Big Data Analytics for Detailed Urban Mapping. Mihai Datcu Daniela Molina Espinoza, Octavian Dumitru, Gottfried Schwarz

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1 Big Data Analytics for Detailed Urban Mapping Mihai Datcu Daniela Molina Espinoza, Octavian Dumitru, Gottfried Schwarz

2 Big Data: The German EO Digital Library The data access Folie 2

3 Information vs. Data ERS1 24-JUL x512 pixels TerraSAR-X 11-OCT x512 pixels Folie 3

4 Folie 4

5 EOLib: Earth Observation image Librarian EOLib is a modular system composed of several components: PGS in blue and new EOLib in orange EOLib offers mining/search services for accessing the image archive EOLib generates semantic Folie 5 descriptions of the image content D. Espinoza-Molina and M. Datcu, Earth-Observation Image Retrieval Based on Content, Semantics, and Metadata, IEEE TGARS, vol. 51, no. 11, pp , 2013.

generates semantic Folie 5 descriptions of the image content D. Espinoza-Molina and M.

6 EOLib: Data Mining and KDD new components Data Model Generation Data Mining DataBase Query Engine Visual Data Mining Knowledge Discovery in Databases Epitome Generation Folie 6

7 Data Model Generation Folie 7

8 Data Model Generation TerraSAR-X L1b product TerraSAR-X image Tiles with different size Primitive features: Gabor filter and weber local descriptor Metadata Extraction Image Tiling Quick looks generation Primitive Feature extraction Create the product model Folie 8

weber local descriptor Metadata Extraction Image Tiling Quick

9 Data Mining Database Folie 9

10 Data Mining Data Base It is a relational database DMDB comprises about 800 processed products 8. millions of tiles 20 thousand metadata entries. 106 semantic labels Folie 10

11 Query Engine Folie 11

12 Query Engine Metadata Coordinates (lat/lon) Incidence angles Acquisition time Pixel spacing Number of columns/ rows sensor Mission orbits Semantics Agriculture Cropland Rice plantation.. Bare ground Cliff Desert.. Forest Forest coniferous Forest mixed. Urban area Commercial areas High density residential areas. Metadata parameter based on XML annotation file of TerraSAR-X L1b products Semantic parameters based on EO Taxonomy Folie 12

. Forest Forest coniferous Forest mixed. Urban area Commercial areas High density residential areas.

13 Query Engine: Examples Example of query: Storage tanks and Medium density urban area are the query parameters Folie 13

14 Visual Data Mining Folie 14

15 Visual Data Mining Provides a projection of the entire database Representation of the data in the 3D space (dimensionality reduction) Interactive exploration and analysis of very large, high complexity data sets This allows the user: To browse the image archive To find scenes of interest Semantically consistent groups may appear inside the data Folie 15

very large, high complexity data sets This allows the user: To browse the image archive

16 Knowledge Discovery in Databases Folie 16

17 Knowledge Discovery in Databases KDD used to define semantic annotations of the image content. Interactive search supported b y r e l e v a n c e f e e d b a c k mechanisms Goals is to build a model which performs the mapping between low-level image descriptors (primitive features ) and high-level image concepts (semantics) Folie 17

Interactive search supported b y r e l e v a n c e f e e d b a c k mechanisms Goals

18 KDD: GUI Work Flow: Classification SVM with RF Annotated category Collections Tiles Folie 18

19 Folie 19

20 Cascaded Active Learning Level 0: 200x200 pixels Folie 20

21 Cascaded Active Learning Level 1: 100x100 pixels Folie 21

22 Cascaded Active Learning Level 2: 50x50 pixels Folie 22

23 Cascaded Active Learning Level 0 200x200 pixels Level 1 Refugee camp in Jordan 100x100 pixels Level 2 50x50 pixels Semantic Tents Sand category Folie 23

24 Cascaded Active Learning Level 0 200x200 pixels Level 1 100x100 pixels Petroleum storage area near Riffa, Bahrain Level 2 50x50 pixels Semantic Storage tanks Industrial buildings category Folie 24

25 Ontology SAR images Tiling Patches Features Features Features Features Features Features Features Features Features Primitive features Classification Category 1 Category 2 Category 3 Category 4 Category n Annotation High density residential areas Airport - Runways Agriculture Boats Railways tracks Semantic catalogue Folie 25

26 Content Semantic Annotation Proposed three-level annotation scheme Settlements Inhabited built-up areas o High density residential areas.. Uninhabited built-up areas o Skyscrapers.. Industrial production areas Industrial facilities o Industrial buildings.. Industrial storage areas o Depots and dumps.. Military facilities Air force facilities.. Agriculture Greenhouses.. Natural vegetation Mixed forest.. Transport Airports o.. Roads o Runways Streets and roads.. Railways o Railway tracks.. Bridges and tunnels o Bridges and fly-overs.. Ports and shipbuilding facilities o Harbour infrastructure.. Water vessels o Small vessels (boats).. Bare ground Mountain.. Water bodies Buoys.. Folie 26

27 Content Semantic Annotation Folie 27

28 Semantic catalogues 350 cities 850 classes - Bangkok (Thailand); - Shenyang (China); - Nazca Lines (Peru); - Havana (Cuba); - Venice (Italy); - Vasteras (Sweden); - Oran (Algeria); - Bogota (Columbia) 28 Folie 28

29 Results: Venice Investigated area - Venice, Italy Validation our results were compared for the same area with CORINE Land Cover (CLC) categories. TSX image Further validation our results to be compared with Urban Atlas categories. Folie 29

30 Results: Venice Our classification 17 categories versus CLC 10 categories (CLC) CORINE Land Cover our classification!!! bridges, buoys, and sea categories of our proposed annotation method are included in marine waters coastal lagoons in the case of CLC Folie 30

31 Results: Venice Data analysis Percentage of patches per semantic category for Venice and a typical patch per category Folie 31

32 Results: Venice Quantitative results Precision / recall per semantic category for Venice Folie 32

33 SCENE CATEGORIES & INFORMATION CONTENT: BUCHAREST 1 HS TerraSAR-X Scene = up to image patches (100 x 100 m) Folie 33

34 Evaluation Evolution of precision/recall results among four categories (storage tanks, ships, ocean, and industrial areas) out of seven for all levels (left side) and for the finest-level (right side). Folie 34

35 Folie 35 Vortrag > Autor > Dokumentname > Datum

36 Disaster effects analysis The damages in the agriculture can be clearly seen by comparing the classification in pre disaster image (left figure) with the post disaster image (right figure). TerraSAR-X scene before Tsunami TerraSAR-X scene after Tsunami Agriculture Flooded areas Bridges Bridges Aquaculture Debris H. Voltage poles H. Voltage poles Folie 36

37 Data Analytics Folie 37

38 Conclusions Operational Data Mining, Visual Data Mining, KDD used to define semantic annotations of the image content. Interactive search supported by active learning mechanisms More than 1000 detailed categories of buildup scenes Time Series Exploration and Analysis Big Data Folie 38

39 Acknowledgment: The VDM component was developed by TERRASIGNA References P. Blanchart, P., M. Ferecatu, C. Shiyong Cui, M. Datcu, 2015, Pattern Retrieval in Large Image Databases Using Multiscale Coarse-to-Fine Cascaded Active Learning, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume: 7, Issue: 4, Pages: D. Molina Espinoza, M. Datcu, 2013, Earth-Observation Image Retrieval Based on Content, Semantics, and Metadata, IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 11, pp C. Dumitru, S. Cui, D. Faur, M. Datcu, 2014, Data Analytics for Rapid Mapping: Case Study of a Flooding Event in Germany and the Tsunami in Japan Using Very High Resolution SAR Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,. O. Dumitru, M. Datcu, 2013, Information Content of Very High Resolution SAR Images: Study of Feature Extraction and Imaging Parameters. IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 8, pp M. Datcu, K. Seidel, 2005, Human Centered Concepts for Exploration and Understanding of Images, IEEE Trans. on Geoscience and Remote Sensing, ISSN , Vol. 43, No.3, pp M. Datcu, H. Daschiel, et al, 2003, Information mining in Remote Sensing Image Archives: System Description, IEEE Trans. on Geoscience and Remote Sensing, ISSN , Vol. 41, No. 12, pp Folie 39

Big Data Challenge: Mining Heterogeneous Data. Prof. Mihai Datcu. German Aerospace Center (DLR) Munich Aerospace Faculty

Big Data Challenge: Mining Heterogeneous Data Prof. Mihai Datcu German Aerospace Center (DLR) Munich Aerospace Faculty Sensing & Big Data Big Data: - Computer hardware and the Cloud - Storage Challenges