structure: there is not a campus, the departments are

scattered around the city. The defense of Master Degree thesis are still held in the ancient building of “Bò”

• Some traditions:– Drinking spritz– The “papiro” (papyrus) and the celebrations for those who

graduate


What i did in ItalyAfter the high school i enrolled at the Department of

Information Engineering. Topics of study: Math, Physics, Signals & Systems, Computer Architecture, OS, Networks, DB, Programming and data structure, ….

• 2004: Bechelor Degree• 2005: 6 months at the Aberdeen University (Erasmus)• 2007: Master Degree: my thesis was developed inside

the CARtographic GENeralization (CARGEN) project• 2007: obtained a one-year scholarship to work on the

CARGEN project• 2008: enrolled in the PhD School of Sciences and

Technologies of Information• 2009: 6 months at TU-Delft !!


The CARGEN Project• CARtographic GENeralization

• Aims: automatic generalization of the Istituto Geografico Militare 1:25000 geodatabase (IGM DB25) from the 1:5000 geodatabase of the Regione Veneto (GeoDBR)

• The process is a db > db generalization, it is not intended for paper map production


Figures involved• Regione Veneto: those with the money.

– With a law of 1997 they are allowed to self-produce small scale maps. The idea is to use their ‘frequently’ updated 1:5000 cartography to produce 1:25000 and 1:50000 maps

– Most of their maps are in shapefiles (2.5D spaghetti coded) but they are slowly replacing them with a geodatabase

– At the present day there is a struggle in Italy to set up a unique standard national data model (now each region has its own)


Figures involved• Istituto Geografico Militare: those with the know

how. – Historical (and only) Italian NMA (est. 1870, soon

after the unification of Italy). IGM has some serious problems to keep all its small scale (1.25000 to 1.000.000) cartography up to date (actually it doesn’t: their 1:50000 maps date back to 1969!) They are slowly accepting the idea to derive their maps more than creating them from scratch.

– Their base map, the DB25, is a geodatabase 1:25000 scale.


Figures Involved• University of Padova: those who should make

things work– Team:

• 2 professors• 1 phd, 1 full-time employed• Many students doing their Msc or Bsc thesis

– Tools:• Language: Java and SQL• GIS: Intergraph Geomedia• DBMS: Oracle Spatial 10g• Hope, luck, patience and will


Starting point• Data model of 1:5000 GeoDBR

• Data model of 1:25000 IGM DB25

• Sample source data: Parco delle Dolomiti Bellunesi (132.000 ha)

• IGM specifications for:– DB25 acquisition from surveys– DB25 derivation (draft)


Data models: GeoDBR• Features classified in Layers - Themes – Classes• The model complies with italian Intesa GIS • Simple geometries, no topology (but topology checks)• Geometries: points, polylines (some), polygons with

holes (eg: woodland, cropland, buildings, roads, railroads, rivers, lakes)

• Networks (road, railways, rivers) are represented with– A graph in which each arc is a polyline– Areas, each of them has the same id of the corresponding arc

(not all the arcs have an area: ie minor rivers)– Attributes are stored on the arcs of the graph– There is NO INFORMATION that tells if two connected arcs are

part of a whole road or of a whole river


GeoDBR


Data Models: IGM DB25• Features classified in classes – subobjects• The classes are an extension of DIGEST

classification: each class name is a FACC code, preceded by a character (A,L,P) to identify its geometric type

• The model is very rich (around 250 objects, some quite ‘odd’ eg: BH175 Trough)

• Simple geometries, mostly points and polylines (polygons mainly for buildings, woodlands, croplands, lakes and bigger rivers). Points can be used to represent big objects (eg a soccer field) by the means of a later application of a symbol


DB25


Semantic generalization• The first step of the job was to analyze the

compatibility between the two data models and then to match each DB25 feature in one or more GeoDBR feature

• This work was done all by hand, reading carefully the definitions in the data models

• Some modifications have been proposed (and accepted) both to DB25 and GeoDBR to increase the compatibility (and thus the number of object that could be derived)


Semantic generalization• The second step was then to formalize the

relationships among the GeoDBR and DB25 features: this relations where structured into (pseudo)SQL queries

• Some of the pseudo queries could be directly translated into SQL queries

• Other needed some kind of computation and thus was necessary to develop some algorithms


Example• The object LCA010 – L901A “Contour

line on glacier” has not a match in the GeoDBR

• It’s possible to derive it by calculating the intersection between countor lines and glaciers


Example• In the IGM DB25 rivers classification is based

also on their width, but this attribute is not present in the GeoDBR model

• Our solution was to calculate the average width for each arc of the hydrography graph


Cartographic Generalization• When we approached the problem of cartographic

generalization, the first step was to write down a list of all the transformations that had to be applied to the original geometries in order to make their rappresentation compatible with the 1:25000 target scale

• This list of transformation was written ‘inspired’ by:– The DB25 data model– The IGM acquisition limits for each object in the DB25– An IGM draft on how to derive the DB25– Talking to –and trying to think as- cartographers– Comparing a rare (almost unique) example of DB25 map with

the source data


Example

Collapsing area to pointCollapsing area to line

Elimination of all road segments shorter than 250 m

Elimination of rivers shorter than 250 m

Collapsing dual carriageways to one line

Semplifying road junctionsSemplifying buildings

Rivers selection…

Due to data model

Due to data acquisition limits

Due to Generalization advices

Cartographic-wise inspiration


Cartographic Generalization• The whole list of required transformations

was then transformed in a ‘shopping list’ of algorithms– Some algorithms are rather simple or already

developed (eg. collapsing area to points, vertex simplification, … )

– Other required to be developed, (and in many cases from scratch since there isn’t a big lecterature on big scale generalization)


Example• A not easy algorithm: a complete and

robust solution to collapse areas to line (and infact we don’t have it yet!)

ok

??


Cartographic Generalization• For most of the DB25 objects the

requirements could be met using simple algorithms

• Most of the efforts in the cartographic generalization were due to a limited number of classes of the DB25:– Rivers– Roads– Buildings

157

82

Feature with requirementsFeature without requirements

34

149

44

32

Other

Generalization

Acquisition

Reconstruction


Generalization of Rivers• Main topics:

– Harmonization of the results of the width-based classification

– River selection:• Elimination of rivers shorter than 250m• Elimination of rivers too close to each other

– Typification of drainage networks


Rivers harmonization• according to the DB25 model, only rivers wider than 20

m are represented as areas: the others are lines• this classification can lead to some esthetically

unacceptable results that need to be solved (usually ‘upgrading’ that river segment or filling the empty space)


Rivers harmonizationSpace left empty after a river has been collapsed

to line due to its width

Empty space now filled

Harmonization the end of a river with areal

geometry


River SelectionProblems• According to the DB25 specs, all the river segments

shorter than 250 m should be eliminated, but we can’t do it with a query or we’ll shorten ALL the rivers by 250 m (and also disconnect part of the graph): we must select only those segments that are not the ‘head’ of a major river (or worse is in the middle of it)

• The 1:5000 source data has a really dense hydorgraphic network: in some areas it would be good to select the less important rivers and delete them. Unfortunately the GeoDBR taxonomy for rivers is poor (there are just 3 classes of rivers): we need a way to find which arc are part of the least important rivers

Solution: data enrichment


Rivers: data enrichment1. Build a river hierarchy: on the base of the

Z coordinate find for each river the ‘fathers’ and the ‘sons’

2. With a top to bottom procedure store in each arc new informations to enrich the data model:

• Strahler ordering• Number of branches uphill• Length to the furthest source uphill• (area of its watershed)


Rivers: going up the flowUsing the enriched data, with a bottom-up

procedure we can build the rivers: starting from the arc with the highest Strahler order we calculate a ranking among all its ‘fathers’ and we choose that with the highest score; we go up the flow this way until we find a source: the whole river has been built.

At the end of the procedure, all the arcs are part of one river


River SelectionThe requirements now can be met:• with a simple query we delete all the arcs that

are part of rivers shorter than 250 m• to select the rivers by density

– we build a buffer around each of them and calculating how much of their areas collides with (=are covered by) other buffers

– then recursively, starting from the rivers with more collisions, we delete one river at time and update the statistics about collisions. We stop when no buffer is covered by more than a certain threshold. Only minor rivers can be deleted: a ranking algorithm prevents that important rivers are deleted.


River Selection

Results

River kept

River selectedand eliminated


Drainage network typification

Problem: how to typify a drainage network?


Drainage network typificationSolution:1. cluster the drains on the base of their

direction2. for each cluster, find its envelope (the

convex hull can be too big and touch other features)

3. inside the envelope delete all the original drains and draw new drains, with the same direction and equally spaced (but at a greater distance one from the other)


Cluster


Boundary


Drawing new drains


Final result


Generalization of Roads• Main topics:

– Road selection:• Elimination of road segments shorter than 250m

– Road junction simplification– Double carriageways (Highways)


Road Selection

Problem:

• Similar to what we saw for the rivers: we can’t delete any arc shorter than 250 m. More than that, we can’t delete a dangling arc if it represents the only road that leads to a building.


Road Selection

Solution:A road shorter than 250 m is deleted only if:

• It is dangling• It is not the only access to a building• It is not on the map limit

40 m


Road Junction SimplificationAt 1:5000 scale the representation of some road

junctions is too complex and involves too many arcs

Will be presented

at UDMS 2009


Road Junction Simplification

Problem:

1. How can we find this ‘complex’ road junctions?

2. How can we solve them?


Road Junction Simplification

Solution:

1. Junctions that need to be simplified have too many redundant arcs. If they are redundant, they create a cycle in the graph! We find all the (small) cycles in the road graph

2. We classify the different type of junctions and apply a specific algorithm on each of them


Road Junction classificationEach junction, found searching cycles in the road graph, can be characterized by the number and shape of its:

– Incoming roads– Crossing roads– Distinctive nodes– Inner roads

Inner roads

Incoming road

Crossing road

Cluster of cycles

Distinctive nodes


Road Junction classification

ROUNDABOUTS

DELTA-T Junctions•One crossing road•One incoming road

CROSSROADS

•Two crossing roads

Other junctions

PAIRED DELTA-T Junctions•One crossing road•Two incoming roads


Road Junction generalization

Solution:


Highway Generalization

Problems:

• Double carriageways have to been collapsed to a centerline (when they are close enough)

• Highways can have intersections with:– Other highways– Normal road graph

• The arcs inside the area of a refueling station must be deleted


Gate / Toll

Double Carriageways

Refueling station

Highway junctions

What to do here??

How to couple?

Come semplificare?

How to simplify?


Highway Generalization

Solution:


Generalization of Buildings• Main topics:

– Building aggregation– Building simplification

• Simplification of the outline• Squaring

– Topology issues


Building Aggregation

Aggregation: according to the IGM DB25 specs building closer than 3 m have to be merged into one single building.

We need then an algorithm able to bridge the gap among candidate buildings and glue them together


Building Aggregation

convex hull

buffers

oriented MBR


Building Simplification• To simplify the buildings outline we

developed a modified version of Monika Sester algorithm

180° 0°

90°


Building Simplification• Improvements / Changes:

– The range of the degree is extended of +-15°– Area is spreaded on the face instead than deleted

(case 0°)– The size of the area deleted must be under a certain

threshold (case 180°)


Squaring of BuildingsReduces the number of different angles in a shape in the way that angles can be just multiples of a certain base angle set by the user.


Squaring of Buildings

Problem: not all the buildings should be squared!



Solution: test of squaring-ability (passed)

Gradi



Solution: test of squaring-ability (failed)

Gradi


Generalization of BuildingsResults


Topology IssuesProblem• To obtain a good result, the building

simplification algorithms should be applied to blocks of buildings, ie cluster of buildings that touch themselves.

• Unfortunately we can’t merge together all the buildings that touch, since doing so we’ll lose a lot of information; moreover in some cases this operation is strictly forbidden (eg. you can’t merge a church with the surrounding buildings)

• Applying the algorithms on each building leads to topology errors (overlaps among touching buildings)


Topology Issues

Solution:

• A solution would be an algorithm able to operate on the outline of a cluster of buildings keeping trace of the inner partitions of the cluster and dividing the cluster in the singular buildings once the simplification has been done

• This algorithm at the moment doesn’t exist.


To do / Open issues• Railway station generalization

• Displacement

• Intelligent partitioning


What i can do in Delft• Topics:

– Area to line collapsing algorithm?– Generalization of a cluster of buildings manteining the

inner partition?– Learn about topology and tGap?– Formalization of a possible data model for 1:50000

database?– 1:5000 > 1:25000 > 1:50000 MRDB ?– Trying to generalize Dutch Topo10 maps?– …ending this presentation that probably took too long!


Fine

Thank you for

your attention and patience!

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