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Article

Automated Detection of Beaver-Influenced Floodplain Inundations in Multi-Temporal Aerial Imagery Using Deep Learning Algorithms

1
Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA
2
Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA
3
Department of Earth Sciences, University of Connecticut, Storrs, CT 06269, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2025, 14(10), 383; https://doi.org/10.3390/ijgi14100383
Submission received: 25 August 2025 / Revised: 16 September 2025 / Accepted: 25 September 2025 / Published: 30 September 2025

Abstract

Remote sensing provides a viable alternative for understanding landscape modifications attributed to beaver activity. The central objective of this study is to integrate multi-source remote sensing observations in tandem with a deep learning (DL) (convolutional neural net or transformer) model to automatically map beaver-influenced floodplain inundations (BIFI) over large geographical extents. We trained, validated, and tested eleven different model configurations in three architectures using five ResNet and five B-Finetuned encoders. The training dataset consisted of >25,000 manually annotated aerial image tiles of BIFIs in Connecticut. The YOLOv8 architecture outperformed competing configurations and achieved an F1 score of 80.59% and pixel-based map accuracy of 98.95%. SegFormer and U-Net++’s highest-performing models had F1 scores of 68.98% and 78.86%, respectively. The YOLOv8l-seg model was deployed at a statewide scale based on 1 m resolution multi-temporal aerial imagery acquired from 1990 to 2019 under leaf-on and leaf-off conditions. Our results suggest a variety of inferences when comparing leaf-on and leaf-off conditions of the same year. The model exhibits limitations in identifying BIFIs in panchromatic imagery in occluded environments. Study findings demonstrate the potential of harnessing historical and modern aerial image datasets with state-of-the-art DL models to increase our understanding of beaver activity across space and time.
Keywords: computer vision; remote sensing; landscape ecology; beaver habitat mapping computer vision; remote sensing; landscape ecology; beaver habitat mapping

Share and Cite

MDPI and ACS Style

Zocco, E.; Witharana, C.; Ortega, I.M.; Ouimet, W. Automated Detection of Beaver-Influenced Floodplain Inundations in Multi-Temporal Aerial Imagery Using Deep Learning Algorithms. ISPRS Int. J. Geo-Inf. 2025, 14, 383. https://doi.org/10.3390/ijgi14100383

AMA Style

Zocco E, Witharana C, Ortega IM, Ouimet W. Automated Detection of Beaver-Influenced Floodplain Inundations in Multi-Temporal Aerial Imagery Using Deep Learning Algorithms. ISPRS International Journal of Geo-Information. 2025; 14(10):383. https://doi.org/10.3390/ijgi14100383

Chicago/Turabian Style

Zocco, Evan, Chandi Witharana, Isaac M. Ortega, and William Ouimet. 2025. "Automated Detection of Beaver-Influenced Floodplain Inundations in Multi-Temporal Aerial Imagery Using Deep Learning Algorithms" ISPRS International Journal of Geo-Information 14, no. 10: 383. https://doi.org/10.3390/ijgi14100383

APA Style

Zocco, E., Witharana, C., Ortega, I. M., & Ouimet, W. (2025). Automated Detection of Beaver-Influenced Floodplain Inundations in Multi-Temporal Aerial Imagery Using Deep Learning Algorithms. ISPRS International Journal of Geo-Information, 14(10), 383. https://doi.org/10.3390/ijgi14100383

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