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Analysis of institutional authors

Liz López, HelenaAdapterCasanova Mateo, CarlosAuthorHuertas Tato, JavierAuthorPerez Aracil, JorgeAuthorLiz-López H, Huertas-Tato J, Pérez-Aracil J, Casanova-Mateo C, Sanz-Justo J, Camacho DCorresponding Author

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December 7, 2023
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Spain on fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information

Publicated to: Knowledge-Based Systems. 283 111198- - 2024-01-11 283(), DOI: 10.1016/j.knosys.2023.111198

Authors:

Liz-López, H; Huertas-Tato, J; Pérez-Aracil, J; Casanova-Mateo, C; Sanz-Justo, J; Camacho, D
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Affiliations

Univ Alcala, Signal Theory & Commun Dept, Plaza San Diego S-N, Alcala De Henares 28801, Spain - Author
Univ Politecn Madrid, Comp Syst Dept, Calle Alan Turing S-N, Madrid 28031, Spain - Author
Univ Valladolid, Remote Sensing Lab, LATUV, Plaza Santa Cruz, Valladolid 47002, Spain - Author
Universidad de Alcalá - Author
Universidad de Valladolid - Author
Universidad Politécnica de Madrid - Author
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Abstract

Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour of individuals is unpredictable. However, atmospheric and environmental variables affect the spread of wildfires, and they can be analysed by using deep learning. In order to mitigate the damage of these events, we proposed the novel Wildfire Assessment Model (WAM). Our aim is to anticipate the economic and ecological impact of a wildfire, assisting managers in resource allocation and decision-making for dangerous regions in Spain, Castilla y León and Andalucía. The WAM uses a residual-style convolutional network architecture to perform regression over atmospheric variables and the greenness index, computing necessary resources, the control and extinction time, and the expected burnt surface area. It is first pre-trained with self-supervision over 100,000 examples of unlabelled data with a masked patch prediction objective and fine-tuned using a very small dataset, composed of 445 samples. The pretraining allows the model to understand situations, outclassing baselines with a 1,4%, 3,7% and 9% improvement estimating human, heavy and aerial resources; 21% and 10,2% in expected extinction and control time; and 18,8% in expected burnt area. Using the WAM we provide an example assessment map of Castilla y León, visualizing the expected resources over an entire region.
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Keywords

atmospheric variablesautoencoderdeep learningfew-shot learningfusionregression modelAtmospheric variablesAutoencoderConvolutional neural-networksDeep learningFew-shot learningFusionRegression modelWildfire risk assessment

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Knowledge-Based Systems due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 26/204, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-12-19:

  • Google Scholar: 2
  • WoS: 8
  • Scopus: 15
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-12-19:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 40.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 47 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/81551/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 234
  • Downloads: 96
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (CAMACHO FERNANDEZ, DAVID) .

the author responsible for correspondence tasks has been CAMACHO FERNANDEZ, DAVID.

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