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Citizen science, vgi, geo crowd sourcing, big geo data how they matter to the foss4g community

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Key note speech at FOSS4G 2015 - Seoul - 18/9/2015

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Citizen science, vgi, geo crowd sourcing, big geo data how they matter to the foss4g community

  1. 1. Citizen Science, VGI, Geo- CrowdSourcing, Big Geo Data: how they matter to the FOSS4G Community Maria Antonia Brovelli Politecnico di Milano – Como Campus Dept. of Civil and Environmental Engineering (DICA) - Italy K Seoul Hotel Grand Ballroom B 18 September 2015 POLITECNICO DI MILANO GEOlab - COMO Campus
  2. 2. 2Citizen science Citizen science: scientific work undertaken by members of the general public, often in collaboration with or under the direction of professional scientists and scientific institutions. June 2014 It is a fairly new name but an old practice
  3. 3. 3Volunteered Geographic Information The name was coined in 2007 (Goodchild), but it was already a real practice. Geopaparazzi. Because not all paparazzis are evil!
  4. 4. 4 OSM Streets and Buildings Milan and Seoul 2006-2015 Volunteered Geographic Information
  5. 5. 5OpenStreetMap at FOSS4G Europe 2015 Peter Mooney , Luca Delucchi and Marco Minghini
  6. 6. 6Checking the quality Comparison of OpenStreetMap and authoritative road network datasets
  7. 7. 7Indoor Mapping at FOSS4G Europe Nicola Dorigatti and Ludovico Biagi
  8. 8. 8Emotional Maps: participatory sensing How do we feel the city? Photo by Thomas Leuthard Haosheng Huang
  9. 9. 9Emotional Mapping at FOSS4G Europe 2015 Haosheng Huang and Eleonora Ciceri Available at Google Play or Apple Store: emomap Download the code:
  10. 10. 10Other participatory sensing applications Architectural barriersArchitectural barriers Cultural elementsCultural elements Street furnitureStreet furniture BiodiversityBiodiversity
  11. 11. 11
  12. 12. 12OSAKA Bike Parking Report Available at Google Play : ODKCollect To connect ODK Collect to the ODK Aggregate server: • hit the Options device button, then select General Settings • select Configure platform settings and insert: – URL: – Username: maria - Password: osaka2015
  13. 13. 132D viewer
  14. 14. 14Policrowd2.0 Osaka
  15. 15. 15Architecture (Policrowd2.0)
  16. 16. 16Volunteer thinking Vijay Charan Venkatachalam and Irene Celino DUSAF (Lombardy)
  17. 17. 17 1. Look at the pixel within the blue border 2. For that pixel, choose the most suitable land cover category 3. Watch the time! 4. Win scores and badges and beat your friends!
  18. 18. 18Geocrowdsourced information
  20. 20. 20Geo Big Data: Satellites
  21. 21. 21 Sensors are everywhere and they are the electronic skin of the Earth Geo Big Data: Sensors Citizens as sensors
  22. 22. 22Example 1: EarthServer  Agile Analytics on 1+ Petabyte space/time datacubes • Earth Science (3D sat image timeseries, 4D weather); Planetary Science  Open standards, open source • OGC WCS + WCPS, integrated data/ metadata search • rasdaman + NASA WorldWind + more  Intercontinental initiative: EU+US+AUS  EarthServer: Datacubes at Your Fingertips Peter Baumann & al, Jacobs Univ. Bremen -Germany
  23. 23. 23Example 2: support for massive datasets in GRASS Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature: Data from 2000-today 4 Earth coverages per day Processing of 17,000 maps of 415 million pixels each (250 m size) ● In total 300 nodes with 600 Gb RAM ● 132 TB of raw disk space, XFS, GlusterFS ● Circa 2 Tflops/s ● Scientific Linux operating system, blades headless ● Queue system for job management (Grid Engine), used for GRASS jobs ● Computational time for all data: 1 month with LST-algorithm V2.0 ● Computational time for one LST day: 3 hours on 2 nodes Markus Neteler & al, FEM-Italy
  24. 24. 24Geo Big Data: social media 9,890 Tweets sent in 1 second 2,528 Instagram photos uploaded in 1 second 2,153 Tumblr posts in 1 second 1,843 Skype calls in 1 second 29,290 GB of Internet traffic in 1 second 50,232 Google searches in 1 second 106,299 YouTube videos viewed in 1 second 2,420,172 Emails sent in 1 second
  25. 25. 25Geo Big Data: the example of Milano GRID ● Two months of data, with a temporal step of 10 minutes ● Grid of 100 x 100 cells with size = 235 m
  26. 26. 26 ● Received SMS: a Call Detail Record (CDR) is generated each time a user receives an SMS ● Sent SMS: a CDR is generated each time a user sends an SMS ● Incoming Calls: a CDR is generated each time a user receives a call ● Outgoing Calls: CDR is generated each time a user issues a call ● Internet connections: a CDR is generate each time – a user starts an internet connection – a user ends an internet connection – during the same connection one of the following limits is reached:​ • 15 minutes from the last generated CDR • 5 MB from the last generated CDR ● Geolocalized Tweets (Anonymized twitter users) Geo Big Data: Milano GRID
  27. 27. 27Sensing the City - 1
  28. 28. 28Sensing the City - 1 Students: Emanuele Mariani, Jacopo Mossina; Supervisors: Giorgio Zamboni, Maria A Brovelli
  29. 29. 29Sensing the City - 2 Student: Anna Trofimova Supervisors: Carolina Arias, Maria A Brovelli
  30. 30. 30Sensing the City - 2 Filtering with date and land coverage classes
  31. 31. 31Sensing the City - 3 NETCDF Carolina Arias, Maria A Brovelli, Simone Corti, Giorgio Zamboni EST-WA
  32. 32. 32GeoForAll Geocrowd Geocrowdsourcing CitizenScience FOSS4G Peter Mooney & Maria A Brovelli
  33. 33. 33 Thanks to all people of my team contributing on these topics: Carolina Arias, Ludovico Biagi, Marco Brambilla, Eleonora Ciceri, Simone Corti, Eylul Candan Kilsedar, Emanuele Mariani, Marco Minghini, Monia Molinari, Jacopo Mossina,Gabriele Prestifilippo, Anna Trofimova, Vijay Charan Venkatachalam, Giorgio Zamboni Thanks to Peter Baumann, Irene Celino, Luca De Lucchi, Nicola Dorigatti, Haosheng Huang, Hayashi Hirofumi, Yoshida Daisuke, Peter Mooney, Markus Neteler, Venkathes Raghavan Thanks for your attention!
  34. 34. 34 현지 조직위원회 에게 감사 당신은 중대하다 !