Monika Sester

Institute of Cartography and Geoinformatics

Leibniz Universität Hannover

Germany

Collaborative data acquisition and processing

Observation

More and more digital spatial data sets are available that are accessible via web-services

-> OGC standard; INSPIRE; Geodata infrastructures More and more new sensors are available that measure

elements/attributes of phenomena of our environment More and more information is gathered by “the crowd”

This leads to a huge collection of spatially relevant or related information of different …

Type Quality Up-to-dateness Granularity in geometry and semantics …

2010/dagstuhl

Observation ff

Semantic Web – the web of data Information which is semantically annotated “a little semantics brings you a long way” E.g.: location information

Gazetteers: Geonames OSM - OpenStreetMap Wikipedia-Articles with placenames contain coordinate …

2010/dagstuhl

Delineation of landscapes: Weserbergland

2010/dagstuhl

Mapping all articles containing “Weserbergland”

Observation ff

Semantic Web – the web of data Information which is semantically annotated “a little semantics brings you a long way” E.g.

Gazetteers: Geonames OSM - OpenStreetMap Wikipedia-Articles with placenames contain coordinate …

Idea: exploitation of all available information for incremental refinement

and enrichment Decentralized processing possible, as “local information matters

locally”;

• -> scalability

• -> fault tolerance 2010/dagstuhl

Cooperative, decentralized processing

Geosensor network for precipitation measurement

Many measuring devices with (possibly) limited capabilities (but given quality)

Cooperation in local environment with neighboring sensors (underlying idea: measured phenomena is same / similar)

Incremental refinement Confirmation of existing data -> increasing quality (i.e.

averaging) Refinement in terms of acquisition of increasing detail

Example: determination of rainfall using moving cars as moving rain gauges

2010/dagstuhl

Rainfall

Most important input information for hydrological planning and water resources management

Especially: highly dynamic and nonlinear processes like floods, erosion or wash out of pollutants

High variability in space and time Measuring of rainfall:

non-recording rain gauges with a daily observation interval, sufficient density (station per 90 km2)

recording rain gauges for the observation of short time step rainfall -> not adequately dense (one station per 1800 km2)

Weather radar

• raw reflectivities have to be converted into rainfall intensities -> sufficient point precipitation network is needed for calibration

IDEA: use cars as moving sensors for rainfall2010/dagstuhl [Haberlandt & Sester, hessd, 6(4) 2009]

Simulation

Distribution of cars depending on type of road, time of the day Assumption of different equipment rates: 0.5, 1, 2 and 4% of

cars are equipped with such a system

2010/dagstuhl

Idea: use cars as moving rain sensors

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1 point in time 1h

2010/dagstuhl

Selke

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Sensor equipment rate [%]

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Trautenstein

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Performance of areal rainfall estimation from the rain gauge network (horizontal red line) and from the car networks with different sensor equipment rates (bars). For interpolation of the car rainfall observations IK4 (heavy dotted bars), IK10 (medium dotted bars) and OK (light dotted bars) are used. In addition the average network densities Dsub for the station network (blue triangles) and the car networks (blue squares) for each subbasin are provided. 2010/dagstuhl

On-going work

Current simulation: Assumed rainfall measurement:

• Exact, 10 intervals, 4 intervals

But in reality: measurement of wiper frequency, instead of rainfall

-> need for calibration, i.e. determination of R=f(W) For each car (type), driver, location (forest vs. open area), …

Idea: Distributed calibration of R-W-relationship

[Schulze, Brenner, Sester, 2010, ISPRS-SDH, HongKong, Cooperative Information Augmentation in a Geosensor Network]

2010/dagstuhl

Local communication + cooperation: -> WR-relationship

Station Sa: Ra

Car C1:W1

R1=f(W1, Ra,d(C1,Sa))

Station Sb: Rb

2010/dagstuhl

Local communication + cooperation: -> WR-relationship

Station Sa: Ra

Car C1:W1

Car C2,:W2

R2=f2(W2,R1‘,d(C1,C2))

Station Sb: Rb

R2=f‘2(W2,Rb,d(C2,Sb))

2010/dagstuhl

Quality of rainfall measurement of individual car

2010/dagstuhl

Using Kalman filtering

Standard deviation of rainfall measurement

2010/dagstuhl

Challenges

Distributed sensing&computing: challenges and benefits

Challenges: Sensors deliver: Heterogeneous data Heterogeneous quality Heterogeneous coverage Heterogeneous data types: from low-level to high-level

information, e.g. raw Lidar points to GIS-data

Benefits: Highly timely information -> “instant information” Scalability Redundancy – fault tolerance:

• System does not depend on one sensor Multi-purpose use of data (beyond original acquisition purpose)

2010/dagstuhl

Challenges

Blur between data acquisition and processing / analysis Decentralized data handling and processing Data structures that handle hierarchical, multi-representational

data sources Information fusion (depending on quality of integrated data

sources) Handling of temporal changes

When does a change happen, how many measurements have to “vote” for it?

2010/dagstuhl