Pith. sign in

REVIEW 3 major objections 4 minor 43 references

GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper builds GeoOutageKG, a queryable knowledge graph that links county-level power outage reports, nightly satellite imagery, and derived outage maps so outage severity can be analyzed jointly at multiple spatial and temporal…

desk verdict Solid KG construction with a genuine integration artifact, but the unvalidated OutageMap modality and overbroad claims need revision before the resource is trusted. read the letter →

arxiv 2507.22878 v1 pith:4Z3DXENB submitted 2025-07-30 cs.IR cs.CLcs.CY

Ethan Frakes , Yinghui Wu , Roger H. French , Mengjie Li This is my paper · ORCID
classification cs.IRcs.CLcs.CY
keywords knowledgegraphpoweroutageontologynighttimelightimagerytimeseriesgeospatiotemporalmapmultiresolutionanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper's central claim is that a knowledge graph can bind three complementary outage data sources into one queryable resource: county-level outage reports with 15-minute resolution, nightly satellite images with fine spatial detail, and derived outage maps for hurricane events. The authors build GeoOutageKG for Florida, aligning each source to an ontology called GeoOutageOnto, and report over 10.6 million outage records, 313,702 nighttime light images, and 15,544 outage maps. If the claim is right, analysts can ask one question and get all three modalities for a county and time window, enabling outage severity evaluation, energy access disparity analysis, and grid decision support.

What carries the argument

The object that carries the argument is GeoOutageOnto, an OWL2 ontology whose three subontologies correspond to the three data modalities: OutageRecord, NTLImage, and OutageMap. Each instance is tied to a county through a representsCounty relation, the images and maps are typed as Image resources, and satellite and sensor information is linked through fromSatellite and fromSensor relations. This shared spatial and temporal grounding is what lets a single query retrieve, for a specified county and date range, the raw nightly image, the derived outage map, and the corresponding outage records. The knowledge graph is stored as RDF/Turtle and exposed through a SPARQL endpoint.

What would settle it

For a set of county-date pairs in GeoOutageKG, count how often large dark areas in an OutageMap occur while the corresponding OutageRecord shows few or no customers out; if the mismatch is frequent and systematic, for example during moonlit or cloudy nights with no reported outages, the derived maps cannot anchor the analysis.

Watch

Extended reading notes

Core claim

The discovery is that heterogeneous outage datasets can be semantically unified into a single RDF knowledge graph without forcing them into one spatial or temporal grid. GeoOutageKG represents each modality as its own ontology class: OutageRecord for the time series, NTLImage for nightly county-masked satellite imagery, and OutageMap for derived outage severity maps. Each instance is tied to a county and date, so a query about a specific place and time window can return reported customer outages, raw imagery, and derived maps together. The authors demonstrate this with a SPARQL query for Lee County around Hurricane Ian that retrieves all three modalities and filters for outage surges above 100 customers.

Load-bearing premise

The outage maps come out of a model that compares nightly satellite brightness to the average of the previous three months, and they are stored as real outage observations even though the paper reports no check of those maps against ground truth.

Editorial extensions

If this is right

  • A single SPARQL query can join the three modalities for a county and storm window, so severity can be assessed jointly from imagery and reported outages rather than from one source.
  • The resource currently covers all 67 Florida counties over roughly a decade, with outage records from 2014 to 2024 and imagery from 2012 to 2025, giving enough breadth for historical multiresolution analyses.
  • Because the schema reuses existing linked-data vocabularies for administrative regions, satellites, sensors, and image media, adding new data types such as weather, infrastructure, or demographics does not require redesigning the graph.
  • The same construction pipeline can be applied to other states or national subdivisions, since the ontology is not Florida-specific.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Given the 15-minute cadence of the outage records and the daily cadence of the imagery, one extension the paper does not develop is using the graph to interpolate sub-county outage extent between satellite overpasses.
  • A natural next test would be to compute the overlap between dark regions in OutageMap instances and county-level outage counts across all hurricane windows; a strong correlation would validate the maps, while a null result would suggest treating that modality as provisional.
  • If the maps do hold up, the same ontology could be repurposed for non-hurricane outage causes, such as winter storms or heat-driven grid failures, since the schema separates event context from the three data modalities.
  • The paper floats a large-language-model retrieval interface as future work; nothing in the design blocks building a natural-language query layer over the SPARQL endpoint and testing it against direct SPARQL answers.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper describes the construction of GeoOutageKG, a knowledge graph for Florida power outage analysis that integrates three data modalities: county-level EAGLE-I outage time series (about 10.6 million records, 2014-2024), NASA Black Marble nighttime-light images (about 314,000 county-night images, 2012-2025), and derived outage maps (about 15,500) generated by a VST-GNN baseline-difference method. The authors present GeoOutageOnto, an OWL2 ontology reusing DBpedia, GEOSatDB, and W3C Media Resources, report instance and triple statistics, and illustrate querying with a SPARQL example for Hurricane Ian. The resource is publicly available with code and ontology documentation.

Significance. The main contribution is the semantic integration and public release of disparate outage datasets, with substantial curation effort visible in the statistics and in the reuse of established vocabularies. Reproducibility is supported by the GitHub and OSF repositories. However, the headline claims of improved outage analysis and multiresolution reasoning are not yet demonstrated: no evaluation against single-modality baselines is provided, and the derived OutageMap modality is unvalidated. The resource is potentially valuable, but its central value proposition requires additional validation and, in places, more modest or better-supported claims.

major comments (3)
  1. [§3.2, §4.2] The OutageMap class is a primary modality but is generated without validation. Section 3.2 states that outage maps come from 'Aparcedo et al.'s VST-GNN model ground-truth visualizations' by comparing daily radiance with the three-month mean. No error analysis or comparison with EAGLE-I records (already present in the same knowledge graph) is given, and the Limitations section does not acknowledge this. Because the severity use case in Section 5.1 treats these maps as valid outage representations, the 'robust multimodal data integration' claim is unsupported at a load-bearing point. I request either a direct validation of a sample of OutageMap instances against EAGLE-I records or a clearly stated uncertainty and limitation discussion, together with an assessment of what the integration adds beyond the raw NTL data.
  2. [Abstract; §5] The abstract and introduction state that integrating these sources 'can substantially improve power outage detection, analysis, and predictive reasoning,' but Section 5 only shows a SPARQL query that returns records, images, and maps for Lee County. There is no experiment, baseline, or quantitative metric demonstrating improvement over single-modality analysis. Either add such an evaluation or revise the claims to describe the resource as enabling queries rather than as demonstrated to improve detection, analysis, or prediction.
  3. [§3.2, §3.3, §5.1] The claimed 'multiresolution' capability is not implemented as queryable sub-county knowledge: NTLImage and OutageMap instances are county-level image objects with only frame metadata and a locator, and the example query in Figure 4 binds counties, not pixels or sub-county geometries. The text says satellite data can reveal 'sub-county outage patterns,' but the graph's SPARQL interface is not shown to expose such granularity. Please clarify whether pixel-level or geometry-based querying (for example via GeoSPARQL) is supported, and demonstrate it with a concrete query; otherwise adjust the multiresolution claim to what the knowledge graph actually provides.
minor comments (4)
  1. [Various] There are several typographical errors, including 'Inthispaper' in the Abstract, 'Natural Langauge Processing' in Section 2.1, and 'maintencance' in Section 2.1.
  2. [§4.2, footnote 3] The text says the outage maps cover the 'five most recent major hurricanes' but footnote 3 groups Helene and Milton as one data campaign, implying about four event groups; please clarify how the 58 maps per county per hurricane figure is computed.
  3. [Figure 4, §3.3] The SPARQL query uses `goo:hasNTLImage` and `goo:hasOutageMap`, but these properties are not defined or described in Section 3.3; please define them in the ontology description or modify the query to use only documented predicates.
  4. [§2.3, §5.1] GeoSPARQL is described in Related Work, but the provided SPARQL example does not use GeoSPARQL and no geospatial query is shown; please state explicitly whether the GeoOutageKG endpoint supports GeoSPARQL and, if so, illustrate it.

Circularity Check

0 steps flagged · score 0.0 of 10

GeoOutageKG is a data-integration construction with no derivation that reduces to its inputs; self-citations are not load-bearing.

full rationale

GeoOutageKG's central contribution is the construction of a semantic resource, not a fitted prediction. The pipeline retrieves external datasets (EAGLE-I outage records and NASA Black Marble NTL imagery), filters them, maps them to GeoOutageOnto, and exposes them as RDF triples. The derived OutageMap class is generated by comparing daily NTL radiance to a three-month baseline, following Aparcedo et al. and external references [10,12,19]; this is a data-processing step with no free parameter that is subsequently renamed as a prediction, and it is not defined in terms of the knowledge graph's own outputs. The demonstrated use cases are SPARQL queries over the assembled graph, not empirical claims whose outcome is fixed by construction. The only self-citations are the VST-GNN model (co-authored by Li) used as a tool for map generation and the MDS-Onto project affiliation; neither supplies an unverified premise that makes the construction equivalent to its inputs. The absence of validation or error analysis for the OutageMap modality is a correctness and robustness limitation, not a circularity: a biased data source would make the KG less reliable, but would not make the KG's derivation reduce to its inputs. No equation is shown to equal another by construction, and no fitted parameter is relabeled as a discovery. The ontology reuses established vocabularies (DBpedia, GEOSatDB, Ontology for Media Resources), and the data sources are external and independently documented. The paper is therefore self-contained as a resource-construction paper, and the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No physical entities are invented; the newly defined ontology classes are data-modeling constructs that organize existing data. The central claim rests on the reliability of the upstream data sources and the unvalidated derived outage maps.

assumptions (4)
  • domain assumption EAGLE-I county-level outage counts accurately reflect actual power outages.
    The paper ingests EAGLE-I records as authoritative outage data without independent verification (Section 3.2).
  • domain assumption Changes in VIIRS nighttime light radiance, relative to a three-month baseline, indicate power outage extent and severity.
    Outage maps are derived from such comparisons (Section 3.2), assuming radiance loss is primarily due to outages.
  • ad hoc to paper VST-GNN model visualizations provide correct 'ground-truth' outage maps.
    The paper uses Aparcedo et al.'s VST-GNN outputs to build OutageMap instances (Section 3.2), but does not validate or report error bounds; this is a dependency on the authors' own prior model.
  • domain assumption Reused ontologies (DBpedia, GEOSatDB, Ontology for Media Resources) are accurate and appropriate for linking.
    The KG links county, satellite, and image metadata to these external resources (Section 3.3).

how reviews work

0 comments
Cite this review

Pith. "Pith review of GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis." pith.science (2026). https://pith.science/paper/4Z3DXENB

@misc{pith2026250722878,
  author       = {Pith},
  title        = {Pith review of: GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4Z3DXENB}},
  note         = {Machine review of arXiv:2507.22878}
}
read the original abstract

Detecting, analyzing, and predicting power outages is crucial for grid risk assessment and disaster mitigation. Numerous outages occur each year, exacerbated by extreme weather events such as hurricanes. Existing outage data are typically reported at the county level, limiting their spatial resolution and making it difficult to capture localized patterns. However, it offers excellent temporal granularity. In contrast, nighttime light satellite image data provides significantly higher spatial resolution and enables a more comprehensive spatial depiction of outages, enhancing the accuracy of assessing the geographic extent and severity of power loss after disaster events. However, these satellite data are only available on a daily basis. Integrating spatiotemporal visual and time-series data sources into a unified knowledge representation can substantially improve power outage detection, analysis, and predictive reasoning. In this paper, we propose GeoOutageKG, a multimodal knowledge graph that integrates diverse data sources, including nighttime light satellite image data, high-resolution spatiotemporal power outage maps, and county-level timeseries outage reports in the U.S. We describe our method for constructing GeoOutageKG by aligning source data with a developed ontology, GeoOutageOnto. Currently, GeoOutageKG includes over 10.6 million individual outage records spanning from 2014 to 2024, 300,000 NTL images spanning from 2012 to 2024, and 15,000 outage maps. GeoOutageKG is a novel, modular and reusable semantic resource that enables robust multimodal data integration. We demonstrate its use through multiresolution analysis of geospatiotemporal power outages.

Figures

Figures reproduced from arXiv: 2507.22878 by the authors.

Figure 1
Figure 1. Continuously updated GeoOutageKG construction pipeline. 3 GeoOutageKG: Geospatiotemporal Power Outage Knowledge Graph In this section, we introduce our approach for the construction of the Geospa￾tiotemporal Power Outage Knowledge Graph, GeoOutageKG. Section 3.1 pro￾vides an overview of the construction pipeline and the key components. Section 3.2 describes how we collect, filter and FAIRify the data. Section 3.3 pr… view at source ↗
Figure 2
Figure 2. Raw NTL images from the Black Marble dataset and the corresponding derived outage maps. Rows 1 and 3 show the NTL images for Lee County, FL (during Hurricane Ian, September 26 – October 2, 2022) and Hillsborough County, FL (during Hurricane Milton, October 8 – 14, 2024), respectively. Rows 2 and 4 present the corresponding outage maps for the same counties and time range [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Visualization of GeoOutageOnto, with classes and relations displayed. Novel classes are shown in blue, while reused classes from other ontologies are shown in orange. Class or instance attributes are italicized. GeoOutageOnto reuses several ontologies, most notably DBpedia, GeoSatDB, and Ontology for Media Resources. shown in orange. To include county information, we link to county instances from DBpedia’s [3] Admin… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: (a) SPARQL query for retrieving outage records, NTL Images, and outage maps around Hurricane Ian in 2022. (b) Raw result and visualization of the query: counties with >100 outages. The first 15 results are displayed. 5 Applications and Use Cases In this section, we dem…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 34 canonical work pages

  1. [1]

    IEEE Access 11, 7682–7697 (2023).https://doi.org/10.1109/ACCESS.2023.3235615

    Abdelmalak, M., Cox, J., Ericson, S., Hotchkiss, E., Benidris, M.: Quantitative resilience-based assessment framework using EAGLE-I power outage data. IEEE Access 11, 7682–7697 (2023).https://doi.org/10.1109/ACCESS.2023.3235615

    arXiv 2023

  2. [2]

    Multimodal Power Outage Prediction for Rapid Disaster Response and Resource Allocation

    Aparcedo, A., Lopez, C., Kotta, A., Li, M.: Multimodal power outage prediction for rapid disaster response and resource allocation (2024).https://doi.org/10. 48550/arXiv.2410.00017

    work page Pith review arXiv doi:10.48550/arxiv.2410.00017 2024

  3. [3]

    In: Proceedings of the 6th International The Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference

    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Proceedings of the 6th International The Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference. p. 722–735. ISWC’07/ASWC’07, Springer-Verlag, Berlin, Heidelberg (2007)

    work page 2007

  4. [4]

    Semantic Web3, 355–370 (Oct 2012).https://doi.org/10.3233/ SW-2012-0065

    Battle, R., Kolas, D.: Enabling the geospatial semantic web with parliament and GeoSPARQL. Semantic Web3, 355–370 (Oct 2012).https://doi.org/10.3233/ SW-2012-0065

    work page 2012

  5. [5]

    Beckett, D., Berners-Lee, T., Prud’hommeaux, E., Carothers, G.: RDF 1.1 turtle (Feb 2014),https://www.w3.org/TR/turtle/

    work page 2014

  6. [6]

    In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data

    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collabo- ratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. p. 1247–1250. SIGMOD ’08, Association for Computing Machinery, New York, NY, USA (2008). https://doi.org/10.1145/1376616.1376746

    arXiv 2008

  7. [7]

    In: Burges, C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K

    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K. (eds.) Advances in Neural Information Processing Systems. vol. 26. Curran Associates, Inc. (2013), https://proceedings.neurips.cc/paper_files/paper/2013/fil...

    work page 2013

  8. [8]

    Scientific Data11(1), 271 (2024)

    Brelsford, C., Tennille, S., Myers, A., Chinthavali, S., Tansakul, V., Denman, M., Coletti, M., Grant, J., Lee, S., Allen, K., et al.: A dataset of recorded electricity outages by United States county 2014–2022. Scientific Data11(1), 271 (2024)

    work page 2024

Show all 43 references
  1. [9]

    Bucci, L., Alaka, L., Hagen, A., Delgado, S., Beven, J.: National Hurricane Center tropical cyclone report - Hurricane Ian (Apr 2023),https://www.nhc.noaa.gov/ data/tcr/AL092022_Ian.pdf

    2023

  2. [10]

    Remote Sens- ing 9(3) (2017)

    Cole, T.A., Wanik, D.W., Molthan, A.L., Román, M.O., Griffin, R.E.: Syner- gistic use of nighttime satellite data, electric utility infrastructure, and ambient population to improve power outage detections in urban areas. Remote Sens- ing 9(3) (2017). https://doi.org/10.3390/r...

    2017 doi

  3. [11]

    In: WSP/WOMoCoE@ ISWC

    Cuenca, J., Larrinaga, F., Curry, E.: A unified semantic ontology for energy man- agement applications. In: WSP/WOMoCoE@ ISWC. pp. 86–97 (2017)

    2017

  4. [12]

    Remote Sensing 15(3) (2023)

    Cui, H., Qiu, S., Wang, Y., Zhang, Y., Liu, Z., Karila, K., Jia, J., Chen, Y.: Disaster-caused power outage detection at night using VIIRS DNB images. Remote Sensing 15(3) (2023). https://doi.org/10.3390/rs15030640, https: //www.mdpi.com/2072-4292/15/3/640

    2023 doi

  5. [13]

    NOAA National Centers for Environmental Information (2019)

    GOES-R Series Program: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 0 Data. NOAA National Centers for Environmental Information (2019). https://doi.org/10.25921/tvws-w071

    2019 doi

  6. [14]

    Hagen, A.B., Cangialosi, J.P., Chenard, M., Alaka, L., Delgado, S.: National Hurricane Center tropical cyclone report - Hurricane Helene (Apr 2025),https: //www.nhc.noaa.gov/data/tcr/AL092024_Helene.pdf

    2025

  7. [15]

    w3.org/TR/sparql11-query/

    Harris, S., Seaborne, A.: SPARQL 1.1 query language (Mar 2013),https://www. w3.org/TR/sparql11-query/

    2013

  8. [16]

    In revision, J

    Hoyer, S., Hamman, J.: xarray: N-D labeled arrays and datasets in Python. In revision, J. Open Res. Software (2017)

    2017

  9. [17]

    In: 2022 IEEE 21st Mediterranean Elec- trotechnical Conference (MELECON)

    Huang, T., Baiocchi, M., Lei, X.: An ontological approach for automatic tracking causes of blackouts in power systems. In: 2022 IEEE 21st Mediterranean Elec- trotechnical Conference (MELECON). pp. 813–818 (2022).https://doi.org/10. 1109/MELECON53508.2022.9843078

    2022

  10. [18]

    Data Intelligence 2(1-2), 10–29 (Jan 2020).https://doi.org/10.1162/dint_r_00024

    Jacobsen, A., de Miranda Azevedo, R., Juty, N., Batista, D., Coles, S., Cornet, R., Courtot, M., Crosas, M., Dumontier, M., Evelo, C.T., Goble, C., Guizzardi, G., Hansen, K.K., Hasnain, A., Hettne, K., Heringa, J., Hooft, R.W., Imming, M., Jeffery, K.G., Kaliyaperumal, R., Ker...

    2020

  11. [19]

    ISPRS - International Archives of the Photogrammetry, Remote Sens- ing and Spatial Information Sciences XLII-3, 1853–1856 (Apr 2018)

    Kalb, V., Wang, Z., Román, M., Sun, Q., Molthan, A., Schultz, L.: Monitor- ing disaster-related power outages using NASA Black Marble nighttime light product. ISPRS - International Archives of the Photogrammetry, Remote Sens- ing and Spatial Information Sciences XLII-3, 1853–1...

    2018 doi

  12. [20]

    Kefalidis, S., Plas, K., Koubarakis, M.: TerraQ: Spatiotemporal question-answering on satellite image archives (Feb 2025).https://doi.org/10.48550/arXiv.2502. 04415

    2025 doi

  13. [21]

    Lee, W., Bailer, W., Bürger, T., Champin, P.A., Evain, J.P., Malaisé, V., Michel, T., Sasaki, F., Söderberg, J., Stegmaier, F., Strassner, J.: Ontology for media resources 1.0 (Feb 2012),https://www.w3.org/TR/mediaont-10/

    2012

  14. [22]

    Big Earth Data 8, 1–18 (Mar 2024)

    Lin, M., Jin, M., Li, J., Bai, Y.: GEOSatDB: global civil earth observation satellite semantic database. Big Earth Data 8, 1–18 (Mar 2024). https://doi.org/10. 1080/20964471.2024.2331992

    2024

  15. [23]

    In: Hitzler, P., Fernández, M., Janowicz, K., Zaveri, A., Gray, A.J., Lopez, V., Haller, A., Hammar, K

    Liu, Y., Li, H., Garcia-Duran, A., Niepert, M., Onoro-Rubio, D., Rosenblum, D.S.: Mmkg: Multi-modal knowledge graphs. In: Hitzler, P., Fernández, M., Janowicz, K., Zaveri, A., Gray, A.J., Lopez, V., Haller, A., Hammar, K. (eds.) The Semantic Web. pp. 459–474. Springer Internat...

    2019

  16. [24]

    Frakes et al

    Mahmoud, M.A., Tang, A.Y., Kumar, K., Law, N.L.L.M.F., Gurunathan, M., Ra- machandran, D.: An ontology-based predictive maintenance tool for power substa- 18 E. Frakes et al. tion faults in distribution grid. International Journal of Advanced Computer Sci- ence and Application...

    2020 doi

  17. [25]

    In: Villazón- Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S.M., Shandilya, S.K

    Mederos,A.L.,García-Duarte,D.,Lio,D.G.,Hidalgo-Delgado,Y.,Ruíz,J.A.S.:An ontological model for the failure detection in power electric systems. In: Villazón- Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S.M., Shandilya, S.K. (eds.) Knowledge Graphs and Semantic Web. pp. 130–146....

    2020

  18. [26]

    NASA Earth Science Data and Information System Standards Coordination Office (2023)

    Newman, D.J., (ESCO), E.S.C.O.: SpatioTemporal Asset Catalogs (STAC). NASA Earth Science Data and Information System Standards Coordination Office (2023). https://doi.org/10.5067/DOC/ESCO/ESDS-RFC-044v1

    2023 doi

  19. [27]

    ontotext.com/documentation/11.0/

    Ontotext: GraphDB: GraphDB 11.0 documentation (Jul 2025),https://graphdb. ontotext.com/documentation/11.0/

    2025

  20. [28]

    In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

    Punjani, D., Koubarakis, M., Tsalapati, E.: EarthQA: A question answering engine for earth observation data archives *. In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. pp. 1396–1399 (2023).https://doi. org/10.1109/IGARSS52108.2023.10282475

    2023

  21. [29]

    Scientific Data 12, 628 (2025)

    Rajamohan, B.P., Bradley, A.C.H., Tran, V.D., Gordon, J.E., Caldwell, H.W., Mehdi, R., Ponon, G., Tran, Q.D., Dernek, O., Kaltenbaugh, J., Pierce, B.G., Wieser, R., Yue, W., Lin, K., Kambo, J., Lopez, C., Nihar, A., Savage, D.J., Brown, D.W., Sharma, H., Giera, B., Tripathi, P...

    2025

  22. [30]

    Remote Sensing of Environment210, 113–143 (2018).https://doi.org/10.1016/ j.rse.2018.03.017

    Román, M.O., Wang, Z., Sun, Q., Kalb, V., Miller, S.D., Molthan, A., Schultz, L., Bell, J., Stokes, E.C., Pandey, B., Seto, K.C., Hall, D., Oda, T., Wolfe, R.E., Lin, G., Golpayegani, N., Devadiga, S., Davidson, C., Sarkar, S., Praderas, C., Schmaltz, J., Boller, R., Stevens, ...

    2018

  23. [31]

    Earth Science Informatics (Dec 2017)

    Rovetto, R.J.: An ontology for satellite databases. Earth Science Informatics (Dec 2017). https://doi.org/10.1007/s12145-017-0290-x

    2017 doi

  24. [32]

    Sharma, K., Kumar, P., Li, Y.: OG-RAG: Ontology-grounded retrieval-augmented generation for large language models (2024),https://arxiv.org/abs/2412.15235

    2024 arXiv

  25. [33]

    https://doi

    Stefanini Vicente, G., Marty, R.: BlackMarblePy: Georeferenced Rasters and Statistics of Nighttime Lights from NASA Black Marble (2023). https://doi. org/10.5281/zenodo.10667907, https://worldbank.github.io/blackmarblepy

    2023 doi

  26. [34]

    In: Proceedings of the 16th International Conference on World Wide Web

    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web. p. 697–706. WWW ’07, Association for Computing Machinery, New York, NY, USA (2007). https://doi.org/10.1145/1242572.1242667

    2007

  27. [35]

    Tansakul, V., Myers, A., Tennille, S., Denman, M., Hamaker, A., Huihui, J., Medlen, K., Allen, K., Redmon, D., Chinthavali, S., et al.: EAGLE-I power outage data 2014 - 2022 (May 2023).https://doi.org/10.13139/ORNLNCCS/1975202

    2014

  28. [36]

    Future Generation Computer Systems160, 505–521 (2024)

    Troumpoukis, A., Klampanos, I., Pantazi, D.A., Albughdadi, M., Baousis, V., Barrilero, O., Bojor, A., Branco, P., Bruzzone, L., Chietera, A., Fournand, P., Hall, R., Lazzarini, M., Luna, A., Nousias, A., Perentis, C., Petrakis, G., Pun- jani, D., Röbl, D., Stamoulis, G., Tsala...

    2024

  29. [37]

    ucs.org/resources/satellite-database

    Union of Concerned Scientists: UCS satellite database (May 2023),https://www. ucs.org/resources/satellite-database

    2023

  30. [38]

    W3C: OWL 2 Web Ontology Language Document Overview (Second Edition) (Dec 2012), https://www.w3.org/TR/owl2-overview/

    2012

  31. [39]

    W3C: RDF 1.2 Schema (Apr 2024),https://www.w3.org/TR/rdf12-schema/

    2024

  32. [40]

    Big Data Research 22, 100159 (2020)

    Wang, M., Wang, H., Qi, G., Zheng, Q.: Richpedia: A large-scale, comprehensive multi-modal knowledge graph. Big Data Research 22, 100159 (2020). https:// doi.org/https://doi.org/10.1016/j.bdr.2020.100159

    2020

  33. [41]

    https://www.wikidata.org/wiki/Wikidata: Main_Page (2025)

    Wikimedia Foundation: Wikidata. https://www.wikidata.org/wiki/Wikidata: Main_Page (2025)

    2025

  34. [42]

    G., Groth, P., Goble, C., Grethe, J.S., Heringa, J., ’t Hoen, Peter A

    Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B., Bourne, P.E., Bouw- man, J., Brookes, A.J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C.T., Finkers, R., Gonzalez-Bel...

    2016

  35. [43]

    WMO: Observing systems capability analysis and review tool (2023),https:// space.oscar.wmo.int/

    2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.