1

Geomatics Synthesis ProjectFinal presentationNovember 24th 2010

Supervisors: Edward Verbree, Cristiaan Tiberius, Ben Gorte, Sisi ZlatanovaGroup members: Ye, Daniel, Martin, Marjolein, Bas, Simeon, Hoe-Ming, Sonia, Stratos, Amir, YiJing, Tom

2

Who are we and why are we here?

3

Synthesis Project Definition

Dedicated to the Climate City Campus (http://www.tudelft.nl/live/pagina.jsp?id=d278c89d-70ee-4257-9824-0bcf0cac34bf&lang=en)

“Make TU Delft a showcase for multidisciplinary climate research”

The Geomatics Synthesis project Our goal:Help building a fundamental framework which will support

multidisciplinary climate research in the campus.

Our objectives:“Provide the tools to measure and model the climate in the

campus“

4

Overview

o Urban Climate research

o The tools to do research on Campus Climate

o Showcases

o Building a spatial-temporal sensor system

o Building a 3D environment for climate research

o Conclusions and future work

5

Climate

Climate is what you expect, weather is what you get!

6

Urban ClimateWhat is (urban) climate?

Depends on:

o size

o location

o activities

o stage of development

o …

Example: the energy balance depends on changes of chemical content of the area, CO2 emmision of cars, changes reflection (energy fluxes) of landcover, etc.

7

Stakeholder requirementsStakeholder activity Climate parameter

of interestRequirements on 3D model and sensing system

CCC organization All climate parameters Extensible 3D environment able to store and manage urban objects and their thematic properties

Heat simulations Trees, surface properties Storage of surface parameters and trees. Shadow analysis on trees and buildings

Wind simulations Trees, porosity of tree canopy, building geometry

Ability to insert and extract buildings or trees from the model

People tracking and comfort level

Trees, surface properties, temperature

Continuous tracking of people for mobility research

8

Climate parameters

Dynamic

•Temperature•Wind•Precipitation•Ground Absorption•Air humidity•Air pollution•Soil Contamination•Energy Consumption•People behaviour

Static

•Surface properties•Buildings•Vegetation•Water bodies•Roof types•Building facade

9

The tools for climate research

a method is developed which enables seamless (anytime, anywhere) measurement ofurban climate parameters

a centralized information system able tostore and manage the sensed climate data along with 3D representations of the built environment

10

The tools for climate research

Spatial Database

Dynamic climate parameters measured by sensors

Static climate parameters by modeling city objects and their properties

Get data by

query

Spatial Database

11

Show casesUrban Planning

Research Techniques:

• Continuous tracking with Wi-Fi & GPS platform (picture)

• Temperature/Heat flux sensors (on platform) (Stored in database)

12

Show casesWind simulation

Research question:• The effect of EWI building on wind pattern in a specific area• The contribution of trees’ to Wind pattern in our campus

Valuable data for research:• 3D geometric surface representations of buildings • 3D geometric surface representations of trees• Ability to extract certain buildings only• Surface attributes• Drag coefficient of canopy (winter and summer)

13

I want to know how EWI matters

Sure …

14

1st: OTB with Trees2nd: OTB without Trees

I want to know how trees

around OTB effect Wind

pattern

15

1st: Trees in Summer2nd: Trees in Winter

ID:12021NAME: ChestnutSPECIES:Castanea_SativaHEIGHT: 3.15NDVI:0.27986900000GEOMETRY: SDO_GEOM

LATIN NAME: Castanea_SativaENGLISH NAME: Sweet ChestnutDUTCH NAME: Tamme kastanjePAI: 2.92WAI: 0.32WAI/PAI: 0.11Compress: 1.6Drag CW: 0.2

I want to know how leaves

matters

16

Show casesUrban Heat Island

Research question:The contribution of green areas and water in our campus.

Valuable data for research:

• Grass areas• Water areas• Green roofs (area of flat roofs)

17

Grass areas, waters and buildings

How grass/water

areas matters?

18

Flat roofs

If I want to have green

roofs?

19

Building a spatio-temporal aware sensor network

Enable seamless (anytime, anywhere) measurement ofurban climate parameters in the university campus

20

MoSCoW diagramSensing requirements

21

Sensor networkSensors for climate research

Stationary and moving platforms

Thermometers

Barometers

Hygrometers

Anemometers and wind vanes

Rain gauges

Disdrometers

Pollution sensors

Human tracking

22

Positioning systemsPositioning techniques

GPS

A-GPSHSGPS

IMESGSM

IR

UWB RFI

D

INSWi-FiBluetoot

h

Ultrasonic

Pseudolites

23

Positioning systemsPositioning technique trade-off

GPS and INS reliable for short time periods, errors with accumulation characteristics

GPS and GSM continuous tracking dependent on the cell tower density and distance to the devices

GPS and IR unique IR and then high accuracy, limited range of IR, sensitivity to sunlight

GPS and Wi-Fi densely deployed access points, ubiquitous hardware with Wi-Fi enabled mobile devices, multipath, signal attenuation due to propagation, NLOS

GPS and Bluetooth like Wi-Fi, limited range and communication speed

24

Positioning systemsPositioning techniques combination

GPS

A-GPSHSGPS

IMESGSM

IR

UWB RFI

D

INSWi-FiBluetoot

h

Ultrasonic

Pseudolites

25

Positioning systemsLimitations- GPS blind spots in the campus

Survey with a U-blox GPS receiver

26

Positioning systemsLimitations- GPS blind spots

27

Positioning systemsLimitations- GPS blind spot map

GPS line-of-sight in OTB

GPS availability in OTB

28

Positioning systemsLimitations- Wi-Fi blind spots in the campus

29

ImplementationAccuracy table OTB survey

30

ImplementationContinuous tracking

• Testing usability of available GPS receivers • Garmin 76CSx• U-blox AEK-4t

• Surveying WiFi network• Cisco Aironet Access Points• Cisco Wireless LAN Controllers

31

ImplementationCombinationAlgorithm

32

ImplementationSensors

• Arduino open-source electronics prototyping platform• Low voltage temperature sensor

• Python to read the measurement from the Arduino

33

ConclusionsAdvantages

• Positioning where no GPS is available due to WiFi positioning

• Easy to extend WiFi positioning to indoor environments

• No location knowledge of Access Points needed

• Continuously track sensors within an urban environment

• Other sensors can be used in combination with the Arduino

• Digital• Analog

• GPS shield is available for the Arduino

34

ConclusionsDisadvantages

• WiFi fingerprinting within the TU Delft campus• Not enough coverage of Access Points for positioning

• WiFi blind spots• Low accuracy

• Access Points are placed in a line, zigzag preferred• Transmit Power Control

• Client sends collected fingerprints over the WiFi network• When Access Points are discovered but no data

connection is available the fingerprint is discarded

35

ConclusionsDisadvantages

• Synchronization of devices is difficult• All components write their data into a

data stream so the python code has to poll this stream to acquire the data

• WiFi fingerprint has to be sent to the Positioning Engine, matched and the location streamed back

36

Building a 3D environment for urban climate research

a centralized information system able tostore and manage the sensed climate data along with 3D representations of the built environment

37

MoSCoW diagramSensing requirements

38

Needed objectsWhich objects to model?

• Tree

• Building

•Roof: grass

•Side wall: glass

•Front wall: concrete

• Terrain

• Sensor: Climatic measurements (temperature, humidity, wind flow, rainfall etc.)

• Landuse

39

Literature studyHow to model these objects and attributes?

DXF:o No Thematic and topology attributes

SHP:o Only simple features and no topology

VRML:o No Thematic attributes

KML:o No Thematic attributes

CityGML:Geometric and thematic modelTexture surface and appearanceMultiple Level Of Details for building, trees and terrainTime measurement

40

Literature studyWhy database?

Advantages of Oracle Spatial Database:o Easy access via different software for different usero Make query of objects and attributeso Easy extension of attributes and objects (add column/table)o Spatial analysis and selection in 3D

File based:o Hard to extract infoo Hard to access by different userso Hard to extend

41

ImplementationLand Use

• Processing & Storage

Topograph

ic data

Water bodyRoads

Buildings

Grassland

Land use2D polygons

• We can…Calculate percentage of area of each land cover type within the campus.Calculate how much area of water is within 10 meters distance of OTB.etc…

42

ImplementationBuildings

• We can…Get a campus without EWI building.Retrieve the reflectivity of roof of Civil Engineeringetc…

• Processing & Storage

Existing CityGML model

3D Multi-surfaces

43

ImplementationSensors• Processing & Storage

• We can…Find the hottest/coldest hour of

a day in a certain location.Compare same measurement

from different times.

3D points

Measurements (location,

temperature, time)

44

ImplementationDigital Terrain Model

• Storage

• We can…Find the most fluctuated ground in the campus.Find the lower ground where water may flow to

after rainfall.etc.

Triangulated point cloud

3D surfaces

45

ImplementationTrees• Storage

• We can…Get the height and species of trees.Give trees with seasonal parameters.

3D multiple-surface

Tree surfacePoint Cloud

Tree parameters

46

Literature studyTrees & Climate

• Trees are important in urban heat mitigation strategies:

Create shadeReduce windspeedCool the environment (evapotranspiration)

Why include trees?

Logo of trees for cities

47

Literature studyTrees & Climate

So which tree parameters are useful for climate research?

• GeometrySize, shape, volume area are related to other climate parameters.

Shade and wind analysis.• Drag Coefficient

Necessary for wind analysis.

• Leaf Area IndexLAI is used to predict photosynthetic primary production and as a

reference tool for crop growth.• Normalized Difference Vegetation Index

NDVI is directly related to the photosynthetic capacity and hence

energy absorption of plant canopies.

48

ImplementationOuter hull reconstruction for LOD2

49

ImplementationLOD1 and LOD2

50

Implementation of LAILeaf Area Index • Determine the size of the

footprint from the acquisition system:

ALS (FLIMAP)

• Calculated the probability echo returns from a tree branch

51

Implementation of Drag Coefficient

Climate parameters & Trees

• Beyond the scope of this project to remotely sense the tree type

• Drag coëfficiënt is mostly dependent on the tree type and season

• Therefor a manual classification is performed and stored in the database

52

Implementation - NDVIClimate parameters & Trees

• NDVI raster map derived from Quickbird

• Computed average NDVI value per tree around OTB

• Stored in the database as a attribute of the tree

53

Literature studyDigital Terrain Model (DTM) & Climate

DTMs allow the study of the impacts of terrain on climate atmeso or micro-scale:

• Watershed / Waterflow analysis• Hydrological modeling • Shade analysis • Wind interaction• Etc

54

Implementation

Created two level of details:

Level of detail 1 (low level) •Campus level

Level of detail 2 (high level)•1. OTB•2. Mekelpark

Two level of details (LOD)

55

ImplementationExample of DTM around OTB

• Constraints:• Buildings are not empty• DTM and buildings do not connect perfectly• Random filtering instead of an algorithm that fixes the relevant vertices

56

ConclusionsAdvantages

• The Synthesis project has developed a 3D framework which is able to store representations of the urban environment and measurements made therein

• The 3D framework stores geometries and relevant attributes of all in several level of details:• Buildings• Trees• Terrain • Land use

• CityGML has been extended to support climate research

• The 3D framework is able to store measurements performed by static and mobile platforms

57

ConclusionsDisadvantages

• Custom software has to be written to handle these extensions• Exporting to CityGML is difficult• More parameters could have been extracted, due

to lack of time, scarcity and complexity of extraction algorithms this is limited

58

Conclusions

• CityGML is a suitable model for climate research • The ID increases continuously • Not all the tables are created (point, line) • The place for properties of surfaces … materialattrib?• The exporter does not consider new attributes (and classes) • There is no triggers (the user is responsible for the records in the

database)

CityGML database

59

Conclusion

Framework for climate research:

Implement more positioning techniques to improve the performance of continuously tracking mobile platforms.

Create interface for future users to easily input new features and extract data from spatial database.

The accessibility to the framework could be improved by using the web and mobile networks.

Future work

60

THANK YOU

Special thanks to:Our tutorsThe involved actors and companies