Earth Observation Data Mining: A Use Case for Forest Monitoring

Dumitru, Corneliu Octavian; Schwarz, Gottfried; Pulak-Siwiec, Anna; Kulawik, Bartosz; Lorenzo, José María Montero; Datcu, Mihai

doi:10.1109/igarss.2019.8899135

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Following the data triplification process that we defined, additional information can be attached to parcels throughout their life so that they can be referred to in the analysis process. We will consider, as well, integrating other land cover datasets, such as the one from [33]. Finally, another direction is to consider big data scenarios (using Natura 2000 data for instance) and to evaluate the scalability of the approach, including the possibility of considering hybrid architecture and storage strategies (with relational, NoSQL and RDF storage supports).…”

Section: Discussionmentioning

confidence: 99%

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Tran

Aussenac-Gilles

Comparot

et al. 2020

IJGI

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Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way of qualifying geographic units through their spatio-temporal features. We propose (i) a modular ontology that contributes to the semantic and homogeneous description of spatio-temporal data to qualify predefined areas; (ii) a Semantic Extraction, Transformation, and Load (ETL) process, allowing us to extract data from rasters and to link them to the corresponding spatio-temporal units and features; and (iii) a resulting dataset that is published as an RDF triplestore, exposed through a SPARQL endpoint, and exploited by a semantic interface. We illustrate the integration process with raster files providing the land cover of a specific French winery geographic area, its administrative units, and their land registers over different periods. The results have been evaluated with regards to three use-cases exploiting these EO data: integration of time series observations; EO process guidance; and data cross-comparison.

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Section: Discussionmentioning

confidence: 99%

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Tran

Aussenac-Gilles

Comparot

et al. 2020

IJGI

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“…A framework for forest monitoring has been developed using data mining techniques to detect possible changes from the multispectral data (as acquired from Sentinel-1 and Sentinel-2 from EO Synthetic Aperture Radar), and is applied effectively in two study areas, namely, (i) the Bory Tucholskie National in Poland and (ii) a highly diverse forested region in Romania (Dumitru et al, 2019). Its outputs of forest change maps of the damaged areas and the repetitive maps of forest preservation which help in forest monitoring or disaster management.…”

Section: Lulc Classificationmentioning

confidence: 99%

Multispectral data mining: A focus on remote sensing satellite images

Lim,

Sreevalsan‐Nair,

Daya Sagar

2023

WIREs Data Min & Knowl

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This article gives a brief overview of various aspects of data mining of multispectral image data. We focus on specifically the remote sensing satellite images acquired using multispectral imaging (MSI), given the technology used across multiple knowledge domains, such as chemistry, medical imaging, remote sensing, and so on with a sufficient amount of variation. In this article, the different data mining processes are reviewed along with state‐of‐the‐art methods and applications. To study data mining, it is important to know how the data are acquired and preprocessed. Hence, those topics are briefly covered in the article. The article concludes with applications demonstrating the knowledge discovery from data mining, modern challenges, and promising future directions for MSI data mining research.This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining

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“…Another application is to check information from various sources, including the results provided by our partners. We used the land cover annotation dataset obtained by interactive data mining algorithms on satellite images [30], developed by DLR (German Aerospace Center). For example, it is possible to compare the CESBIO land cover information with the land cover annotated by DLR or to justify the change detection result based on land cover change.…”

Section: Data Cross-verification Thanks To Data Integrationmentioning

confidence: 99%

Semantic Integration of Raster Data for Earth Observation on Territorial Units

Tran

Aussenac-Gilles

Comparot

et al. 2022

IJGI

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Semantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image processing algorithms, a recurrent problem is transferring the pixel values of these rasters into features that make sense of the areas of interest on the Earth, thus facilitating the interpretation of their content. This paper addresses this issue through a semantic data integration process based on spatial and temporal properties. We propose (i) a modular and generic semantic model for the homogeneous representation of data qualifying a geographical area of interest thanks to territorial units (land parcels, administrative units, forest areas, etc.) that we define as divisions of a larger territory according to a criteria in relation with human activities; and (ii) a semantic extraction, transformation and load (ETL) process that builds on the model and the data extracted from rasters and that maps aggregated data to the corresponding unit areas. We evaluate our approach in terms of the (i) adaptability of the proposed model and pipeline to accommodate different use cases (vineyard and urban expansion monitoring), (ii) added value of the generated datasets to assist in decision making, and (iii) scalability of the approach.

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Earth Observation Data Mining: A Use Case for Forest Monitoring

Cited by 4 publications

References 4 publications

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Multispectral data mining: A focus on remote sensing satellite images

Semantic Integration of Raster Data for Earth Observation on Territorial Units

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