Computer-automated bird detection and counts in high-resolution aerial images: a review
Bibliographic record
Abstract
Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time-consuming if images must be analyzed manually. Recent advances in digital cameras and image-analysis software offer unprecedented potential for computer-automated bird detection and counts in high-resolution aerial images. We review the literature on this subject and provide an overview of the main image-analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic image-analysis software. However, the sophisticated analysis capabilities of modern object-based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal-infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true-color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife-monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer-automated aerial photographic census techniques. Conteo de aves realizados utilizando imágenes aéreas pueden ser mas precisos que los que utilizan observadores desde el aire, pero pueden consumir mas tiempo si las imágenes tienen que ser analizadas manualmente. Avances recientes en cámaras digitales y software de análisis de imágenes ofrecen un potencial sin precedentes para la detección computacional automática de aves y conteos en imágenes aéreas de alta resolución. Revisamos la literatura en este tema y ofrecemos una visión general de las principales técnicas de análisis en imágenes. Las aves que tienen un fuerte contraste con los fondos de las imágenes (e.g., aves brillantes en fondos oscuros) son en general las mas sensibles a las detecciones automáticas, en algunos casos solo requieren un software básico de analizador de imágenes. Sin embargo, las sofisticadas capacidades de los software de análisis modernos en imágenes basadas en objetos, proveen formas de detectar aves en situaciones mas desafiantes basadas en una variedad de atributos incluyendo el color, tamaño, forma, textura y contexto espacial. Algunas técnicas desarrolladas para detectar mamíferos pueden ser aplicables en aves, aunque el uso predominante de imágenes aéreas de infra rojo térmico para detectar grandes mamíferos tienen aplicabilidad limitada para las aves, debido a la baja resolución en los pixeles de las cámaras térmicas y el tamaño pequeño de las aves. Sin embargo, el incremento en la alta resolución de las cámaras de color y la disponibilidad de pequeños sistemas de aeronaves no tripuladas (drones) que pueden volar a bajas elevaciones, ahora hacen que sea posible detectar incluso pequeñas aves playeras en imágenes aéreas. Los continuos avances en la tecnología de la cámara y aeronaves no tripuladas, en combinación con software de análisis de imágenes cada vez más sofisticados, ahora hacen posible ahorrara tiempo y recursos a los investigadores involucrados en el monitoreo de las poblaciones de aves, mediante el aumento del uso de la detección y conteos de aves automatizado en imágenes aéreas. Recomendamos una estrecha colaboración entre los profesionales de monitoreo de fauna silvestre y expertos en el campo de la teledetección y la informática para ayudar a generar técnicas de censo relevantes, accesibles y de fácil aplicación automatizada computacionalmente utilizando fotografías aéreas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".