SSTD 2005, Angra dos Reis
Challenges for spatio-
temporal database
researchers
Gilberto Câmara
Director for Earth Observation, National Institute
for Space Research
Member of Executive Committee, Group on Earth
Observations
Member, Scientific Steering Committee, IGBP
LAND project
INPE - brief description
National Institute for Space Research
main civilian organization for space activities in Brazil
staff of 1,800 ( 800 Ms.C. and Ph.D.)
Areas:
Space Science, Earth Observation, Meteorology and
Space Engineering
R&D in GIScience at INPE
Graduate programs in Computer Science and
Remote Sensing
Research areas
Spatial statistics
Spatial dynamical modelling
Spatio-temporal databases
Image databases and image processing
Technology
TerraLib – open source library for ST DBMS
“Give us some new problems”
(Dimitrios Papadias, SSTD 2005)
“Give us some new problems”
What about saving the planet?
Great challenge:
Database support for
earth system science
source: NASA
Earth as a system
Physical Climate System
Climate
Atmospheric Physics/Dynamics Change
Terrestrial
Ocean Dynamics
Energy/Moisture
Human
Activities
Global Moisture Soil CO2
Marine Terrestrial Land
Biogeochemistry Ecosystems Use
Tropospheric Chemistry CO2
Biogeochemical Cycles Pollutants
(from Earth System Science: An Overview, NASA, 1988)
The fundamental question
How is the Earth’s environment changing,
and what are the consequences for human
civilization?
A society with the ability to gather and
understand Earth Science information and make
proactive, timely environmental predictions and
decisions at all relevant geographical and
societal levels.
Source: NASA,
The Road Ahead for Earth
Observations
A new international organization tasked with
implementation a Global Earth Observation
System of Systems (GEOSS).
GEOSS shall coordinate a wide range of space-
based, air-based, land-based, and ocean-based
environmental monitoring platforms, resources
and networks – presently often operating
independently.
Membership in GEO currently includes 51
countries plus the European Commission, and
29 participating international organisations.
Coordinating Earth Observing Systems
Vantage Points Capabilities
L1/HEO/GEO
Far- TDRSS &
Permanent
Space Commercial
Satellites
LEO/MEO
Near- Commercial
Space Satellites
and Manned
Spacecraft
Airborne Aircraft/Balloon
Event Tracking
Deployable
and Campaigns
Terrestrial
Forecasts & Predictions User
Community
Remote Sensing:
Increased EO capability
GeoSensors: New technology of earth
observations
Smart Dust (UC “Spec” mote
Berkeley)
UC Berkeley
Intel
mote
MICA
mote
Group on Earth Observation System of
Systems
G8 supports GEOSS
The G8 welcome the adoption of the 10-year
implementation plan for development of the Global Earth
Observation System of Systems (GEOSS).
We will:
(a) move forward in the national implementation of GEOSS in our
member states;
(b) support efforts to help developing countries and regions
obtain full benefit from GEOSS, such as placement of
observational systems to fill data gaps, developing of capacity for
analysing and interpreting observational data, and development
of decision-support systems and tools relevant to local needs;
Gleneagles plan of action, 2005
How good is the plan for GEOSS?
GEOSS should agree to use any one of four open
standard ways to describe service interfaces (CORBA,
WSDL, ebXML or UML).
The standard ISO/IEC 11179, Information Technology--
Metadata Registries, provides guidance on representing
data semantics.
Data and information resources and services in GEOSS
typically include references to specific places on the
Earth. Interfaces to use these geospatial data and
services are agreed upon through the various Spatial
Data Infrastructure initiatives (e.g., OGC WMS, WCS,
and WFS).
GEOSS Implementation Plan, 2004
The Five Orders of Ignorance
0th Order Ignorance (0OI): Lack of Ignorance
I (provably) know something
1st Order Ignorance (1OI): Lack of Knowledge
I do not know something
2nd Order Ignorance (2OI): Lack of Awareness
I do not know that I do not know something
3rd Order Ignorance (3OI): Lack of Process
I do not know a suitably effective way to find out that I don’t know that I
don’t know something
4th Order Ignorance (4OI): Meta-Ignorance
I do not know about the Five Orders of Ignorance
The five orders of ignorance, Phillip G. Armour, CACM, 43(10), Oct 2000
What does GEOSS doesn’t know that it doesn’t
know?
Ontology
Lake Habitat
Converter
GIS A GIS B
We don’t now how to do this!!
The Road Ahead: Geosensors
Advances in remote sensing are giving
computer networks the eyes and ears they need
to observe their physical surroundings.
Sensors detect physical changes in pressure,
temperature, light, sound, or chemical
concentrations and then send a signal to a
computer that does something in response.
Scientists expect that billions of these devices
will someday form rich sensory networks linked
to digital backbones that put the environment
itself online.
(Rand Corporation, “The Future of Remote Sensing”)
From Global to local
scales: Spatio-temporal
modeling in Amazonia
Source: Carlos Nobre (INPE) Amazonia: the forest..
Source: Carlos Nobre (INPE
Source: Carlos N
Deforestation...
Fire...
Source: Carlos Nobre (INPE)
photo source: Edson Sano (EMBRAPA)
Large-Scale Agriculture
Agricultural Areas (ha)
1970 1995/1996 %
Legal Amazonia 5,375,165 32,932,158 513
Brazil 33,038,027 99,485,580 203
Source: IBGE - Agrarian Census
photo source: Edson Sano (EMBRAPA)
Cattle in Amazonia and Brazil
Unidade 1992 2001 %
Amazônia Legal 29915799 51689061 72,78%
Brasil 154,229,303 176,388,726 14,36%
Fonte: PAM - IBGE
Cattle in Amazonia and Brazil
Unidade 1992 2001 %
Amazônia Legal 29,915,799 51,689,061 72,78%
Brasil 154,229,303 176,388,726 14,36%
photo source: Edson Sano (EMBRAPA)
McDonald’s is bad for the planet!
Cattle in Amazonia and Brazil
Unidade 1992 2001 %
Amazônia Legal 29915799 51689061 72,78%
Brasil 154,229,303 176,388,726 14,36%
Fonte: PAM - IBGE
Cattle in Amazonia and Brazil
Unidade 1992 2001 %
Amazônia Legal 29,915,799 51,689,061 72,78%
Brasil 154,229,303 176,388,726 14,36%
Amazon Deforestation 2003
Deforestation 2002/2003
Deforestation until 2002
Fonte: INPE PRODES Digital, 2004.
Amazônia in 2005
source: Greenpeace
Amazônia em 2015? fonte: Aguiar et al., 2004
Modelling Complex Problems
Application of interdisciplinary knowledge to produce a
model.
If (... ? ) then ...
Desforestation?
Limits for Models: the line of our
ignorance
Social and Economic
Quantum
Uncertainty on basic equations
Systems
Gravity
Particle
Living
Physics
Systems
Global
Change
Chemical Applied
Reactions Sciences
Meteorology
Solar System Dynamics
Complexity of the phenomenon
source: John Barrow
What Drives Tropical Deforestation?
% of the cases
5% 10% 50%
Underlying Factors
driving proximate causes
Causative interlinkages at
proximate/underlying levels
Internal drivers
*If less than 5%of cases,
not depicted here.
source:Geist &Lambin
Nested Cellular Automata:
A Foundation for Building
Multifunctional Landscape
and Urban Dynamic
Models
Tiago Garcia Carneiro
Gilberto Câmara
Antônio Miguel Monteiro
Ana Paula Aguiar
Maria Isabel Escada
(manuscript under preparation, 2005)
Dynamic Spatial Models
f (It) f (It+1) f (It+2) f ( It+n )
F F
..
“A dynamical spatial model is a computational
representation of a real-world process where a location
on the earth’s surface changes in response to variations
on external and internal dynamics on the landscape”
(Peter Burrough)
Dynamic Spatial Models
Forecast
tp - 20 tp - 10
tp
Calibration Calibration tp + 10
Source: Cláudia Almeida
Emergence: Clocks, Clouds or Ants?
Clocks
Netwon’s laws (mechanistic, cause-effect
Paradigms:
phenomena describe the world)
Clouds
Stochastic models
Theory of chaotic systems
Ants
The colony behaves intelligently
Intelligence is an emergent property
Deterministic CA Models
(Clarke et al., 1997)
Diffusive
Spontaneous growth and
new growth spread of a
new growth
centre
Organic
Seed Cell
growth
Cell urbanised by
this step
Cell urbanised at Road
previous step influenced
growth
Growth moved to
road, and spread
Road
Source: Cláudia Almeida
Requirements
Simulation of different partitions in space
Each partition has different actors and processes
Farms
Settlements
10 to 20 anos
Recent
Settlements Old
(less than 4 Settlements
Source: Escada, 2003
years) (more than
20 years)
Spatial dynamic modeling
Demands Requirements
Locations change due to discretization of space in cells
external forces
Realistic representation of generalization of CA
landscape
Elements of dynamic discrete and continous
models processes
Geographical space is Flexible neighborhood
inhomogeneous definitions
Extensibility to include user-
Different types of models defined models
Cell Spaces
(a) land_cover equals deforested in 1985 (a) land_cover equals deforested in 1985
attr_id object_id initial_time final_time land_cover dist_primary_road dist_secondary_road
C34L18 01/01/1985 31/12/1985
C34L181985-01-0100:00:001985-12-3123:59:59 forest 7068.90 669.22
C34L18 01/01/1988 31/12/1988
C34L181988-01-0100:00:001988-12-3123:59:59 forest 7068.90 669.22
C34L18 01/01/1991 31/12/1991
C34L181991-01-0100:00:001991-12-3123:59:59 forest 7068.90 669.22
C34L18 01/01/1994 31/12/1994
C34L181994-01-0100:00:001994-12-3123:59:59 deforested 7068.90 669.22
C34L18 01/01/1997 31/12/1997
C34L181997-01-0100:00:001997-12-3123:59:59 deforested 7068.90 669.22
C34L18 01/01/2000 31/12/2000
C34L182000-01-0100:00:002000-12-3123:59:59 deforested 7068.90 669.22
C34L19 01/01/1985 31/12/1985
C34L191985-01-0100:00:001985-12-3123:59:59 forest 7087.29 269.24
C34L19 01/01/1988 31/12/1988
C34L191988-01-0100:00:001988-12-3123:59:59 deforested 7087.29 269.24
C34L19 01/01/1991 31/12/1991
C34L191991-01-0100:00:001991-12-3123:59:59 deforested 7087.29 269.24
C34L19 01/01/1994 31/12/1994
C34L191994-01-0100:00:001994-12-3123:59:59 deforested 7087.29 269.24
C34L19 01/01/1997 31/12/1997
C34L191997-01-0100:00:001997-12-3123:59:59 deforested 7087.29 269.24
C34L19 01/01/2000 31/12/2000
C34L192000-01-0100:00:002000-12-3123:59:59 deforested 7087.29 269.24
Hybrid Automata
Formalism developed by Tom Henzinger
(UC Berkeley)
Applied to embedded systems, robotics, process
control, and biological systems
Hybrid automaton
Combines discrete transition graphs with continous
dynamical systems
Infinite-state transition system
Event
Jump condition
Control Mode A Control Mode B
Flow Condition Flow Condition
Space is Anisotropic
Spaces of fixed location and spaces of fluxes in Amazonia
Motivation
Which objects are NEAR each other?
Motivation
Which objects are NEAR each other?
Using Generalized Proximity Matrices
Consolidated area Emergent area
Computational Modelling with Cell
Spaces
Cell Spaces
Components
Cell Spaces
Generalizes Proximity Matriz – GPM
Hybrid Automata model
Nested enviroment
Environment: A Key Concept in TerraME
An environment has 3 kinds of sub models:
Spatial Model: cellular space + region + GPM
(Generalized Proximity Matrix)
Behavioral Model: hybrid automata + situated
agents
Temporal Model: discrete event simulator
The spatio-temporal structure is shared by several
communicating agents
Support for Nested Environments
U
U
U
Environments can be nested
Multiscale modelling
Space can be modelled in different resolutions
Nested CA x Traditional CA
CA
Homogeneous, isotropic space
Local action
One attribute per cell (discrete values)
Finite space state
Nested CA
Non-homogeneous space
Action-at-a-distance
Many attributes per cell
Infinite space state
Software Architecture
RondôniaModel São Felix Model Amazon Model Hydro Model
TerraME Language
TerraME Compiler
TerraME Virtual Machine
C++ Signal C++ C++
TerraLib
Processing Mathematical Statistical
TerraME Framework librarys librarys librarys
TerraLib
http://www.terralib.org/
Deforestation Rate Distribution Module
Small Units Agent
latency
> 6 years Deforesting
Newly implanted
Deforestation >
Year of 80% Factors affecting rate:
creation
Slowing down Global rate
Iddle Relation properties density -
Deforestation =
100% speedy of change
Year of creation
Credit in the first years (small)
Large and Medium Units Agent
Deforesting
Deforestation >
80%
Year of
creation
Slowing down
Iddle
Deforestation =
100%
Allocation Module: different resolution,
variables and neighborhoods
1985
Small farms environments:
500 m resolution
Categorical variable:
deforested or forest
One neighborhood relation:
•connection through roads
Large farm environments:
1997 1997
2500 m resolution
Continuous variable:
% deforested
Two alternative neighborhood
relations:
•connection through roads
• farm limits proximity
Simulation Results
1985 1988 1991
1994 1997
Mining Patterns of Change
in Remote Sensing Image
Databases
Marcelino Pereira S. Silva
Gilberto Câmara
Ricardo Cartaxo M. Souza
Dalton M. Valeriano
Maria Isabel S. Escada
(IEEE Data Mining Conf, 2005)
Images are everywhere!
Observational satellites from 1 meter to 1 km
Spectral bands, ranging from visible to radar
Periodic sources of information
Knowledge gap in Earh Observation
source: John McDonald (MDA)
Why image database mining?
Most applications of EO data
“Snapshot” paradigm
Recipe analogy
Take 1 image (“raw”)
“Cook” the image (correction + interpretation)
All “salt” (i.e., ancillary data)
Serve while hot (on a “GIS plate”)
But we have lots of images!
MSS - Landsat 1
WRS1 248/62
07/07/1973
Why image database mining?
What’s in an Image?
Isan image just a field of energy received by a
sensor?
Are images instruments for capturing landscape
dynamics?
(Camara, Egenhofer et al, 2001)
Improving Societal Benefits
In search of a “killer-app”
How many cutting-edge applications exist for
extracting information in large image databases?
How much R&D is being invested in spatial data
mining in large repositories of EO data?
How do we put our image databases to more effective
use?
Image Database mining
A large remote sensing image database is a
collection of snapshots of landscapes, which
provide us with a unique opportunity for
understanding how, when, and where changes
take place in our world.
We should search for changes, not search for
content
Image mining part I: Finding patterns
Image segmentation
Segmentation extracts objects from images
Segmentation comparison
A comparison of segmentation programs for high resolution
remote sensing data, G. Meinel, M. Neubert, ISPRS Congress,
2004
Spatial Patterns of Deforestation
CORRIDOR DIFFUSE FISHBONE GEOMETRIC
Colonization Small farms Planned Large farms
along roads settlement
and rivers
Image mining part II: finding spatial
configurations
Geometrical patterns – Terra do Meio (PA),
2003
Diffuse patterns – Terra do Meio (PA),
2003
Image mining results
Image mining results
What’s the behavior of large farmers in Terra do
Meio during this period (1997-2003)? Is the area
of new large farms increasing?
In 2000, this kind of deforestation reached a
peak of 55,000 ha, but decreased in the
following years. In 2003, the deforestation area
associated to large farms decreased to 29,000
ha. This indicates that large farms are reducing
their contribution to deforestation.
From Moving Objects to
Moving Regions
Real-time monitoring of Amazon
deforestation
Yearly estimates Deforestation maps Recent MODIS/WFI Ground Station
data
Detection of new deforestation
Web maps
External users
Deforestation betwen Landsat Image
13/Aug/2003 and 07/May/2004 13/Ago/2003
Deforestation Deforestation in
13/Ago/2003 until 13/Aug/2003 (yellow) +
deforestation from
07/Mai/2004 13/Aug/2003 until
07/mai/2004 (red)
Deforestation
Fifteen days later... 13/Aug/2003
(yellow) +
Deforestation on deforestation f
21/May/2004 13/Aug/2003 u
07/May/2004 (r
+ deforestation
21/May/2004
(orange)
From moving objects to moving regions
Desforestation areas detected in 07/21 May
(blue dots) + fire stops detected in 10/11 Jun
Query examples
Select all deforestation units and fire spots within these units that
occurred in the same year
Select all deforestation units and fire spots within these units, such
that the fire spots occurred up to 90 days after the deforestation
Select all deforestation units and fire spots within these units that
occurred at the same month of the same year
Select all deforestation units and fire spots within these units that
occurred at same month (of any year)
Select the time difference between the deforestation units and fire
spots within these units
TerraLib: Open Source
Tools for GIS Application
Development
www.terralib.org
Spatial Information Engineering
Technological change
Current generation of GIS
Built on proprietary architectures
Interface+function+database = “monolythic” system
Geometric data structures = archived outside of the
DBMS
New generation of object-relational DBMS
Alldata will be handled by DBMS
Standardized access methods (e.g. OpenGIS)
Users can develop customized applications
TerraLib: the support for TerraME
Open source library for GIS
Data management
object-relational DBMS
raster + vector geometries
ORACLE, Postgres, mySQL, Access
Environment for customized GIS
applications
Web-based cooperative development
http://www.terralib.org
TerraLib: Scientific Motivation
GIScience has brought us new concepts
Ontologies
Spatio-Temporal models
Uncertainty
Geocomputation
How do we build “proof-of-concept” prototypes?
Will GIScience be driven by the industry?
We need open source tools to share our results!
TerraLib: Open source GIS library
Data management
All of data (spatial + attributes) is in
database
Functions
Spatial statistics, Image Processing,
Map Algebra
Web-based co-operative
development
http://www.terralib.org
Operational Vision of TerraLib
DBMS
TerraLib
Oracle
Spatial Spatial Access Spatial
Operations Operations
Geographic API for
Application Spatial MySQL
Operations Postgre
SQL
TerraLib MapObjects + ArcSDE + cell spaces + spatio-temporal models
TerraLib applications
Cadastral Mapping
Improving urban management of
large Brazilian cities
Public Health
Spatial statistical tools for
epidemiology and health services
Social Exclusion
Indicators of social exclusion in
inner-city areas
Land-use change modelling
Spatio-temporal models of
deforestation in Amazonia
Emergency action planning
Oil refineries and pipelines
(Petrobras)
TerraLib Support for Cell Spaces
Cellular Space
TerraLib: Spatial
statistics
R- TerraLib interface: the case for strong
coupling
R data from geoR package.
R-Terralib interface
Loaded into a TerraLib database, and visualized with TerraView.
Wrap-up: databases for
Earth System Science
Computational Modelling and Databases
Design and implementation of
computational enviroments for
modelling
Requires a formal and stable
description
Implementation allow experimentation
Rôle of computer representation
Bring together expertise in different
field
Make the different conceptions explicit
Make sure these conceptions are
represented in the information system
The basic question
What are the different spatio-temporal data
types and how can we design an algebra for
spatio-temporal objects based on them?
How can these spatio-temporal data types be
represented in an object-relational DBMS?
What query languages and algorithms are
needed to handle ST data?
Spatio-temporal data types
Life Geometry Attributes Genealogy
Events Ephemeral Point Fixed -
Cadastre Long Polygon Variable Important
Fields Permanent Cell, Variable -
Polygon
Spatio-temporal Permanent Polygon Variable Important
patterns
Location based Permanent Point Variable -
services
Genealogy of ST objects
Earth System Computation Models: The role of
database community
What are the data structures we need for earth
system science?
How to handle spatio-temporal fields in
databases?
How to handle moving-regions in databases?
How to develop computational models for
spatio-temporal data?
Image database mining: research
challenges
How to handle effectively images in DBMS?
What types of patterns are important for remote
sensing image DB?
What pattern-finding algorithms capture changes
in images?
Geosensors: How can DB researchers
help?
We need a comprehensive semantics of spatial
data
What are spatio-temporal fields? What are the
associated data types, operations, data
structures and query and indexing schemas?
What is geosensor data? How to describe the
world based on samples in space and time?
Spatial modeling and spatial statistics
How can we merge spatial statistics
with ST DBMS?
How can we support nested CA and
cell spaces?
How do we model anisotropic
space?
What modeling languages are
suitable for ST DBMS?
Long-term challenges
Can our current STDBMS handle the challenges
of earth system modeling?
What STDBMS technology would handle earth
system modeling?
What knowlege processing tools are included in
our STDBMS?
What knowledge processing tools do we need
for the next generation of STDBMS?
Vision: from data to knowledge
fonte: NASA
“Give us some new problems”
We need databases for saving the planet