• Home
  • About

GIScience News Blog

News of Heidelberg University’s GIScience Research Group.

Feed on
Posts
Comments
« Join our final “SocialMedia2Traffic” project presentation on March 22
New paper on “improving change analysis with full temporal information” »

Where to map in OpenStreetMap next? Experiences from Mozambique, India, and Tonga

Mar 8th, 2022 by Hao Li

OpenStreetMap (OSM) has been intensively used to support humanitarian aid activities, especially in the Global South. Its data availability in the Global South has been greatly improved via recent humanitarian mapping campaigns and due to the efforts of local communities. However, large rural areas are still incompletely mapped. The timely provision of map data is often essential for the work of humanitarian actors in the case of disaster preparation or disaster response. In this context, volunteers or humanitarian organizations often ask where do we need to map in OpenStreetMap next?

To answer such questions, we recently developed the Missing OSM Maps service to identify OSM missing built-up areas within countries that recently suffered from natural disasters (e.g., flood, tsunami, volcano eruption, etc.). The objective is to establish an end-to-end framework for fast and accurate unmapped human settlements localization and detection by integrating heterogeneous geospatial datasets, such as volunteered geographical information (VGI), social media (geotagged tweets), and remote sensing (RS)

Study areas of Mozambique, India, and Tonga

Figure 1: Study areas of Mozambique, India, and Tonga

satellite images.

Currently, the processed study areas range from Mozambique (hit by Cyclone Idai in 2019), and India (hit by Cyclone Fani in 2019), to the Kingdom of Tonga (affected by Hunga Tonga eruption in 2022), See Figure 1. The Missing OSM Maps provides an intuitive way of exploring OSM missing built-up area, which consists of mainly 4 steps (Figure 2):

  • Step1: What is already in OpenStreetMap? - First, users can check how many features, specifically buildings, have been mapped by OSM volunteers by setting Satellite Opacity and DeepVGI Opacity both to zero.
  • Step2: What can we see from remote sensing satellite images? - Next, users can further check whether there are real human settlements over the study areas. In order to look into the satellite images, you might set Satellite Opacity to one, and leave DeepVGI Opacity as zero.
  • Step3: What can we get from DeepVGI built-up estimation from RS? - In this step, we demonstrate the DeepVGI automatic building detection results, which are generated based on our deep learning models together with very high-resolution satellite images. For more detail, you might want to check out our related publication.
  • Step4: Can we even count the number of human settlements from RS? - In addition to the binary estimation of built-up areas, the perdition of the total number of human settlements within each satellite image tile is also available. (still beta version)


Main steps of exploring OSM missing built-up area

Figure 2: Main steps of exploring OSM missing built-up area

In our DeepVGI project, we are interested in combining deep learning technology with crowdsourcing VGI data for real-world problems. The concept and method used to create the Missing OSM Maps were proposed in Herfort et al. 2019 and Li et al. 2019. Moreover, the walk through of “Automatic building detection with ohsome2label and Tensorflow” is openly available on our GIScience GitHub.

In future work, we want to further develop this method and extend this service, with which we aim to support better and faster humanitarian mapping from both a machine-assisted mapping perspective and an OSM data quality assessment perspective. Stay tuned for our future updates and have fun with the Missing OSM Maps!

The Missing OSM Maps service is available here: https://missingosm.geog.uni-heidelberg.de/

Methods used:

  • Herfort, B., Li, H., Fendrich, S., Lautenbach, S., Zipf, A. (2019): Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning. Remote Sensing 11(15), 1799. https://doi.org/10.3390/rs11151799
  • Li, H., Herfort, B., Huang, W., Zia, M., and Zipf, A. (2020): Exploration of OpenStreetMap Missing Built-up Areas using Twitter Hierarchical Clustering and Deep Learning in Mozambique. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2020.05.007

Previous related work:

  • Herfort, B., Lautenbach, S., Porto de Albuquerque, J., Anderson, J., Zipf, A. (2021): The evolution of humanitarian mapping within the OpenStreetMap community. Scientific Reports 11, 3037 (2021).
    DOI: 10.1038/s41598-021-82404-z
  • Pisl, J., Li, H., Lautenbach, S., Herfort, B., and Zipf, A. (2021): Detecting OpenStreetMap missing buildings by transferring pre-trained deep neural networks, AGILE GIScience Ser., 2, 39, https://doi.org/10.5194/agile-giss-2-39-2021.
  • Li, H., Herfort, B., Zipf, A. (2019): Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks. Proceedings of the 22nd AGILE Conference on Geographic Information Science, Limassol, Cyprus.
  • Zipf, A, Chen, J. (2017): Humanitarian Mapping with Deep Learning and Volunteered Geographic Information. BIG 2017 (BigData Innovators Gathering). Perth. co-located with WWW2017.
  • Chen, J., Y. Zhou, A. Zipf and H. Fan (2018): Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping. IEEE Transactions on Geoscience and Remote Sensing (TGRS). 1-10. https://doi.org/10.1109/TGRS.2018.2868748
  • Scholz, S., Knight, P., Eckle, M., Marx, S., Zipf, A. (2018): Volunteered Geographic Information for Disaster Risk Reduction: The Missing Maps Approach and Its Potential within the Red Cross and Red Crescent Movement. Remote Sens., 10(8), 1239, doi: 10.3390/rs10081239.
  • Barron, C., Neis, P. & Zipf, A. (2013): A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis. Transactions in GIS, DOI: 10.1111/tgis.12073.

Tags: deep learning, deepVGI, disaster, humanitarian mapping, MissingOSM, OpenStreetMap

Posted in Digital Humanities, OSM, Research

Comments are closed.

  • About

    GIScience News Blog
    News of Heidelberg University’s GIScience Research Group.
    There are 1,674 Posts and 0 Comments so far.

  • Meta

    • Log in
    • Entries RSS
    • Comments RSS
    • WordPress.org
  • Recent Posts

    • New paper on the automatic characterization of surface activities from 4D point clouds
    • OSHDB Version 1.0 Has Arrived
    • Job Opening for Postdoc / Senior Researcher on OpenStreetMap Road Quality Analysis
    • Geography Awareness Week 14.-19.11.2022
    • Open Data: Multi-platform point clouds and orthophotos of the inland dune in Sandhausen
  • Tags

    3D 3DGEO Big Spatial Data CAP4Access Citizen Science Conference crisis mapping Crowdsourcing data quality deep learning disaster DisasterMapping GeoNet.MRN GIScience heigit HOT humanitarian humanitarian mapping Humanitarian OpenStreetMap team intrinsic quality analysis landuse laser scanning Lidar machine-learning Mapathon MapSwipe MissingMaps Missing Maps ohsome ohsome example Open data openrouteservice OpenStreetMap OSM OSM History Analytics Public Health Quality quality analysis remote sensing routing social media spatial analysis Teaching VGI Workshop
  • Archives

    • December 2022
    • November 2022
    • October 2022
    • September 2022
    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
    • October 2014
    • September 2014
    • August 2014
    • July 2014
    • June 2014
    • May 2014
    • April 2014
    • March 2014
    • February 2014
    • January 2014
    • December 2013
    • November 2013
    • October 2013
    • September 2013
    • August 2013
    • July 2013
    • June 2013
    • May 2013
    • April 2013
  •  

    March 2022
    M T W T F S S
    « Feb   Apr »
     123456
    78910111213
    14151617181920
    21222324252627
    28293031  
  • Recent Comments

    GIScience News Blog CC by-nc-sa Some Rights Reserved.

    Free WordPress Themes | Fresh WordPress Themes