=Paper=
{{Paper
|id=Vol-2977/paper1
|storemode=property
|title=Semantic Integration of Raster Data for Earth Observation on Territorial Units
|pdfUrl=https://ceur-ws.org/Vol-2977/paper1.pdf
|volume=Vol-2977
|authors=Ba-Huy Tran,Catherine Comparot,Cassia Trojahn,Nathalie Aussenac-Gilles
}}
==Semantic Integration of Raster Data for Earth Observation on Territorial Units==
https://ceur-ws.org/Vol-2977/paper1.pdf
Semantic Integration of Raster Data for Earth
Observation on Territorial Units ?
Ba-Huy Tran?? , Nathalie Aussenac-Gilles,
Catherine Comparot, and Cassia Trojahn
IRIT, Université de Toulouse, CNRS, Toulouse, France
firstname.lastname@irit.fr
Abstract. Raster is a common data format in satellite image process-
ing. A raster allows us to model geographic phenomena as a regular
surface in which each cell (or pixel) is associated with a phenomenon
value. Many rasters can be provided for the same geographic area, for
the same phenomenon at different dates or different phenomena; they
can be compared, combined, used to generate a new one, etc. A recurrent
issue however is to transfer data from pixels to features to qualify ter-
ritorial units, which requires complex aggregation processes. This paper
addresses this issue thanks to a semantic data integration process based
on spatial and temporal properties. We propose i) a modular and generic
ontology used for the homogeneous representation of data qualifying a
geographical area of interest; and ii) a Semantic Extraction, Transfor-
mation, and Load (ETL) process that relies on the ontology and data
extracted from rasters and that maps the aggregated data to the corre-
sponding areas. We evaluate our approach in terms of the (i) adaptability
of the proposed model and pipeline to accommodate different use cases,
(ii) added value of the generated datasets in helping decision making,
and (iii) approach scalability.
Keywords: Earth observation, semantic data integration, spatio-temporal,
change detection, land cover, NDVI
Earth Observation (EO) is a domain that has greatly evolved in the last
years thanks to large-scale Earth monitoring programs, such as the US Landsat
Programand the EU Copernicus Program In particular, with the Copernicus
program launched by the European Space Agency (ESA), EO satellites provide
users with free, reliable, and up-to-date Earth image data and metadata. The
availability of these data sources has opened the opportunity to better support
existing domain-oriented applications and to foster emerging new ones, from
agriculture to forestry, environmental monitoring to urban planning, climate
studies, and disaster monitoring. These sources of data, coupled with the devel-
opment of machine learning algorithms have boosted the image processing field
and their application in those different domains.
?
Supported by H2020 grant for the CANDELA project from an H2020 EC research
??
Copyright ©2021 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 B-H. Tran et al.
One common data format in satellite image processing is raster. A raster
models geographic phenomena as a regular surface in which each cell (or pixel)
is associated with an indicator or a phenomenal value according to a predefined
codification or classification. Such representations may be automatically built
by different types of algorithms including machine learning. Several rasters can
be provided for the same geographic area to monitor the same phenomenon
at different dates or different phenomena; they can be compared, combined,
used to generate a new one [19]. However, in a decision-making perspective, the
interpretation of their content requires higher-level representations associated
with features that bring meaning to the areas of interest on Earth.
This paper addresses the integration of data calculated from rasters as a
way of qualifying geographic areas of interest, based on their spatio-temporal
properties. These areas of interest are usually represented as geospatial features
in a vector format. We are interested in studying (i) the kind of ontology required
to support knowledge extraction from EO data and to describe homogeneously
different analysis results provided by the rasters; (ii) how to make accessible and
usable rich EO data and (iii) how to enable the data traceability to enhance user
confidence and data exploitation. The contributions of this paper are as follows:
(i) a generic vocabulary that allows the semantic and homogeneous description
of spatio-temporal data to qualify predefined areas (ii) a configurable semantic
ETL process based on the proposed model (iii) an EO eco-system that allows
for exploiting Sentinel images.
The rest of this paper is organized as follows. Section 1 presents the semantic
model for integrating geographic areas and data extracted from rasters, through
their spatial and temporal dimensions. Section 2 details the semantic data inte-
gration process. Section 3 discusses the experimental evaluation of our approach.
The main related work is discussed in section 4 and finally, section 5 ends the
paper and presents the perspectives for future work.
1 Semantic model
We propose a semantic model to represent data from rasters that provide ob-
served properties of a geographic area of interest (land parcel, administrative
unit, forest, etc.) along with their spatio-temporal dimensions. This model also
allows keeping track of integrated data and image metadata. To this end, it
relies on a generic and modular ontology that extends vocabularies available
as OGC standards such as GeoSPARQL, and W3C recommendations such as
OWL-Time1 , SOSA2 , DCAT3 , and PROV-O4 .
The model is composed of several sub-models describing the various kinds
of data without the need of instantiating the whole model. These sub-models
are a) a territorial observation model (for representing geospatial units and their
1
https://www.w3.org/TR/owl-time/
2
https://www.w3.org/TR/vocab-ssn/
3
https://www.w3.org/TR/vocab-dcat-2/
4
https://www.w3.org/TR/prov-o/
Semantic Integration of Raster Data 3
Fig. 1. Territorial Observation Model representing property values of territories calcu-
lated from EO rasters.
associated observations); b) an EO model (for representing Sentinel image meta-
data); and c) an EO analysis model (for representing raster results produced by
image processing). Due to the lack of space, only a) is depicted.
Territorial observation model (tom): The tom model (Figure 1) takes as
input any output of EO analysis activities as long as it comes in raster format. A
collection of observations (represented as tom:GeoFeatureObservationCollection)5
is composed of several observations (tom:GeoFeatureObservation), observing a
given property (sosa:ObservableProperty) on a given territorial . Territories, pre-
senting a footprint on Earth, are represented by the tom:GeoFeature class that
specializes the sosa:FeatureOfInterest and geo:Feature classes. They belong to
a type (tom:GeoFeatureType), such as an administrative unit (village, county),
land register parcel, agricultural parcel, forest, or geospatial data grid (such as
Sentinel tiles). Each observation result in the percent value (sosa:hasSimpleResult)
covered of the property among all observed properties in the collection to which
it belongs; for example, 40% of Mixed Forest and 60% of Coniferous Forest.
The prov-o:Entity class allows keeping trace (using the prov-o:wasDerivedFrom
property) of the raster file used to create the collections of observations on the
one hand and of the vector file used to create the territorial units on the other
hand (Cf the eoam module described below).
Sentinel images metadata (eom): The eom (EO model) is the part of
the ontology dedicated to representing metadata of Sentinel images. It mainly
describes the product (an image) as a result of a sosa:Observation. Each ob-
servation is associated with a temporal information, i.e., the date of capture
(sosa:phenomenomTime) and a spatial information, i.e., the area being cap-
5
The sosa:ObservationCollection class is an extension proposed in a current working
draft (https://www.w3.org/TR/vocab-ssn-ext/)
4 B-H. Tran et al.
tured (either by the geometry property of the eom:Product or through the
sosa:hasFeatureOfInterest relation).
EO analysis model (eoam): Sentinel images are consumed in different kinds
of analyses, such as machine learning algorithms that identify changes between
two images. The eoam model (EO Analysis Model) provides information about
the results of these activities since they are or will be consumed by the semantic
integration process. Thanks to the PROV-O vocabulary, it is possible to know
which Sentinel images have been used as input of a process (eoam:EOAnalysis),
and which agent (prov-o:wasAssociatedWith) realized it. DCAT is used to cat-
alog both the raster datasets and vector datasets. In this way, both raster files
(eoam:RasterFile) and vector files (eoam:GeoFeatureFile) are considered as dis-
tributions of these datasets. A raster distribution is detailed with temporal cov-
erage information (i.e. the dct:temporal and dct:Location properties provided by
DCAT), but also the spatial resolution (dcat:spatialResolutionInMeter ). A vec-
tor distribution describes the main attributes of the files, such as the file size
(dcat:byteSize), file format (dct:format), or used CRS (dct:conformsTo).
2 EO data analysis process
The EO data analysis pipeline is depicted in Figure 2. This pipeline is composed
of three main tasks:
– Satellite images processing and analysis: This task consists of process-
ing and analyzing satellite images coming from a DIAS (Data and Informa-
tion Access Services). The possible analysis could be from the simple one
as NDVI calculation to the sophisticated ones like change detection on time
series or land cover annotation. The task generates raster files as a result.
– Semantic data integration: The task extracts data from the generated
raster files using vector files that contain the territorial units to be ob-
served. Vector sources could come from open data repositories or our se-
mantic database through Semantic search.
– Semantic search: The task aims to analyze the integrated data by querying
the semantic database. The SPARQL query results can be used to perform
once again the first two tasks for further analysis, either as parameters for
guiding the process or as input data. The results can also be used by spe-
cialized GIS applications for detailed analyses of the whole integrated data.
The semantic data integration process can be divided into several steps, as
in an ETL process. The main steps of the process are described in the following.
Data extraction from rasters and vector files This step aims to extract
and structure data according to different purposes. The process requires two
sources: a vector and a raster. First, metadata are extracted. Next, the vector
file is processed to extract information about territorial units of a given type.
They will populate the tom:GeoFeature. The raster is also processed to qual-
ify each territorial unit contained in the vector file. Properties of interest (e.g.
Semantic Integration of Raster Data 5
Fig. 2. Overall EO data analysis pipeline.
mean values) are extracted through pixel aggregation or spatial masks are cre-
ated to eliminate undesired areas. Currently, we distinguish two types of raster,
depending on the type of their pixel values:
– Categorical rasters (as for land cover): in this kind of rasters, since a pixel
value represents a class (vineyard, for example), additional information is
needed to decode it. For example, the value 15 is decoded as a vineyard in
a CESBIO land cover raster;
– Continuous rasters (as for NDVI or change indicators): a pixel value will be
automatically classified into a level (or class) such as Very low, Low, Middle,
High, or Very high.
To qualify a territory unit from a raster, either new properties (e.g., mean
values) are extracted through pixel aggregation, or spatial masks are created to
eliminate undesired areas.
Data transformation This step aims at transforming the processed data into
the semantic one. Templates that define the mappings between the extracted
data structure (in JSON) and the ontologies are used as a basis in this process.
They are usually handwritten. While different data translation tools exist, such
as D2RQ6 , Ultrawrap7 , Morph8 , Ontop9 , TripleGeo10 or GeoTriples11 , we chose
to adapt the mapping template and processing mechanism described in [3]. This
choice is motivated by the fact that it contains functions helping to perform
more sophisticated operations, especially feature masking. The output of this
step is a set of RDF files.
Data load The final step consists of importing the RDF files into the triplestore,
following a materialization approach. The advantage of such an approach is to
facilitate future processing, analysis, or reasoning on the materialized RDF data.
6
http://d2rq.org/
7
https://capsenta.com/ultrawrap/
8
http://mayor2.dia.fi.upm.es/oeg-upm/index.php/en/technologies/315-morph-rdb/
9
http://ontop.inf.unibz.it/
10
https://github.com/GeoKnow/TripleGeo
11
http://geotriples.di.uoa.gr/
6 B-H. Tran et al.
3 Experimental evaluation
3.1 Application use cases
Two use cases of the CANDELA project have been chosen for demonstration:
– Vineyard use case: The objective of the use case is to retrieve changes in
vineyards that were damaged by natural hazards such as frost or hail. The
area of study is located in the Aquitaine region, in France. The vineyards of
the area were reported heavily damaged by frost on the 20th of April 2017.
– Urban expansion use case: The use case aims at studying the changes
related to urban expansion in agricultural areas. We study changes in villages
around Bordeaux city, one of the largest cities in France, and it is surrounded
by agricultural areas, between 2017 and 2020.
Both use cases share a common set of raster types:
– Change indicator: Change indicators, representing the probability of changes
(between 0 and 1) of pixels of two Sentinel images, are obtained by executing
tools from partners of the project.
– NDVI: NDVI information is obtained by processing near-infrared and red
sensors of Sentinel images. The output values are between -1 and 1. Since
the values between -1 and 0 represent the elements composed of water, these
values are set to 0 so that the rasters only contain values between 0 and 1.
– Land cover: The datasets provide land cover information given an area on
Earth. The CESBIO land cover datasets12 are used for our use cases. They
cover the French territory with a spatial resolution of 10m2 .
Regarding the territorial units (vector data), land register data is used for
the first use case while the second consumes administrative unit data:
– Land register: Land register data is available from the French government
data website13 in GeoJSON format or shapefiles.
– Administrative unit data: Information of villages inside an area of interest
can be obtained from the French government website14 . The datasets are
available in shapefiles and are updated yearly. Administrative units are linked
to the INSEE RDF database15 .
3.2 Model genericity
The genericity of the model is proved by the facts that: (i) The model treats
in the same manner whatever the raster datasets, and their versions, as long as
they exist in the right format; (ii) Different classifications can be used to observe
the same type of EO property; (iii) The system can consume whatever the vector
source describes any territorial divisions.
12
http://osr-CESBIO.ups-tlse.fr/ oso/
13
https://cadastre.data.gouv.fr/datasets/cadastre-etalab
14
https://www.data.gouv.fr/en/datasets/decoupage-administratif-communal-
francais-issu-d-openstreetmap
15
https://rdf.insee.fr/
Semantic Integration of Raster Data 7
3.3 Pipeline genericity
Since the components of the pipeline are organized as services (python functions
and docker images), the users can customize the pipeline by choosing and chain-
ing up as many services as they like through Jupyter notebooks of the platform.
– Vineyard use case: (i) we first obtain all parcels of the Saint Emilion village
from the land register data and CESBIO land cover raster for 2017. Semantic
data integration is next used to integrate land cover and parcel information.
(ii) Vineyard parcels inside the villages are retrieved via Semantic search. (iii)
Appropriated Sentinel-2 images are used for NDVI calculation. (iv) These
images are also used for change detection. (v) The generated rasters from
the 3rd and 4th steps along with the vector from the 2nd step are integrated
into the semantic database. (vi) Finally, semantic search can be used for
analyzing all integrated information related to the vineyard of interest.
– Urban expansion use case: (i) We first select adapted Sentinel images and
execute NDVI calculation. (ii) These images are also used for change detec-
tion. (iii) We obtain vector data of all villages of the Gironde department.
Semantic data integration is next launched using the raster generated from
the previous step and the obtained vector files. iv) Finally, we perform a
semantic search to analyze the integrated data related to the villages of
interest.
Fig. 3. Analyse on the vineyards during the period 19th April 2017 - 29th April 2017.
Left: Change level detected. Right: Degradation of NDVI.
The semantic data integration is configurable with a set of parameters that
can be provided as raster metadata or as function parameters. Users can also
provide custom thresholds for class classification since the results of image pro-
cessing and analysis may highly depend on the scenarios.
8 B-H. Tran et al.
3.4 Use cases analysis
We evaluate the result obtained using the pipeline presented in 3.3, knowing
that the results highly depend on the precision of the algorithms provided by
our partners.
– Vineyard use case: Two Sentinel-2 images collected on the T30TYQ tile
are used for change detection and NDVI computation; they are respectively
dated 2017/04/19 and 2017/04/29: the choice of these images is based on
the fact that they have very low cloud cover (0% and 15%) and the interval
between these observations covers the period of study.
Figure 3 represents an overview of the change levels detected and the degra-
dation of NDVI The Very low change level is eliminated since it’s not very
relevant. The NDVI degradation indicator represents the total percentage of
degradation of five NDVI levels. We also eliminated parcels having the NDVI
degradation below 20% . Finally, there are 858 parcels detected as having
changed, 756 parcels detected as having NDVI degradation above 20%, and
510 parcels detected in both cases.
– Urban expansion use case: For NDVI calculation and change detection,
it is recommended to collect images in the same period and in summer
to limit the cloud cover and the influence of vegetation growth. So, two
Sentinel-2 images were collected on 2nd August 2017 and 6th August 2020
and have 0% of cloud cover. Figure 4 (right) represents an overview of the
change levels detected and the NDVI levels degraded between these two
dates, together with the source Sentinel images (left). We can observe that:
(i) the change levels detected match quite well with the degraded NDVI due
to urbanization; (ii) the more the village approaches the city, the more it
is changed. The next analysis could be comparing change, NDVI and land
cover information at parcel level for particular villages.
Fig. 4. Analyse on urban expansion (2017-2020) at village level. Left (Sentinel images).
Right: village INSEE code, change and NDVI indicators.
Semantic Integration of Raster Data 9
3.5 Approach scalability
In terms of triplestore, we first opted for Strabon16 for its many advantages.
It has a good overall performance thanks to particular optimization techniques
that allow spatial operations to take advantage of PostGIS functionality instead
of relying on external libraries [16]. For complex applications that include both
spatial joins or spatial aggregations, Strabon is the only RDF store that performs
well [13]. While Strabon performed well when considering a reduced number
of triples in the data store, we faced scalability problems when increasing the
number of triples. Indeed, the same remarks are reported in [17]. For these
reasons, we thus examined query federation approaches where the data is stored
in different triplestores. In our case, Strabon is used only for spatial data and
GraphDB17 for the rest. Preliminary experiments showed several advantages of
this option: (i) faster response time for non-spatial queries (ii) scalable and robust
as it ensures the result regardless of the amount of integrated data. In terms of
the size of the generated RDF datasets, we populated 0.5M geofeatures (about
2.5M triples) in Strabon and 4M observations (97.5M triples) in GraphDB using
their API. The processing and loading time on a virtual node (4 cores CPU at
2.6GHz, 8GB of RAM, and SSD disk) was about 40 hours.
4 Related Work
4.1 Semantic ETL and models for EO data integration
Different semantic ETL proposals have addressed the transformation and in-
tegration of (open) EO data to LOD. In [20], the authors model EO imagery
as a data cube with a specific place and time, thanks to the W3C RDF Data
Cube (QB) ontology [9]. This model combines standard vocabularies such as
SSN, OWL-Time, SKOS, and PROV-O. In [14], QB was used to publish tabular
time series data and to structure it into slices that support multiple views on
the data. As a spatio-temporal data cube, the semantic EO data cube [5] con-
tains EO data where for each observation at least one nominal interpretation is
available. Following a semantic ETL approach along with ontology-based data
access (OBDA), [7] extends Data Cube, GeoSPARQL, and OWL-Time ontolo-
gies to offer access to Copernicus services information. Here, SOSA is adopted
to represent observation collections but alignments18 exist between SOSA and
QB. Closer to us, [11] defined an ETL process to integrate EO image and ex-
ternal data sources, such as Corine Land cover, Urban Atlas and Geonames.
The process is carried out based on their SAR ontology. Another close work in
terms of datasets is from [18], where data is integrated and published as LOD
based on an ontology, called proDataMarket. Three data sources, the Spanish
land parcel identification system, Sentinel-2 satellite, and LiDAR flights, are
16
http://strabon.di.uoa.gr/
17
https://graphdb.ontotext.com/
18
https://www.w3.org/TR/vocab-ssn/
10 B-H. Tran et al.
integrated. In [1], satellite images are classified and enriched with additional se-
mantic data to enable queries about what can be found at a particular location.
In our approach, an exploitation of our integrated data could also be facilitating
the image search. Finally, while we do not fully address the scalability of our
approach, several works are addressing the issue of managing large volumes of
EO data.
4.2 Processing of raster data in a semantic framework
Raster data can be represented following two approaches: either by treating the
entire raster grid as coverage or by providing procedures to extract vector objects
from the raster matrix. The first approach relies on constructing a semantic
representation of the raster pixels so that each pixel attributes (geometry, values)
are maintained. In [2] the RDF Grid coverage ontology is designed to allow a
native integration of coverage in RDF triplestores. The gridded structure of the
data is preserved and can be queried using SciSPARQL. Recently, the Ontop-
spatial extension [8] has been developed to process raster data and create virtual
geospatial RDF views above it.
The second approach consists of extracting entities from rasters and repre-
senting them as ontological features. These entities are sets of raster elements
(i.e. sets of pixels) that meet a certain context-dependent definition. In [4], the
approach integrates and processes vector and raster data from LOD repositories
using vectorization and mathematical tools for geo-processing. First, bounding
boxes for input raster are used to query LOD endpoints for entities correspond-
ing to a certain concept. The returned entities along with their geometry are
next used to select raster pixels for supervised training based on content-based
descriptors. Finally, the results can be vectorized and inserted back into the
original repository. In [11], the approach proposes to restructure the images in
patches that have a fixed size, on which external information is associated. Each
patch is directly transformed into a feature based on the ontology. [12] extends
current standards to represent raster geo-data. A region of interest is first poly-
gonized and then the data is transformed into a semantic representation using
R2RML mapping rules. This workaround is arguably not a complete solution to
represent raster files in RDF as the original geometric source of the data is not
preserved [15]. Here, the areas of interest are predefined, hence the geometries
(polygons) are known. Another close work is from [10], where a raster allows
for modeling geographic phenomena as a regular surface in which each cell (or
pixel) is associated with a phenomenon value. However, they store each value
associated with a pixel corresponding to an observation. Here, we aggregate the
values of a region. While their modeling is close to ours in terms of reused vo-
cabularies, they do not represent metadata and provenance nor exploit Sentinel
images. Finally, our work adapts the one presented in [6] in several ways, with
a different focus on the pipeline that generates RDF data from EO rasters and
other data sources. Moreover, we do not need to consider versions of adminis-
trative units, and we explicitly refer to satellite images thanks to which we can
compute new indices
Semantic Integration of Raster Data 11
5 Conclusion
This paper presented an approach for the integration of data calculated from
rasters as a way of qualifying territorial units, based on their spatio-temporal
features. We proposed a modular and generic ontology for semantically and ho-
mogeneously describing spatio-temporal data that qualify predefined areas, to-
gether with the provenance of all sources of data. This ontology is generic enough
for describing data that can be calculated from any source that conforms to a
raster format. We defined a configurable semantic ETL process guided by this
ontology. The process extracts data from rasters and links observations to ter-
ritorial units through their spatio-temporal dimensions. This process produces
a semantic database that can be exploited for different purposes. We illustrated
the approach by integrating various raster datasets for two use cases of a joint
project. As future work, we plan to exploit big data scenarios with the manage-
ment of Natura 2000 areas19 . We also consider extending the proposed approach
to deal with data coming from CSV, especially, weather observation data. Finally,
we would like to publish our knowledge base as linked open data for scientific
purposes.
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