1. Crowd Sourcing, OpenStreetMap and the Namibia Flood SensorWeb Dan Mandl NASA/Goddard Space Flight Center

2. OpenStreetMap and Crowd Sourcing OpenStreetMap – Collaborative project to create free editable map of world – Driving force Restrictions on use or availability of map info across world Availability of low cost GPS devices – Free map clients – Free editing tools (Java OpenSteetMap (JOSM), Potlatch) – Free database software (PostGRES, PostGIS and Osmosis) Crowd Sourcing [from Wikipedia] – Process that involves outsourcing tasks to a distributed group of people. – Process can occur both online and offline. – Different from an ordinary outsourcing since it is a task or problem that is outsourced to an undefined public rather than a specific body. – In this case, use people to gather Global Positioning System (GPS) locations of water and collate information 2

3. 30% End Goal! Use historical Moderate Resolution Imaging Spectroradiometer (MODIS), Radarsat, and Earth Observing-1 (EO-1) Water Level Maps to relate Hydrologic Model Streamflow to Spatial Extent of Flooding. Streamflow time 90% 70% 30% River

4. 2010 Initial Experiment to Correlate Remotely Sensed Rain Upsteam with Flood Wave Downstream 4 Note blue bars (TRMM data) indicating a surge of rainfall upstream Then a flood wave appears downstream at Rundu river gauge days later (gauge data) Flood Dashboard Zambezi basin consisting of upper, middle and lower catchments Early CREST Model trying to predict flood wave (Green) Riverwatch Model trying to predict flood wave (Orange) Better than CREST based on AMSR-E

5. Early Look at Inundation Extent Related to River Height Envisat swath Radarsat Data March 25, 2009 EO-1 Data March 2009 Envisat Data March 2009 Zambia water lines from old database Lower Zambezi catchment Multiyear river gauge measurements 5

6. Unosat Flood Time Sequence In March 2009 in Caprivi when Katima Mulilo River Gauge Above 7.5 M Vision is to generate similar product automatically when floods predicted and pair them with river gauge measurements 6

7. Vision Based on Preliminary Experiments Use TRMM or other satellites to estimate rainfall in Angola and correlate satellite detection with resulting river gauge height increase (typically 5 – 10 days later) Use satellite rainfall forecasts to possibly provide more advanced warning Correlate historical river gauge heights with resultant flood inundation – Find river gauge level that results in flood event – Use historical satellite imagery to determine where water will go once over river banks 7

8. Challenges Accuracy of remotely sensed Tropical Rainfall Measuring Mission (TRMM) rainfall estimates Calibrating the Coupled Routing and Excess Storage (CREST) distributed hydrological or equivalent model without extensive rain gauge data, rain fall gauge data, Digital Elevation Model (DEM) data and soil maps Even less accuracy for predicted rainfall estimates Accurate determination of water locations when remotely sensed by satellites – How to calibrate and validate – How to collect, store and correlate ground GPS data with satellite classified imagery – How to work with questionable areas like heavily grassy areas Floods do not stop at the grass but satellites often don’t see water within grass and trees 8

9. Approach 9

10. Upstream: TRMM Rainfall Forecast 10

11. Upstream: TRMM Precipitation Accumulation Calculator (PAC) User Defined Shapefiles 11

12. Waterpedia Diagram Version 1 1/7/13 Postgres Database (Joyent) NRT Satellit e Data Planet OSM Baseline Water Database Flood Map Processor Radarsat.Geobliki. Com (Joyent) Water extent and Flood Maps (KML tiles, MB tiles, OSM tiles) Baseline Water Layer National Hydrology Agency Server W/ JOSM National Postgres Database Annotated High water reference Low water reference Flood Map Enhanced Water Reference and Flood Maps Mobile Platform Track Collector For Field Data Automated Time Stamp GPS Stamp for shoreline (Lat, Long, Altitude) User Name/ User Type Edit polygons: Drought OR Low Normal OR High Normal OR Not flooded: misclassified as flooded Flooded: misclassification due to deep slope reflection OR Flooded: misclassification due to dry flat surface OR Flooded: misclassification due to vegetation cover OR Flooded: misclassification due to other Enter Notes Input data source (field measurement/ other: describe) Land Cover Type text Regional Hydrology SME Server W/ JOSM publish corrected map publish NRT map Proposed OpenStreetMap Cal/Val Process

13. Conducted Preliminary OpenStreetMap (OSM) Crowd-Sourced 2 Day Exercise Traveled to Rundu area – 12 people from Namibia Hydrological Services – 3 people from NASA – 1 person Univ. of Oklahoma – 2 boats – 1 helicopter – GPS, camera with GPS Measured riverbank with GPS in Rundu area corresponding to Radarsat images overlaid with EO-1 images Ingested GPS ground points, Radarsat images for Rundu and Divindu area Conducted exercise to gather data multiple ways, ground, boat and helicopter Combined in OSM to make preliminary assessment 13

14. OpenStreetMap Ground Validation Exercise at Rundu With Radarsat Detected Water and GPS Ground Truth 1-31-13 14

15. OpenStreetMap With Radarsat Detected Water and Global Positioning System (GPS) Ground Truth 1-31-13 Zoomed with Corresponding Helicopter GPS Photo 15 Validation result on 1-31-13: Note that Radarsat water detection off based on GPS and Bing. Need to fix Radarsat processing. Probably projection error. (Also need to mark grassy area in database)

16. Another Example Near Divindu with Radarsat Data Versus Planet.osm Data in Red In this example, Radarsat is more accurate then existing data in Planet.osm 16

17. EO-1 Water Edge Detection Radarsat Water Edge Detection (yellow polygon) Team 1 walking bank to collect GPS point s (red X’s) Team 2 walking bank to collect GPS point s (green X’s) One of 500 GPS photos from helicopter Integrated Water Edge Detection Display with Walking GPS Measurements, GPS located photos, Radarsat/EO-1 water edge detections

18. Integrated Water Edge Detection Display with Boat GPS Measurements, GPS located photos, Radarsat/EO-1 water edge detections Boat Team 1 track walking bank to collect GPS point s (purple track) Boat Team 2 track walking bank to collect GPS points (orange track with numbered waypoints) Radarsat Water Edge Detection (yellow polygon) EO-1 Water Edge Detection

19. GPS photos Overlaid on Boat Track Boat Team 1 track with overlaid GPS photos (green track)

20. GPS Photo Shows Terrain Type Overlaid on Boat Track (geotagged elephants!) Boat Team 1 track with overlaid GPS photos (green track) with photo overlay showing land terrain

21. 6/14/2016 GPS Photo Shows Terrain Type Overlaid on Boat Track and Radarsat/EO-1 Water Detection

22. Vision Maps of existing water and flood waters increase in accuracy and detail over time due to many crowd sourced data sources Map is edited and reloaded to database Data is formatted in structured manner so that it can be retrieved by tags (e.g source, high mark, questionable area Use database to develop more accurate water locations which then can feed into models Models evolve over time to increase accuracy Don’t need to gather all data at once, proceed as manpower and resources available 22

23. Conclusion Integrating OpenStreetMap, crowd sourcing and various satellite and ground based data provides a mean of organizing flood related data to more easily mine for salient information for decision support The exercise conducted in January 2013 between Namibia Hydrological Services and NASA was used to familiarize both teams with the process and examine possible future directions Preliminary results demonstrated that at a minimum, using this approach would provide a measure of how good the data is relative to reality 23