Crowdsourcing approach for large scale mapping of built-up land
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1 Crowdsourcing approach for large scale mapping of built-up land Kavinda Gunasekara Geoinformatics Center Asian Institute of Technology, Thailand. Regional expert workshop on land accounting for SDG monitoring and reporting 26 Sep 2017
2 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
3 Geoinformatics Center Established in 1999 Over 1,600 Graduate students from 40+ countries 14,000 alumni from 74 countries 22,000 short-term trainees from 71 countries Establish in 1959 as a Post Graduate School Catering for higher education in Asia Over 100 faculty members from 26 countries
4 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.
5 (ongoing)
6
7 Project details Facebook:
8 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 km 2 Population: 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
9 Why? Interpretation on Google Earth Red lines: village boundary Yellow polygons: built-up land
10
11 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
12 Google Earth Pro vs Digital Globe Map API
13 Object Oriented Classification Segmentation
14 Object Oriented Classification Classification Classification for a small area Results are better than a pixel based classification
15 Object Oriented Classification Classification
16 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.
17 Urban Area Preliminary results of object oriented classification (built-up land shows in red)
18 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)
19 Rural Area Yellow polygons: built-up land
20 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
21 Crowdsourcing approach
22 Architecture of crowdsourcing approach 5. Write Digitized data into database and make grid as completed USER 2 USER 3 USER 1 1. Send Remaining Random Grid 2. Get background Bing Map image Internet 4. Send Digitize Data USER n 3. Digitize
23 Number of Grids: Grid size: 1.2km * 0.8 km
24 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.
25 Notes: These buildings are very important to be digitized as those highly vulnerable to flood
26 Notes: Near the river and need to digitize all the building, could digitize as building cluster
27 Notes: Level of this details would be enough
28 Notes: All the buildings near the river and mountainous area need to be digitized
29 Notes: Hope you can understand level of details we expect to be digitized
30 Progress of the work
31 Live demonstration of crowdsourcing tool
32 Monitoring and Quality Control Continuously monitoring the quality of the work and communicating with crowdsourcing people
33
34 Final product
35
36
37 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
38 Pilot project on implementation of data sharing platform in Pacific Island Countries Geo-portal (GEONODE)
39 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:
40 End User (Public) Geoportal (GEONODE) Agency 1 Agency 2 Agency 3 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
41 Example from Tonga Pilot Project
42 Tonga Geoportal Access from
43 3 Types of Data Layers GIS or Satallite Image Data Maps Combination of Layers Documents
44 Data can be easily accessed by, Searching with Keywords By Category
45 Current Data in Tonga Geoportal
46 Other Operational Geo-portals in Pacific Micronesia Fiji Tonga
47 Drones for mapping Building low cost fixed-win drones Low cost drones for mapping Land cover mapping Vegetation health mapping 3D mapping/ 3D buildings
48 Thank you
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