crowdsourcing approach for large scale mapping of built-up …...crowdsourcing approach for large...

Crowdsourcing approach for large scale mapping

of built-up land

Kavinda Gunasekara

Kavinda@ait.asia

Geoinformatics Center

Asian Institute of Technology, Thailand.

Regional expert workshop on land accounting for SDG monitoring and reporting – 26 Sep 2017

Content • Introduction to AIT and Geoinformatics Center

• Project details

• Why we need build-up land

• Image classification approach

• Crowdsourcing approach

• Monitoring and Quality Control

• Project on data sharing platform in Pacific Island countries, support by UNESCAP and implementation by AIT

• Drones for mapping

Establish in 1959 as a Post Graduate School

Catering for higher education in Asia

• Over 1,600 Graduate students from 40+ countries • 14,000 alumni from 74 countries • 22,000 short-term trainees from 71 countries • Over 100 faculty members from 26 countries

Geoinformatics Center Established in 1999

Activities of the Geoinformatics Center - AIT

• Projects and Consulting Works

• Capacity building Programs, primarily in Asia and the Pacific

• QZSS GPS Monitoring Station and GNSS Research

• Emergency Disaster Response Mapping

• Rapid Mapping Support for Sentinel Asia & IDC

• Applied Research (DRR, Poverty, Environment, etc.)

• Exchange Programs: Students, Researchers, Experts

• Information Sharing and Publications: Journal, Conference, Reports, Manuals etc.

(ongoing)

http://arcg.is/2r9Lw5m

Project details

http://www.uttarakhand-dra.in/ Facebook: https://www.facebook.com/UttarakhandDRA/

Why we need built-up land

• Multi hazards (Flood, Earthquake, Landslide and Industrial) risk assessment project in Uttarakhand state • Size of Uttarakhand state: 53,483 km2

• Population: 10.08 million

• Location and extent of built-up land is essential for the risk assessment task

• Extent of built-up land is needed below the village level

• All the censes data at village level

Why? Interpretation on Google Earth

Red lines: village boundary Yellow polygons: built-up land

Image Classification Approach

• Tested methods a. Normalized Built-up Area Index (NBAI)

b. Pixel-based classification

c. Object oriented classification This methods gave best results and further extended

Tested on Google Earth images

Tested on Digital Globe Map API

• Limitations • High resolution image coverage over the state

• Commercial satellite images/budget

Google Earth Pro vs Digital Globe Map API

Object Oriented Classification – Segmentation

Object Oriented Classification – Classification

• Classification for a small area

• Results are better than a pixel based classification

Object Oriented Classification – Classification

• We can use object attributes to refine the classification

• E.g. Differentiate between roads and buildings using their length/width ratio.

Object Oriented Classification – Classification

Preliminary results of object oriented classification (built-up land shows in red)

Urban Area

(a) (b)

Use of object attributes to refine the classification (Yellow represents the built-up land) (a) Supervised classification (b) Refined classification using object attributes (e.g. width:length ratio)

Urban Area

Rural Area

Yellow polygons: built-up land

Summary of Object Oriented image classification

• Small area was able to classify successfully

• Batch processing was not able to perform for larger area

• Rural area classification was not successful

• High cost of high resolution images

Crowdsourcing approach

Architecture of crowdsourcing approach

Internet

1. Send Remaining Random Grid

3. Digitize

2. Get background Bing Map image

4. Send Digitize Data

5. Write Digitized data into database and make grid as

completed

USER 1

USER 2

USER 3

USER n

Number of Grids: 75563 Grid size: 1.2km * 0.8 km

Crowdsourcing approach

• Source and details • High Resolution Satellite Product – Bing Map (free) • Scale – 1 : 20,000 • Image Acquisition Dates – 2010 to 2015

• Training and quality control • Before Operational Stage - Each GIS Digitizers were trained before start the work to

enhance their ability to interpret high resolution Satellite Data • Operational Stage – Quality of digitizing work was randomly assessed daily basis and

guide GIS Digitizers improve their quality of work • After Operational Stage – After finishing all the grids, each grid was assessed by GIS

expert and identified around 12 % of the grids which haven’t done properly. And those grids were removed from the products and re-digitized by the best GIS Digitizers.

Notes: These buildings are very important to be digitized as those highly vulnerable to flood

Notes: Near the river and need to digitize all the building, could digitize as building cluster

Notes: Level of this details would be enough

Notes: All the buildings near the river and mountainous area need to be digitized

Notes: Hope you can understand level of details we expect to be digitized

Progress of the work

Live demonstration of crowdsourcing tool

• http://www.geoinfo.ait.ac.th/ukd/

Monitoring and Quality Control

Continuously monitoring the quality of the work and communicating with crowdsourcing people

Final product

Summary of crowdsourcing approach

• 8 non-GIS undergraduates utilized for the interpretation

• 4 GIS experts worked on the initial interpretation

• Digitization of whole state was completed within 3 weeks

• 4 GIS experts used for accuracy assessment and refined the errors within 2 weeks

Pilot project on implementation of data sharing platform in

Pacific Island Countries

Geo-portal (GEONODE)

Geo-portal is a centralized platform to share all the spatial data

• Open Data with Public

and

• Restricted data only among the Agencies

GEONODE is one such open source solution for a Geoportal

Image Source: https://e3geoportal.ecdc.europa.eu/E3%20Images/E3Geoportal_home.png

End User (Public)

Agency 1 Agency 2 Agency 3

Geoportal (GEONODE)

• Mainly Intended to Facilitate one way data flow from Data Providers to Public (End User) • Additionally, restricted data can be shared among only intended Agencies

Example from Tonga Pilot Project

Tonga Geoportal

• Access from http://202.134.

25.30

3 Types of Data

• Layers – GIS or Satallite Image Data • Maps – Combination

of Layers • Documents

Data can be easily accessed

by,

• Searching with Key-

words • By Category

Current Data in Tonga Geoportal

Other Operational Geo-portals in Pacific

• Micronesia http://www.geoportal.oeem.gov.fm/

• Fiji http://www.fijigeoportal.gov.fj/

• Tonga http://202.134.25.30

Drones for mapping • Building low cost fixed-win drones

• Low cost drones for mapping

• Land cover mapping

• Vegetation health mapping

• 3D mapping/ 3D buildings

Thank you