Big Data, Data and Information Mining for Earth Observation

Image Information Mining and
Knowledge Discovery from Earth
Observation Data
Towards the Sentinels Era

P.G. Marchetti ESA, M. Iapaolo Randstad
Ground Segment and Mission Operations Department
Research and Ground Segment Technology Section
Earth Observation Programmes Directorate
pier.giorgio.marchetti@esa.int michele.iapaolo@esa.int

International Conference Frontiers in Diagnostic Technologies (ICFDT 2013)

26/11/2013

Outline

1. Background on the European Space Agency
2. Motivation
3. Overview of ESA activities in the IIM field
4. Systems and services for EO data exploitation
5. The road ahead


26/11/2013

The Heritage: ERS and ENVISAT
• ERS and Envisat missions 1991-2012
•

More than 2 Petabytes of data

•

Two decades of global change records

•

Need for data preservation, availability
and exploitation

ESA UNCLASSIFIED – For Official Use


26/11/2013

Ten Years of Envisat Science
5000 scientific
projects
using Envisat data

Iceland
2010

Ozone hole 2005

Arctic 2007

First images
L’Aquila 2009

Global air
pollution

B-15A
iceberg
Chlorophyll
concentration

Japan 2011

Bam earthquake
Prestige tanker
oil slick

CO2 map

Launch

Hurricane
Katrina

Envisat
Symposium
Salzburg (A)

Mar 02

Sep 04

Envisat was the Sentinel
“precursor” for many operational
users

Envisat
Symposium

Montreux (CH)
Apr 07

Living Planet
Symposium
Bergen (N)
Jun 10

Living Planet
Symposium

Edinburgh (UK)
Sep 13

and many workshops dedicated to specific Envisat user communities


26/11/2013

The Copernicus Programme
Copernicus (formerly known
as GMES) is a European space
flagship programme led by
the European Union

Space
Component

Provides the necessary data
for operational monitoring of
the environment and for civil
security
ESA coordinates the space(*)
component
(*)spacecraft, flight operation segment, ground
segment

In-Situ
Component

Services
Component

6


26/11/2013

Copernicus Space Component: Dedicated
Missions

S1A/B: Radar Mission
S2A/B: High Resolution Optical Mission
S3A/B: Medium Resolution Imaging and Altimetry Mission
S4A/B: Geostationary Atmospheric Chemistry Mission
S5P: Low Earth Orbit Atmospheric Chemistry Precursor Mission
S5A/B/C: Low Earth Orbit Atmospheric Chemistry Mission
Jason-CS A/B: Altimetry Mission
7


26/11/2013

Copernicus Contributing Missions

COSMO-Skymed

SPOT (VGT)

TerraSAR–X
Tandem-X

PROBA-V

Radarsat
DMC

Pléiades

Copernicus
Contributing
Missions

Cryosat

Deimos-2

RapidEye

Jason

Atmospheric
missions

SPOT (HRS)
MetOp

Meteosat 2nd Generation


26/11/2013

Motivation
1. Foster the use of IIM and derived technologies in support of the EO
data exploitation
2. Develop state-of-the-art data processing for improving access and
dissemination of future EO data (e.g. Sentinels mission)
3. Implement systems and services for supporting the “scientific
exploitation” of EO data
4. Investigate new approaches and methodologies to exploit data from

all available missions and archives (joint effort with Long Term Data
Preservation programme)

26/11/2013

Image Information Mining
Coordination Group (IIMCG)
The Image Information Mining Coordination Group (IIMCG):
Space Agencies (ESA, DLR, CNES, ASI)
European Institutions (EUSC, JRC)
 National Research Institutes (Uni-Trento, ETHZ, INGV, Mississippi
State University)



Main objectives:
 Inform Agencies and partners, promote research and technological
activities on IIM (automatic information extraction from EO data for
image understanding and retrieval)
 Promote the use of IIM techniques for management and exploitation
of very large EO data archives/missions (PB of data)
 Foster the role of IIM in the context of future missions and existing
archives
 Involve industry and agency partners to increase the relevance of
IIM activities in Europe


26/11/2013

10 years of IIM activities at ESA
2000

2002

2004

2006

2008

2010

2012

Knowledge Driven Information
Mining Prototype
Knowledge-centred Earth
Observation Prototype

Multi-sensor Evolution Analysis Prototype


Technology activities over last decade



Main achievements:






KIM System: IIM reference prototype @ ESRIN
Platforms for EO data exploitation (KEO, GPOD, SSE, etc.)
Tools for multi-temporal and evolution analysis (MEA)

Issues:




Limited number of scientific and industrial partners involved
National efforts not coordinated and harmonised
Funds limited wrt the size of the research goals


26/11/2013

Image Information Mining
From Data to Information

Data (PB)

Acquisition

EO Data

Catalogue & Ordering

Image Information
Mining (IIM)
Information

Algorithms & Applications

Knowledge

Models & Ground Truth

Information (KB)

 Provides processing tools to extract features from images and associate meaning
to extracted features (bridging the gap between data and information)
 Empower users (researchers, service providers, decision makers) to identify and
reuse relevant information for their applications
 Encourage the use of common cooperative environments to achieve a common
knowledge

26/11/2013

KIM

(Knowledge-based Information Mining)

The KIM prototype developed @ ESRIN permits:
 Intelligent and effective access to information in large EO datasets
 Improved exploration and use of EO images for scientific research
 Extraction of relevant information for different applications (change
detection, global monitoring, disaster management, …)
 Implementation, integration and validation of services derived from IIM methods

Three main components:
1. Ingestion Software (Primitive Feature Extraction / Clustering)
2. Database (storing extracted information)
3. Interactive Client Application
i.
ii.
iii.
iv.

Training and definition of “semantic rules”
Application of training (rules) to the entire collection
Definition of “semantic labels” for extracted information
Store for successive re-use


26/11/2013

KIM Architectural Elements
Input
 EO images

Output
 Identifiers of searched images
 Feature Maps / Thematic maps

laveirteR egam I desaB tnetnoC
laveirteR egam I desaB tnetnoC

KIM
seirotisopeR ataD
seirotisopeR ataD

Ingestion

Feature Extraction
Clustering

Database
XUA
XUA

noitacifissalC desivrepusnU
nInformation usnU
oitacifissalC desivrep

Mining

EO Images
seires emiT
ssiisyls na iT
e re a em
sisylana


26/11/2013

Client

KIM Search and label
KIM permits to inspect a collection of images…
…interactively define “semantic features” using the “primitive features” extracted by the
system…
…search for the defined feature within the entire collection…


26/11/2013

KIM Information Extraction
…and extract Feature Maps or Thematic Maps
Cloud masks

Flooded areas


26/11/2013

Forest Monitoring

KIM Primitive features
Spectral

Spectral signature

Texture

Structural information extracted with the Gibbs Marcov Random Fields (GMRF) model
S0 - full resolution images; S1 - sub-sampled images

DCT

Discrete Cosine Transform: transforms signals and images from the spatial domain to
the frequency domain

EMBD

Enhanced-Model-Based-Despeckling: performs a high quality despeckling of SAR
images

Area

Area of the objects detected with the segmentation process

Compactness

Compactness of the objects detected with the segmentation process

Spectral Mean

Mean value of the radiometric information of the image inside the closed area detected
by the segmenter

Spectral
Variance

Variance of the radiometric information of the image inside the closed area detected by
the segmenter

Hu Moments

Hu-Moment Invariants: shape information conveyed by the contour points. Hu moments
are invariant to scale, rotation and translation (the first 4 out of 7 invariant moments as
shape descriptors have been used).


26/11/2013

KIM Validation
KIM has been tested and validated with different datasets:
1. MERIS RR / MERIS FR

2. ERS / ASAR
3. SPOT
4. Landsat
5. Maps (Level 2 / Level 3 products)
Large number of collection created
Low number of significant semantic features identified


26/11/2013

KIM for Information Extraction
1. Flood Detection (SAR data)
2. Cloud Detection (MERIS RR)
3. Long-term Forest Monitoring (Landsat)

4. Rapid Mapping / Damage Assessment (VHR optical data)

Potentialities of the tool have been highlighted and
confirmed in different contexts

End-users expectations not always achieved


26/11/2013

KEO

(Knowledge centred Earth Observation)

KEO is a distributed Component-based Processing Environment (CPE)
permitting to:
a.

Create & semantically identify internal/external Processing Components

b.

Graphically chain Processing Components into processing chains

c.

Create Processing Components from IIM components (KIM training)

d.

Export and store outputs into Web Servers (WFS, WMS, WCS)

KEO also provides some relevant Reference Data Sets:
a.

Heterogeneous data and information, growing with external
contributions (images, documents, DEMs, photos, processors, etc.)

b.

In support of various applications: Classification, Time Series
Analysis, Ortho-rectification, Urban Monitoring, Interferometry, etc.)


26/11/2013

KEO CPE
Graphical Processor Designer


26/11/2013

MEA

(Multi-temporal Evolution Analysis)

Multi-temporal analysis of HR / VHR products:
1. Select multi-temporal applications that might benefit from such extension
2. Design, implement and integrate the automatic multi-temporal algorithms
to support the selected applications

3. Create the needed HR/VHR Reference Data Sets and Evolution Models
4. Develop standard interfaces between the different systems for common
exploitation of ingested data and processing capabilities
5. Integrate algorithms and Evolution Models provided by other independent
projects

6. Validate (with the support of a Validation Group) the Automatic Multitemporal algorithms and Evolution Models


26/11/2013

MEA

(Multi-temporal Evolution Analysis)

The MEA-ASIM system aims at providing:
1. Advanced tools for Land Use / Land Cover change analysis
2. Level-2 EO products for real time exploitation
3. Interfaces to external systems (G-POD, KEO, data providers, etc.)
4. Access to data via standard WCS OGC interface

RSS Data Farm

5. Native support for Sentinel-2 datasets


26/11/2013

MEA Pixel and Coverage analysis

Time-Series Analisys
Single and multi plot functionality


26/11/2013

MEA Pixel and Coverage analysis

Cross-comparison of EO products


26/11/2013

Exploitation Platforms for EO
Development and implementation of collaborative Exploitation
Platforms (G-POD, SSEP, E-CEO, etc.):
1. Fostering the scientific exploitation of EO data
2. Automating the creation data mining and information extraction
experiments and algorithms

3. Supporting the creation of EO-based applications and services
4. Supporting the entire scientific research process:
a.

Addressing specific scientific challenges and tackling new research
problems in a “parallel and collaborative way”

b.

Generation of reproducible results that can be easily shared and
validated


26/11/2013

Research and Service Support:
Research Process


26/11/2013

RSS G-POD Process Steps

Principal Investigators
•
•

RSS

EO algorithms delivery
Data type and range indication
•

Data are made available in the RSS
catalogue

•

Algorithm porting and Integration

•

Output validation

•

Test and validation (involving the PI)

•

On-demand EO data processing

•

On-demand EO data processing

•

Use of produced data (scientific
projects delivery)

•

Delivery

•

Publications


26/11/2013

RSS Flexible Resources
On-demand processing service:
Process

EO data

delivery

G-POD

EO Scientists
Principal
Investigators

Platform

Volume accessed by PI projects in 2012:
Infrastructure
• Total Number Submitted Jobs 38,774
• Average Number of Products per Job: 35
• Average Product Size: 700 MB
• Total Size Data Processed: 906 TB
ESRIN
- 172 cores
- 400 TB

UK-PAC
- 96 cores
- 300 TB


Flexible/
Unlimited
Infrastructure
- 10-200 cores
- 1-10 TB

26/11/2013

Flexible Infrastructure satisfies:
HW requirements
Connectivity requirements
SLA (HA, help desk, ticketing
systems, etc.)

RSS Facts & Figures
On-demand Processing: actual figures in the last 3 years
•Supported more than 40 active users per year
•Supported >20 processing/re-processing campaigns (included entire missions, e.g.
MERIS, ASAR, SMOS and TPM)
•Integrated ~10 new algorithms per year
•Upgraded ~15 algorithms per year
•Set-up flexible (additional) processing capacity in less than 2 working days
•Managed >450 TB data farm (ESA, TPM and scientific products)
•

ESA – ENVISAT (~320TB), ERS (~50TB), SMOS (~10TB)

•

TPM – MSG (~19TB), METOP (~11TB), ALOS (~2TB)

•

Scientific products – AARDVARC Swansea University and MGVI JRC (produced
by GPOD and distributed via SSE), MKL3 ACRI


26/11/2013

SMOS Testbed
Service Purpose
SMOS Testbed aims to provide a flexible test environment to support the ESA calibration team for L1
calibration, and the Expert Support Laboratories (ESLs) for L2 Soil Moisture and Ocean Salinity pre-validation.

G-POD support elements
–

Fast integration of new versions

–

SMOS L0 NRT ingestion chain set-up for L1 NRT custom re-processing

–

Access to online data for bulk re-processing

–

Access to flexible cloud resources for meeting deadlines

–

On-demand SMOS L1 and L2 processors available for SMOS Teams

SMOS New
Processor
Delivery

Processor
Integration in
G-POD

G-POD
Processing
Campaign

Results
Analysis and
Validation

Auxiliary And
Calibration Datasets


26/11/2013

SMOS Testbed
SMOS L1 TESTBED
Processor:s Calibration,
Telemetry, Level 1A, 1B, 1C
Supported versions: 3.46, 5.00,
5.01, 5.02, 5.03, 5.04, 5.05, 6.00,
6.01
Reference data series: L0, L1A,
L1B, L1C (reprocessed)
Auxiliary data baseline: as
per Operational environment


SMOS L2 SOIL
MOISTURE TESTBED

SMOS L2 OCEAN
SALINITY TESTBED

Processor: SM L2, SM L2 postprocessing
Supported versions: 4.00, 4.01
Reference data series: L1C
(reprocessed)
per CESBIO reprocessing

Processor: OS L2
Supported versions: 5.00, 5.50
Reference data series: L1C
(reprocessed)
per Operational environment


26/11/2013

The road ahead (1)
Funds for a full scale research programme are needed to foster:
 The widening of competence and expertise in several research
centres/industrial actors in Europe
 The widening of efforts to cover time series analysis and data
analytics in general
 Research and development of multi dimensional and scalable DB
solutions (including nosql databases, hadoop, etc.)
 Large collaborative and persistent effort on crowdsourcing,
benchmarking, image and feature annotation and evaluation
 Establishing a theoretical framework to bridge the semantic gap and
be able to assign “discriminating power” to extracted features and
“categorization” of extracted classes/objects
 High quality software and algorithm developments able to reach at
least the “software prototype” readiness level


26/11/2013

The road ahead (2)
To achieve these goals it is necessary to:
 Establish a common “Big Data Mining” framework with
interdisciplinary partners
 Establish a R&D network to sustain this field
 Establish a network of users, and give them access to IIM resources
(system, data, …)
 Enlarge the scope of “Image” Mining to the physical parameters
measured by EO instruments
 Address the “instrument” gap, instrument-application
 Develop methods to use heterogeneous data: in
situ, metadata, linked data, models, etc.


26/11/2013

The road ahead (3)
 Activities to be started:
 Promote IIM technology acceptance for EO users
 Extend and adapt methods from multimedia and social nets
 Apply human computing, gather knowledge from the use of the
system, adaptation, personalization, etc.
 Focus on Web/Internet based systems
 Develop simple and specific HMI and GUI
 Focus on Visual Data Mining, Visual Analytics, and related methods

 In the PDGS identify “long term data preservation” and “interactive
data exploitation” components
 Design data representations: actionable information


26/11/2013

The road ahead (4)
Merge the best of :
• data mining approach
• time series capability
• ability to support and host the user
algorithm


26/11/2013

MANY THANKS!



26/11/2013

Big Data, Data and Information Mining for Earth Observation

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