Semantic description of Satellite
Earth Observation Time Series

Daniela F. Milon-Flores<fnref n="1" />, Camille Bernard<fnref n="1" />,
Jérôme Gensel<fnref n="1" />

Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France

daniela.milon-flores@univ-grenoble-alpes.fr; camille.bernard@univ-grenoble-alpes.fr; jerome.gensel@univ-grenoble-alpes.fr

RÉSUMÉ. Cet article présente l'ontologie SeTT (pour Semantic environmental Trajec-
tory of Terrotory) construite dans le but de décrire et synthétiser, dans le web des don-
nées ouvertes et liées (LOD), des séries temporelles d'indices satellitaires, tels que le
NDVI (Normalized Difference Vegetation Index), indices difficiles à appréhender par
tout un chacun. L'ontologie SeTT offre différents niveaux d'annotations sémantiques
de ces séries temporelles, la forme ultime étant une trajectoire sémantique, et prévoit
d'immerger ces trajectoires dans le web des LOD afin de les relier à des informations
contextuelles pouvant expliquer les tendances environnementales décrites.

MOTS-CLÉS: Web sémantique, ontologie, trajectoire sémantique, observation satellite de la terre, série temporelle

KEYWORDS: Semantic Web, Ontology, Semantic trajectory, Satellite Earth Observa-
tion, Time Series

1. Introduction

Thanks to initiatives such as Landsat <fnref n="1" /> and Copernicus Sentinel <fnref n="2" /> programs, satellite data time series are now available and openly accessible, facilitating the monitoring of changes in land use and land cover. These datasets are used to derive satellite-based indices, presented in the form of Earth Observation Time Series (EO-TS). For instance, NDVI (*Normalized Difference Vegetation Index*) is a satellite-derived index that quantifies vegetation density, with values ranging from -1 to 1 (Appel, Pebesma, 2019). These indices help experts to analyze environmental changes. However, for non-expert audiences, the lack of semantics makes data difficult to understand and use (Nativi *et al.*, 2017; Giuliani *et al.*, 2019). Without explicit metadata and contextual information, interpreting index measurements and analyzing their temporal evolution be-

<footnote n="1">1. landsat.gsfc.nasa.gov</footnote>

<footnote n="2">2. copernicus.eu/en</footnote>
comes even more challenging. Expertise is required to interpret value varia-
tions and identify correlations between satellite-derived indices. This raises the
question of how to effectively transfer EO data to spatial planners, local sta-
keholders, or citizens in a manner that allows for pre-analysis, synthesis, and
easy interpretation of EO-TS data and its evolution over time. To enhance
the interpretability of EO-TS data for a broader audience, we propose in this
article an ontological model, called SeTT (i.e., *Semantic environmental Tra-
jectory of Territory*) that exploits Semantic Web (SW) technologies (Hogan,
2020; Cyganiak et al., 2014) and standards ontologies such as the RDF Data
Cube (Richard Cyganiak, Tennison, 2014), OWL Time (Hobbs, Pan, 2006) and
GeoSPARQL (Open Geospatial Consortium, 2012) to describe trends in EO-TS
as Semantic Trajectories. The following sections present the works related to
this research, followed by the SeTT Ontology and a conclusion to this article.

## 2. Related Work

In order to study and describe the evolution of diverse entities within the Semantic Web, the use of semantic trajectory models has become widespread, spanning a multitude of domains (e.g., sociology, medicine, geography). Thus, the term *Semantic Trajectories* has been extended to encompass new objects of study beyond GPS traces, such as the career trajectory of a person (Thériault et al., 1999) or the trajectory of a patient (Mannhardt, Blinde, 2017). In (Yan et al., 2009), several semantic layers have been defined in order to construct semantic trajectories step by step. This Multi-Layer trajectory model begins by identifying *raw trajectories* in the data: this step involves cleaning the data (e.g., removing outliers) and then cut the cleaned sequential data into several segments, where each segment represents a separate raw trajectory. Then, *structured trajectories* are constructed. They synthesize the raw trajectories into a series of consecutive episodes and events. An *Event* denotes the end of an *Episode* and the beginning of a new one. Finally, *semantic trajectories* extend the structured trajectories with semantic information, including metadata that offers additional context to the episodes and events. In this article, building on our previous work in the field of semantic life trajectories of individuals (Noel et al., 2017) and inspired by the work of (Camara, 2022) (they introduce initial concepts for an ontology to describe land use change but it has not been further developed to date), we propose a multi-layered semantic trajectory model for the description of the environmental evolution of territories. We present this ontological model in the following section.

3. The SeTT Ontology

In this section, we present the SeTT Ontology (i.e., *Semantic environmental
Trajectory of Territory*), we developed to describe the environmental evolution
of territories. It is based on the representation of semantic environmental trajec-
tories and leverages Linked Open Data (LOD) Cloud technologies. An overall representation of the SeTT concepts and relations is depicted in Figure 2. The definitions of all concepts within this ontology are available online in a technical documentation<fnref n="3" />. The SeTT model is a multi-layer ontology specifically designed in order to describe the evolution through time of a portion of land on earth i.e., a Territorial Unit (TU) (please see the *Territorial Unit* concept at the top of the Figure 2), delineated either for the purpose of territorial analysis or by a human group administering the area (e.g., a municipality within the French territory).

<figure><img src="image_1.png" /><figcaption>FIGURE 2. Main concepts of the SeTT Ontology.</figcaption></figure>

<caption>FIGURE 1.</caption>

As in (Yan et al., 2009), several semantic layers have been defined in order to construct a semantic environmental trajectory step by step. The initial processing stage, and the first level in our ontology, involves isolating the *raw trajectories*. Each raw trajectories correspond to one time series of a satellite-derived index for an observed TU (all the pixel values from the satellite images are aggregated by TU) over a specific period, cleaned of outliers and low-quality data and converted in the Semantic Web using the standard RDF Data Cube ontology (Richard Cyganiak, Tennison, 2014). The RDF Data Cube ontology

<footnote n="3">3. https://danielafe7-personal.github.io/TheSeTTOntology/</footnote>
is particularly well-suited for this purpose, as it enables the indexing of each
satellite index observation according to spatial, temporal, and thematic dimen-
sions (Milon-Flores et al., 2024). The second processing step involves identifying
trends (Segment concept) and breakpoints (Transition Point concept) within
the raw trajectories and characterizing the resulting trends using a generic
vocabulary comprising terms such as "increase" or "decrease" (SlopeDirection
concept) . These segments can be automatically detected using various me-
thods. For instance, we are currently experimenting with the segmentation of
satellite indices using BEAST (Zhao et al., 2019) and CCDC (Zhu, Woodcock,
2014). Thus, our ontology also includes an extensive lexicon for describing the
algorithm used for TS segmentation, along with its hyperparameters, ensu-
ring the reproducibility of results. When annotated and enriched, segments
and transition points are described as Episodes and Events that compose a
Thematic (semantic) Trajectory. By adopting a multiple thematic viewpoints
(i.e., Theme concept) in the ontology, one can model several thematic trajec-
tories associated with the same TU across different environmental domains,
such as Vegetation, Urbanization, Temperature, Water, and Snow, considering
that each of these themes may involve multiple satellite indices or other data
sources. At this high semantic level, the description of the increasing slope of a
trend takes the form of "warming" when describing temperatures, or "greening"
when describing vegetation. Additionally, the episodes and events described at
this thematic level serve as connection points within the LOD cloud, linking
to information that can further explain and characterize the environmental
trajectory.

4. Conclusion and Future Work

In this article, we introduced the SeTT Ontology, specifically designed to
describe environmental trajectories derived from satellite data. The next step
focuses on the automatic detection and enrichment of episodes and events wi-
thin these time series, leveraging algorithms like BEAST. Our final objective is
to build a Knowledge Graph, based on EO data extracted from the Swiss Data
Cube 4, within the framework of a research collaboration between France and
Switzerland (ANR PRCI TRACES).

5. Aknowledgment

TRACES French-Swiss PRCI project. Funded by ANR and FNS. Website:
traces-anr-fns.imag.fr/

<footnote n="4">4. https://www.swissdatacube.org/</footnote>
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