Big Data Analytics for Detailed Urban Mappingdue.esrin.esa.int/muas2015/files/presentation67.pdf ·...

Big Data Analytics for Detailed Urban Mapping

Mihai Datcu Daniela Molina Espinoza, Octavian Dumitru, Gottfried Schwarz

Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum

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Big Data: The German EO Digital Library

The data access


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Information vs. Data

TerraSAR-X 11-OCT-2008

512x512 pixels

ERS1 24-JUL-1992

512x512 pixels


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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 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. 5145-5159, 2013.


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EOLib: Data Mining and KDD new components

Data Model Generation Data Mining DataBase Query Engine Visual Data Mining Knowledge Discovery in

Databases Epitome Generation


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Data Model Generation


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TerraSAR-X L1b product

Metadata Extraction

Image Tiling

Quick –looks

generation

Primitive Feature

extraction

Create the product model

Data Model Generation

TerraSAR-X image Tiles with different size Primitive features: Gabor filter and weber local descriptor


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Data Mining Database


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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


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Query Engine


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Query Engine

Met

adat

a • Coordinates (lat/lon) • Incidence angles • Acquisition time • Pixel spacing • Number of

columns/rows • sensor • Mission • orbits

Sem

antic

s • 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


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Query Engine: Examples

Example of query: Storage tanks and Medium density urban area are the query parameters


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Visual Data Mining


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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


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Knowledge Discovery in Databases


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KDD used to define semantic annotations of the image content.

Interactive search supported by relevance feedback mechanisms

Goals is to build a model which performs the mapping between low-level image descriptors (primitive features ) and high-level image concepts (semantics)

Knowledge Discovery in Databases


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KDD: GUI

Classification SVM with RF

Annotated category

Tiles Collections

Work Flow:


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Cascaded Active Learning Le

vel 0

: 200

x200

pix

els


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vel 1

: 100

x100

pix

els


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vel 2

: 50x

50 p

ixel

s


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Cascaded Active Learning

Level 0

200x200 pixels

Level 1

100x100 pixels

Level 2 50x50 pixels

Refugee camp in Jordan

Semantic Tents Sand category


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Cascaded Active Learning

Level 0

200x200 pixels

Level 1

100x100 pixels

Level 2 50x50 pixels

Semantic Storage tanks Industrial buildings category

. . .

. . .

Petroleum storage area near Riffa, Bahrain


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Ontology

SAR images

Features Features Features Features Features Features Features Features Features

Classification

Category 1 Category 2 Category 4 Category 3 Category n

Primitivefeatures

Patches

Tiling

High density residential areas

Airport - Runways Boats Agriculture

Railways tracks

Semantic catalogue

Annotation


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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 Runways ……..

• Roads o 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 ……..


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Content Semantic Annotation


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Semantic catalogues

-Bangkok (Thailand); -Shenyang (China); -Nazca Lines (Peru); -Havana (Cuba); -Venice (Italy); -Vasteras (Sweden); -Oran (Algeria); -Bogota (Columbia) …

350 cities 850 classes


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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.


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Results: Venice

Our classification – 17 categories versus CLC – 10 categories CORINE Land Cover (CLC)

our classification !!! bridges, buoys, and sea categories of our proposed annotation method are included in marine waters – coastal lagoons in the case of CLC


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Results: Venice

Data analysis Percentage of patches per semantic category for Venice and a typical patch per category


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Results: Venice

Quantitative results Precision / recall per semantic category for Venice


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1 HS TerraSAR-X Scene = up to10 000 image patches (100 x 100 m)

SCENE CATEGORIES & INFORMATION CONTENT: BUCHAREST


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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 35 Vortrag > Autor > Dokumentname > Datum


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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).

Agriculture

Bridges

Aquaculture

H. Voltage poles

Flooded areas

Bridges

Debris

H. Voltage poles

TerraSAR-X scene before Tsunami – 20.10.2010 TerraSAR-X scene after Tsunami – 12.03.2011


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Data Analytics


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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

Conclusions

Slide Number 1Big Data: The German EO Digital LibraryInformation vs. DataSlide Number 4EOLib: Earth Observation image LibrarianEOLib: Data Mining and KDD new componentsData Model GenerationSlide Number 8Data Mining DatabaseData Mining Data BaseQuery EngineQuery EngineQuery Engine: ExamplesVisual Data MiningVisual Data MiningKnowledge Discovery in DatabasesSlide Number 17KDD: GUISlide Number 19Cascaded Active LearningCascaded Active LearningCascaded Active LearningCascaded Active LearningCascaded Active LearningOntologyContent Semantic AnnotationContent Semantic AnnotationSemantic cataloguesResults: VeniceResults: VeniceResults: VeniceResults: VeniceSlide Number 33EvaluationSlide Number 35Disaster effects analysis Data AnalyticsSlide Number 38

Big Data Analytics for Detailed Urban Mappingdue.esrin.esa.int/muas2015/files/presentation67.pdf ·...

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