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Geomatics Synthesis Project. Final presentation November 24 th 2010. Supervisors: Edward Verbree, Cristiaan Tiberius, Ben Gorte, Sisi Zlatanova. Group members: Ye, Daniel, Martin, Marjolein, Bas, Simeon, Hoe-Ming, Sonia, Stratos, Amir, YiJing, Tom. Who are we and why are we here?.

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Geomatics Synthesis Project

Final presentation

November 24th 2010

Supervisors: Edward Verbree, Cristiaan Tiberius, Ben Gorte, Sisi Zlatanova

Group members: Ye, Daniel, Martin, Marjolein, Bas, Simeon, Hoe-Ming, Sonia, Stratos, Amir, YiJing, Tom



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“


Overview
Overview

  • Urban Climate research

  • The tools to do research on Campus Climate

  • Showcases

  • Building a spatial-temporal sensor system

  • Building a 3D environment for climate research

  • Conclusions and future work


Climate

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


Urban Climate

What is (urban) climate?

Depends on:

  • size

  • location

  • activities

  • stage of development

Example: the energy balance depends on

changes of chemical content of the area, CO2 emmision of cars, changes reflection (energy fluxes) of landcover, etc.



Climate parameters
Climate parameters

Static

Surface properties

Buildings

Vegetation

Water bodies

Roof types

Building facade

Dynamic

  • Temperature

  • Wind

  • Precipitation

  • Ground Absorption

  • Air humidity

  • Air pollution

  • Soil Contamination

  • Energy Consumption

  • People behaviour


The tools for climate research

a centralized information system able to

store and manage the sensed climate data along with 3D representations of the built environment

a method is developed which enables seamless (anytime, anywhere) measurement of

urban climate parameters


The tools for climate research

Static climate parameters by modeling city objects and their properties

Dynamic climate parameters measured by sensors

Spatial Database

Get data by query

Spatial Database


Show cases

Urban Planning

  • Research Techniques:

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

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


Show cases

Wind 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)



I want to know how trees around OTB effect Wind pattern

1st: OTB with Trees

2nd: OTB without Trees


I want to know how leaves matters

ID:12021

NAME: Chestnut

SPECIES:Castanea_Sativa

HEIGHT: 3.15

NDVI:0.27986900000

GEOMETRY: SDO_GEOM

LATIN NAME: Castanea_Sativa

ENGLISH NAME: Sweet Chestnut

DUTCH NAME: Tamme kastanje

PAI: 2.92

WAI: 0.32WAI/PAI: 0.11

Compress: 1.6

Drag CW: 0.2

1st: Trees in Summer

2nd: Trees in Winter


Show cases

Urban 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)


How grass/water areas matters?

Grass areas, waters and buildings



Building a spatio-temporal aware sensor network

Enable seamless (anytime, anywhere) measurement of

urban climate parameters in the university campus


Sensing requirements

MoSCoW diagram


Sensor network

Sensors for climate research

  • Stationary and moving platforms

  • Thermometers

  • Barometers

  • Hygrometers

  • Anemometers and wind vanes

  • Rain gauges

  • Disdrometers

  • Pollution sensors

  • Human tracking


Positioning systems

Positioning techniques

Wi-Fi

INS

Bluetooth

IR

UWB

HSGPS

A-GPS

RFID

Ultrasonic

IMES

GPS

GSM

Pseudolites


Positioning systems

  • 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

Positioning technique trade-off


Positioning systems

Positioning techniques combination

Wi-Fi

INS

Bluetooth

IR

UWB

HSGPS

A-GPS

RFID

Ultrasonic

IMES

GPS

GSM

Pseudolites


Positioning systems

Limitations- GPS blind spots in the campus

Survey with a U-blox GPS receiver


Positioning systems

Limitations- GPS blind spots


Positioning systems

Limitations- GPS blind spot map

GPS line-of-sight in OTB

GPS availability in OTB


Positioning systems

Limitations- Wi-Fi blind spots in the campus


Implementation

Accuracy table OTB survey


Implementation

Continuous tracking

  • Testing usability of available GPS receivers

    • Garmin 76CSx

    • U-blox AEK-4t

  • Surveying WiFi network

    • Cisco Aironet Access Points

    • Cisco Wireless LAN Controllers


Implementation

CombinationAlgorithm


Implementation

Sensors

  • Arduino open-source electronics prototyping platform

  • Low voltage temperature sensor

  • Python to read the measurement from the Arduino


Conclusions

Advantages

  • 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


Conclusions

Disadvantages

  • 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


Conclusions

Disadvantages

  • 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


Building a 3D environment for urban climate research

a centralized information system able to

store and manage the sensed climate data along with 3D representations of the built environment


Sensing requirements

MoSCoW diagram


Needed objects

  • Front wall: concrete

Which objects to model?

  • Roof: grass

  • Building

  • Side wall: glass

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

  • Tree

  • Landuse

  • Terrain


Literature study

How to model these objects and attributes?

  • DXF:

  • No Thematic and topology attributes

  • SHP:

  • Only simple features and no topology

  • VRML:

  • No Thematic attributes

  • KML:

  • No Thematic attributes

  • CityGML:

  • Geometric and thematic model

  • Texture surface and appearance

  • Multiple Level Of Details for building, trees and terrain

  • Time measurement


Literature study

Why database?

  • File based:

  • Hard to extract info

  • Hard to access by different users

  • Hard to extend

  • Advantages of Oracle Spatial Database:

  • Easy access via different software for different user

  • Make query of objects and attributes

  • Easy extension of attributes and objects (add column/table)

  • Spatial analysis and selection in 3D


Implementation

Land Use

  • Processing & Storage

Water body

Topographic data

Land use

Roads

2D polygons

Buildings

Grassland

  • 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…


Implementation

Buildings

  • Processing & Storage

Existing CityGML model

3D Multi-surfaces

  • We can…

  • Get a campus without EWI building.

  • Retrieve the reflectivity of roof of Civil Engineering

  • etc…


Implementation

Sensors

  • Processing & Storage

  • We can…

  • Find the hottest/coldest hour of a day in a certain location.

  • Compare same measurement from different times.

Measurements (location, temperature, time)

3D points


Implementation

Digital 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


Implementation

Trees

  • Storage

  • We can…

  • Get the height and species of trees.

  • Give trees with seasonal parameters.

Tree surface

Point Cloud

3D multiple-surface

Tree parameters


Literature study

Trees & Climate

Why include trees?

  • Trees are important in urban heat mitigation strategies:

    • Create shade

    • Reduce windspeed

    • Cool the environment (evapotranspiration)

Logo of trees for cities


Literature study

  • So which tree parameters are useful for climate research?

  • Geometry

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

  • Shade and wind analysis.

  • Drag Coefficient

  • Necessary for wind analysis.

  • Leaf Area Index

  • LAI 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.

Trees & Climate


Implementation

Outer hull reconstruction for LOD2


Implementation

LOD1 and LOD2


Implementation of LAI

Leaf Area Index

  • Determine the size of the footprint from the acquisition system:

  • ALS (FLIMAP)

  • Calculated the probability echo returns from a tree branch


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


Implementation - NDVI

Climate 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


Literature study

Digital Terrain Model (DTM) & Climate

  • DTMs allow the study of the impacts of terrain on climate at

  • meso or micro-scale:

  • Watershed / Waterflow analysis

  • Hydrological modeling

  • Shade analysis

  • Wind interaction

  • Etc


Implementation

Two level of details (LOD)

  • Createdtwo level of details:

  • Level of detail 1 (low level)

  • Campus level

  • Level of detail 2 (high level)

  • 1. OTB

  • 2. Mekelpark


Implementation

Example 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


Conclusions

Advantages

  • 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


Conclusions

Disadvantages

  • 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


Conclusions

CityGML database

  • 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)


Conclusion

Future work

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.


Thank you

Special thanks to:

Our tutors

The involved actors and companies

Thankyou


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