The needs on big data management for Operational Geo-Info Services: Emergency Response, Maritime surveillance, Agriculture Management

Transcription

1 Copernicus Big-Data Workshop 2014, 13/14 March The needs on big data management for Operational Geo-Info Services: Emergency Response, Maritime surveillance, Agriculture Management Marco Corsi e-geos 1

2 Introduction Emergency management Maritime surveillance Agriculture management Conclusions 2

3 INTRODUCTION A digital world with continuous real-time updates from different sources (social media, remote sensing, on-line resources etc.) Organize and process data to help users making better decisions on various domains: Emergency management, Maritime Safety, Agricultural planning 3

4 EMERGENCY e-geos Emergency Mapping Service is made of hardware and software resources human resources (25 engineers on shift) procedures devoted to a rapid and effective response to emergency requests exploiting Optical and SAR satellite information. The Service is available 24/7/365 e-geos Emergency Management Center Emergency Activities: Satellite tasking Image analysis and map production News monitoring to anticipate sat. tasking Procedures review and training Maintenance of reference geodatabase 4

5 Floods Earthquakes eference Maps re event situation elivery time: 6 hours after activation Humanitarian crisis amage Assessment Maps ost event situation elivery time: 3 hours after EO data avail. Fires Oil spill / Ship detection 5

6 EMERGENCY DATA Quick access to available Data for pre event situation maps Catalogues and data harmonization is a key task Standard symbolysm and data structure Data refinement and gap filling Well trained staff available on shift 24/7 is a key asset Quick access to crisis data for post event situation maps Mainly satellite data suitable for the specific type of disaster Other data (including social data): complementing the crisis mapping from satellite data 6

7 EMERGENCY DATA e-geos Geodata Toolkit It is the e-geos solution that provides a simple single entry point to access harmonized heterogeneous geospatial data resources with local or global coverage to be further used in other applications. e-geos Geodata Toolkit can be used to quickly retrieve over a specific AOI: base vector layers (OSM, Geonames, GADM, GAUL, Wikimapia, ) Such heterogeneous data are harmonized in the data model to be immediately ready to use Frequently changing sources (e.g. OSM) are regularly updated in the local copy satellite imagery (MODIS, Landsat, national orthomosaics) Accessed both as webservices and as physical raster files elevation data (SRTM90, ASTER, EUDEM) weather forecasts (OWM) other data that have a spatial component (CSK catalogue, Flickr, ) e-geos Geodata Toolkit is fully open source (Postgres/PostGIS, Python, GDAL) and it is designed to fit distributed access to resources (Master node + Slave nodes). 7

8 Very detailed Base maps can be generated in less than 30 minutes using Open Geodata only thanks to: - Single point access to all resources needed - No time wasted in accessing different resources from different entry points - Harmonized data model with unique representation of features - Availability of standard representation rules (symbolysm) 8

9 Activation Oklahoma City Tornado Event description: On the afternoon of May 20, a large, violent tornado touched down west of Newcastle, Oklahoma and impacted the town of Moore, causing severe damage to residential areas as well as Plaza Towers and Briarwood Elementary schools. The Oklahoma Office of the Chief Medical Examiner has confirmed several fatalities, at least 200 people injured. Triggering entity: e-geos Autoactivation e-geos has been proactively acquiring COSMO-SkyMed images befor and after the event, genearting damage assessment maps provided to US Government. e-geos has been regularly acquiring also optical data for optical-sar combined analysis. 9

10 Damage assessment map generated less 24 hours after the event by analyzing and georeferencing information from open data (tweets, video, ) 10

11 Damage assessment map generated based on WorldView-1 image acquired on May 22nd,

12 EMERGENCY BIG DATA NEEDS Operational use of social network and crowdsourcing (videos, cameras, VGI etc.) Increase the number of sources of open-data and open catalogues Sustain with new technologies the data growth 12

13 MARITIME Ship Detection Map and Report showing information on: number, position, velocity, type of detected ships correlation with AIS or sat-ais, VMS, LRIT (if available) meteo data plus wind and wave fields extracted by SAR images COSMO-SkyMed ASI 13

14 MARITIME Oil Spill Map and Report showing information on: slick location number of detected slicks in the image oil slick confidence assessment Optional Global Wind and Wave information COSMO-SkyMed 2 StripMap HIMAGE - 5m resol. Ascending Orbit, Left Looking, VV Pol. 25 November 2011, Campos Basin, Brazil COSMO-SkyMed ASI 14

15 MARITIME DATA NRT requirement (about 30 min after acquisition) VMS sources for correlation Multiple acquisitions of SAR/Optical imagery 15

16 MARITIME CSK1 HI Data 30/12/ :04:00 16

17 MARITIME CSK1 HI Data 30/12/ :04:00 CSK1 HI Ship Detection Report (20) 17

18 MARITIME CSK1 HI Data 30/12/ :04:00 CSK1 HI Ship Detection Report (20) AIS Data 30/12/ :00-06:00 18

19 MARITIME CSK1 HI Data 30/12/ :04:00 CSK1 HI Ship Detection Report (20) AIS Data 30/12/ :00-06:00 VMS Data 30/12/ :00-06:00 VMS extrapolated at CSK1 Time 19

20 MARITIME CSK1 HI Data 30/12/ :04:00 CSK1 HI Ship Detection Report (20) AIS Data 30/12/ :00-06:00 VMS Data 30/12/ :00-06:00 VMS extrapolated at CSK1 Time Not Correlated Ships 20

21 MARITIME Multiple acquisitions from different sensors allow a real monitoring of an area 21

22 MARITIME BIG DATA NEEDS Multi-mission approach to allow autocorrelation Use the growing number of VMS sources for correlation Use other data sources like MetOc products 22

23 AGRICULTURE Agricultural GeoDataWarehouse (GDWH) is a decision support system that combines GIS and Business Intelligence (BI) technologies in order to support the whole agriculture cycle from the monitoring and optimization of crops to the analysis of the environmental and economic impact of phenomena like soil erosion, climate change, disasters (flood) and drought The application is an efficient instrument to access, explore and analyze available information, turning large amounts of raw and often uncorrelated data into knowledge. 23

24 AGRICULTURE USERS Basic Users These users have no specific knowledge about the system and they simply browse the different GDWH thematic areas and are able to generate and visualize reports with specific hierarchical level for different time and geographic frameworks. Expert Analysts These users have a greater understanding of the GDWH mechanisms and they are able to define their own thematic areas, generate reports with customized contents. Administrators These users are able to generate thematic areas and predefined reports to be used by the Basic Users. All the users, simply browsing the available functionalities, are able to perform geospatial analysis; for example: neighborhood or proximity analysis hierarchical browsing (drill down and roll up) comparison with predefined thresholds, adjustable for different geographic reference areas 24

25 AGRICULTURE EXAMPLES TRIGGERS FILTERS PUNCTUAL ANALYSYS 25

26 AGRICULTURE DATA Already at the edge of big-data (several TB of vectors which are growing faster, 20 years of historical data) Growing data processing needs in terms of multi-temporal geospatial analysis Growing number of external data for Geospatial analysis 26

27 AGRICULTURE MULTI-TEMPORAL GEOSPATIAL ANALYSIS Multi-temporal geospatial analysis has been provided in specific frameworks from the intersection and correlation of heterogeneous information such as: Spatial distribution of farms in terms of: Cadastral and eligible surfaces Consistency of zoo-technical distributions Aid applications and payments Characterization in terms of: Morphological parameters (elevation, exposition, slope) Areas of particular environmental interest Land cover/agronomic use Indicators and other relevant information for the monitoring of National Rural Network: PSN National Development Plan for Rural Development Leader approach for local rural projects Bee population, health and honey production Evaluation of potential impact on agriculture (number of affected farms, total surfaces, etc.) derived from national erosion-landslide risk map 27

28 AGRICULTURE BIG DATA NEEDS Increase capability on Geospatial Analysis (e.g. historical data analysis, simulated risk maps) Increase the use of EO data into the analytics Use Cloud technology (e.g. Map- Reduce spatial analysis) to sustain the volume of growing data (sensors on the tractors) in the future years (smart agriculture) 28

29 Thanks! 29