Evidence map›Paper›PMID 40278010›Full record

ArticleJournal of imaging2025

Large-Scale Coastal Marine Wildlife Monitoring with Aerial Imagery.

Octavio Ascagorta, María Débora Pollicelli, Francisco Ramiro Iaconis, Elena Eder, Mathías Vázquez-Sano, Claudio Delrieux

Abstract read
In one paragraph

Article in Journal of imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Octavio AscagortaDepartamento de Ingeniería, Universidad Nacional de la Patagonia San Juan Bosco, Puerto Madryn 9120, Argentina.ORCID 0009-0009-3302-1581
María Débora PollicelliDepartamento de Ingeniería, Universidad Nacional de la Patagonia San Juan Bosco, Puerto Madryn 9120, Argentina.ORCID 0009-0006-8771-2538
Francisco Ramiro IaconisDepartamento de Física, Instituto de Física del Sur, Universidad Nacional del Sur (UNS) and CONICET, Bahía Blanca 8000, Argentina.ORCID 0000-0002-2373-5793
Elena EderCentro para el Estudio de Sistemas Marinos, Centro Nacional Patagonico, CONICET, Puerto Madryn 9120, Argentina.ORCID 0000-0002-8207-0322
Mathías Vázquez-SanoDepartamento de Biologia, Universidad Nacional de Catamarca, San Fernando del Valle de Catamarca 4700, Argentina.ORCID 0009-0005-8868-6194
Claudio DelrieuxDepartamento de Ingeniería Eléctrica y Computadoras, Instituto de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur and CONICET, Bahía Blanca 8000, Argentina.ORCID 0000-0002-2727-8374

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring coastal marine wildlife is crucial for biodiversity conservation, environmental management, and sustainable utilization of tourism-related natural assets. Conducting in situ censuses and population studies in extensive and remote marine habitats often faces logistical constraints, necessitating the adoption of advanced technologies to enhance the efficiency and accuracy of monitoring efforts. This study investigates the utilization of aerial imagery and deep learning methodologies for the automated detection, classification, and enumeration of marine-coastal species. A comprehensive dataset of high-resolution images, captured by drones and aircrafts over southern elephant seal (

Indexed as

aerial imagerycoastal marine wildlife monitoringdeep learningenvironmental managementpopulation assessment

Identifiers

PMID40278010
PMCPMC12027912

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.