=Paper=
{{Paper
|id=Vol-1660/competition-paper3
|storemode=property
|title=Spatio-Temporal Ontology for Change Analysis of Flood Affected Areas Using Remote Sensing Images
|pdfUrl=https://ceur-ws.org/Vol-1660/competition-paper3.pdf
|volume=Vol-1660
|authors=Kuldeep R. Kurte,Surya S. Durbha
|dblpUrl=https://dblp.org/rec/conf/fois/KurteD16
}}
==Spatio-Temporal Ontology for Change Analysis of Flood Affected Areas Using Remote Sensing Images==
https://ceur-ws.org/Vol-1660/competition-paper3.pdf
Spatio-Temporal Ontology for Change
Analysis of Flood Affected Areas Using
Remote Sensing Images
Kuldeep R. KURTEa,1 and Surya S. DURBHAa,1
a
Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay,
India
Abstract. This article presents ST-IIM (Spatio-Temporal Image Information
Mining) – an ontology to model the spatio-temporal changes during a flood
disaster event. It is integrated with the image ontology to represent the various
characteristics of Remote Sensing (RS) images. The goal is to query, detect and
monitor disaster affected areas from multi-temporal images by automatic
reasoning. The spatial and temporal aspects were modeled using DL-safe rules
based on qualitative topological relationships and Allen's interval algebra
respectively. This enables to draw inferences about the temporal changes and
evolution of the Land Use/Cover (LU/LC) classes during the disaster event for
rapid disaster response and recovery activities.
Keywords. Spatio-Temporal Ontology, RCC8 topological relations, Allen's
interval algebra, Flood disaster ontology
1. Introduction
Natural disasters such as, flood, earthquake have deep impact on human life. Such
natural events cannot be prevented, but their impact can be mitigated and the loss of
human life can be minimized by preparing an action plan for the entire disaster
management cycle.
Remote Sensing (RS) data, captured by various Earth Observation (EO) satellites
plays a very important role in a disaster event, as it provides a synoptic view of the
affected area. During a disaster event, a series of RS images obtained from various RS
platforms can be processed and analyzed, and the information such as flood affected
area (during response phase), and Land Use/Cover (LU/LC) change over the flood
duration, flood recession pattern (during recovery phase) can be extracted. However,
the low-level features obtained from RS imagery are unsuitable to capture the high-
level semantic concept such as spatial patterns, spatio-temporal evolution of LU/LC
classes. Hence, an ontology is useful to conceptualize the domain knowledge, and to
reduce the semantic gap between low-level image features and high level spatio-
temporal semantics.
This paper describes the ontology development (ST-IIM) to model the spatio-
temporal changes specifically for flood disaster situation.
2. Design and Development of ST-IIM ontology
The ontology presented in this paper encodes the formal knowledge about the
qualitative spatial relationships between objects in an image as well as their temporal
1
Contact Authors: kuldeep.iitb@iitb.ac.in; sdurbha@iitb.ac.in
behavior in order to model, retrieve and detect the spatio-temporal changes. The Basic
Formal Ontology (BFO2) is imported as a base ontology and the domain knowledge
(LU/LC classes) and spatial and spatio-temporal knowledge is built upon it. The
ontology (ST-IIM) is developed in Web Ontology Language (OWL-DL) and is openly
available 3. The developed ontology is able to do reasoning about the subsumption
relationships among LU/LC classes, reasoning about spatial relations and spatio-
temporal changes during dynamic events such as flood disaster. The similar work has
been presented in [1] where a 4D-fluent approach is used to represent the evolution of
the temporal information in ontologies.
2.1. Encoding spatial knowledge
To understand the spatial configuration, the spatial relationships among image regions
need to be formally defined and encoded in an ontology. The Region Connection
Calculus (RCC8) defines a set of eight jointly exhaustive and pair wise disjoint (JEPD)
topological relationships (see Figure 1 (a)) [2]. These topological relationships are
defined as a hierarchy of object properties in ST-IIM ontology. For example,
Externally Connected (EC) subPropertyOf Connected (C) and Partially Overlap (PO)
subPropertyOf Overlap (O) are defined. Moreover, the characteristics such as,
symmetry, reflexivity, transitivity are defines for these topological object properties,
e.g. the object property EC and PO are declared as symmetric.
Figure 1. (a) RCC8 topological relations hierarchy [2] (b) Non-Tangential Proper Part (NTPP) example
Also in this work, image regions are assumed to be represented by their Minimum
Bounding Rectangles (MBRs) (see Figure 1(b)). The OWL classes, Region and
BoundingBox are defined in an ontology and connected via hasBBox object property.
The MBR representation of a region divides the space into 25 partitions (4 points, 12
lines and 9 region partitions) [3]. Hence, the MBR representation of two regions will
generate 169 exhaustive spatial configurations. This spatial knowledge is defined in
terms of topological relationships by developing their rule-based logical encoding
using Semantic Web Rule Language (SWRL) in ST-IIM ontology. These spatial rules
compares four datatype properties of two MBRs i.e. hasLeftLong (longitude of lower
left corner of MBR), hasLowerLat (latitude of lower left corner of MBR), hasRightLong
(longitude of upper right corner of MBR), hasUpperLat (latitude of upper right corner
of MBR), which is presently not possible using OWL-DL constructs, and hence SWRL
rules were used. A subset of 65 topological rules are developed and integrated into ST-
2
https://github.com/BFO-ontology/BFO/blob/v2.0/bfo.owl
3
http://home.iitb.ac.in/~kuldeep.iitb/ST-IIM.owl ; http://ontohub.org/fois2016_ontology_comp/ST-IIM
IIM to infer all 169 topological relationships. Table 1 shows example SWRL rules for
two topological relationships NTPP (Non-tangential Proper Part) and PO (Partially
overlap) (refer rules 1, 2).
2.2. Encoding Temporal knowledge
The time ontology published by W3C, available on-line4 is imported in ST-IIM (re-
usability) as a base ontology, as it has the taxonomy of the time classes already defined.
However, this basic ontology is not capable of performing reasoning over temporal
intervals. For example, if events and timestamps of those events are given, this
ontology will not be able to do automatic reasoning to retrieve image regions based on
their temporal interval relations. Hence, SWRL rules were developed to address this
issue. In this ontology, the major classes Instant, DateTimeInterval etc. are defined to
capture the temporal event's instant (e.g. flood event) and the valid time duration of that
event (e.g. flood duration).
Figure 2. (a) Pictorial representation of Allen's temporal relations (b) Example RS images showing
submergence and flood recession pattern of Road, River and other LU/LC classes
In ST-IIM the Allen's temporal algebra [4] is defined as object properties (see
Figure 2 (a)), which along with the encoded SWRL rules for temporal intervals can be
used to state the temporal relationships among two intervals (refer rules 3-6 in Table 1).
For example, whether an interval of submerged road in one part of a city is temporally
overlapping with any other interval of submerged roads in some other part of the same
city. Figure 2(b) shows example of such LU/LC evolution pattern.
Table 1. Example SWRL rules to model spatio-temporal changes (shows only two rules for each category)
No. SWRL Rules
Topological Relation SWRL rules
1 hasBBox(?R1, ?B1), hasBBox(?R2, ?B2), hasLeftLong(?B1, ?B1LeftLong),
hasLeftLong(?B2, ?B2LeftLong), hasLowerLat(?B1, ?B1LowerLat),
hasLowerLat(?B2, ?B2LowerLat), hasRightLong(?B1, ?B1RightLong),
hasRightLong(?B2, ?B2RightLong), hasUpperLat(?B1, ?B1UpperLat),
NTPP
hasUpperLat(?B2, ?B2UpperLat), greaterThan(?B1LeftLong, ?B2LeftLong),
greaterThan(?B1LowerLat, ?B2LowerLat), lessThan(?B1RightLong, ?B2RightLong),
lessThan(?B1UpperLat, ?B2UpperLat) -> NTPP(?B1, ?B2)
2 hasBBox(?R1, ?B1), hasBBox(?R2, ?B2), hasLeftLong(?B1, ?B1LeftLong),
hasLeftLong(?B2, ?B2LeftLong), hasLowerLat(?B1, ?B1LowerLat),
hasLowerLat(?B2, ?B2LowerLat), hasRightLong(?B1, ?B1RightLong),
hasRightLong(?B2, ?B2RightLong), hasUpperLat(?B1, ?B1UpperLat),
PO
hasUpperLat(?B2, ?B2UpperLat), equal(?B1UpperLat, ?B2UpperLat),
greaterThan(?B1LeftLong, ?B2LeftLong), lessThan(?B1LowerLat, ?B2LowerLat),
lessThan(?B1RightLong, ?B2RightLong) -> PO(?B1, ?B2)
Temporal relations SWRL rules
3 inXSDDateTime(?i1, ?t1), inXSDDateTime(?i2, ?t2), lessThan(?t1, ?t2) -> before(?i1, ?i2)
4
https://www.w3.org/2006/time
4 inXSDDateTime(?i1, ?t1), inXSDDateTime(?i2, ?t2), equal(?t1, ?t2) -> at(?i1, ?i2)
5 before(?i12,?i21), hasBeginning(?dt1,?i11), hasBeginning(?dt2,?i21), hasEnd(?dt1,?i12),
hasEnd(?dt2, ?i22) -> intervalBefore(?dt1, ?dt2)
6 before(?i11,?i21),before(?i12,?i22),before(?i21,?i12),hasBeginning(?dt1,?i11),
hasBeginning(?dt2, ?i21), hasEnd(?dt1, ?i12), hasEnd(?dt2, ?i22) -> intervalOverlaps(?dt1, ?dt2)
Spatio-Temporal SWRL rules
7 LULCRegion(?r12), StagnatedFloodWater(?r21), DateTimeInterval(?dt1),
DateTimeInterval(?dt2), hasFirstDefiningRegionOfInterval(?dt2, ?r21),
hasLastDefiningRegionOfInterval(?dt1, ?r12), hasPreviousTemporalNeighborhood(?dt2, ?dt1) ->
hasSubmergenceInterval(?r12, ?dt1), isSubmergedBy(?r12, ?r21)
8 NTPP(?b1,?b2), hasBBox(?r1,?b1), hasBBox(?r2,?b2), isSubmergedBy(?r1,?r2)->
isCompletelySubmergedBy(?r1, ?r2)
9 PO(?b1,?b2), hasBBox(?r1,?b1), hasBBox(?r2,?b2), isSubmergedBy(?r1,?r2) ->
isPartiallySubmergedBy(?r1, ?r2)
3. Representing spatio-temporal changes in flood disaster using ST-IIM
During flood disaster, in response phase, the pre-flood RS imagery can be used along
post-flood RS images to identify the submerged areas and underlying LU/LC classes
such as, flooded roads, flooded cropland, flooded built-up. During the recovery phase
(post flood disaster), a series of multi-temporal RS images captured during and after
the flood, and along with the few pre-flood images of same area can be analyzed
together to get more insight about the temporal behavior of flood. For example,
LU/LC submergence pattern, flood recession pattern etc (as shown in Figure 2(b)).
Such a information mining during recovery phase, provides very important insights for
the future flood mitigation and preparedness plans.
In this work, the class DateTimeInterval is considered as an important class, which
is used to denote a temporal interval during which an image region has been observed
with a specific LU/LC class. The object property hasObservedLULCFirstTime and
hasObservedLULCLastTime are defined as subPropertyOf hasBeginning and hasEnd
respectively. These properties have a class DateTimeInterval as domain, whereas a
class Instant as range. This interval is obtained by tracking an image object with a
particular LU/LC class in a temporal stack of RS images. Hence, a class
DateTimeInterval in ST-IIM holds the information about a persistence of an image
object with a particular LU/LC class along with its temporal interval. To achieve this,
two object properties hasFirstDefiningRegionOfInterval and
hasLastDefiningRegionOfInterval are defined to describe the regions which are
associated with the interval. These properties have range class LULCRegion, which
will indicate the LU/LC observed over an interval. It may happen that, the LU/LC of a
region in an image persists for some temporal interval and later changed to another
class forming another temporal interval. Such temporal interval instances can be
identified and connected via an object property hasPreviousTemporalNeighborhood,
which connects current and previous temporal intervals of a region.
3.1. Example Scenario: Detecting the spatio-temporal interactions between a road and
river during a flood event
Figure 3 shows an example of some temporal intervals which are assumed to be
observed during a flood event. As RS images are obtained at discrete time intervals,
the temporal intervals shown for different LULC classes are discrete. The example
shows, the temporal intervals of two image objects River and Road. After the flood has
occurred the object road has disappeared and submerged by the spread of flooded river-
a region with a new class called StagnatedFloodWater is evolved (refer Figure 2(b)).
The subsequent intervals shows that the River object has started receding before the
Road object resurfaces. Here, an interval I3 is connected with intervals I1, I2 via
hasPreviousTemporalNeighborhood object property. The SWRL rules are developed
and integrated into ST-IIM ontology to infer that, the Road and River regions in
interval I1, I2 isSubmergedBy a region in an interval I3. Moreover, the topological
relations among MBRs of interval defining region's can be used to find whether the
region is partially or completely flooded (refer Table 1 (rules 7-9)).
Figure 3. Shows the pre-post flood temporal intervals for Road, River and StagnatedFlood regions
(vertical dotted lines shows the discrete time instants when RS images are assumed to be acquired)
Representing a temporal interval as described above enables the use of Allen's
temporal algebra for comparing two intervals and do reasoning5 over them. This is
useful to find whether one interval is overlapping other or finishes with other. In the
case of flood disaster, comparing two intervals of FloodAffectedBuilding at two
different parts of the flood affected area can give the insight about the recession pattern
of the flood. Similarly, the post flood evolution of the submerged roads in an affected
area can be obtained by reasoning over these interval relations using rules defined in
this ontology.
Conclusion
The ST-IIM ontology to model spatio-temporal changes during a flood disaster event is
presented. Although flood disaster is chosen for demonstration, the formalism is
applicable to other spatio-temporal domains, where there is a need to understand the
dynamically changing events. The presented ontology can be further extended to
model Allen's temporal interval relations between spatial relations of image objects to
understand the evolution of spatial configuration over time.
References
[1] S. Batsakis and E. G. M. Petrakis, SOWL:Spatio-temporal Representation, Reasoning and Querying over
the Semantic Web, in: Proc. Of Int. Conference on Semantic Systems, 2010, no. 15, pp. 1-9.
[2] D. A. Randell, Z. Cui, and A. G. Cohn , A spatial logic based on regions and connection, in: Proc. of 3rd
Int. Conference on Principles of Knowledge Representation and Reasoning, 1992, pp. 165-176.
[3] D. Papadias and Y. Theodoridis, Spatial relations, minimum bounding rectangles, and spatial data
structures, International Journal of Geographic Information System 11 (1997), 111-138.
[4] J. F. Allen, Maintaining knowledge about temporal intervals, Communications of the ACM, (1983), 832–
843.
5
Pellet- OWL-DL reasoner is used to validate rules and developed ontology (http://clarkparsia.com/pellet)