Automated Detection of Beaver-Influenced Floodplain Inundations in Multi-Temporal Aerial Imagery Using Deep Learning Algorithms
Abstract
1. Introduction
2. Methods
2.1. Study Area and Data
2.2. Modeling Workflow
2.3. Defining Beaver-Influenced Floodplain Inundation
2.4. Compiling Training Dataset
2.5. Deep Learning Model Training
2.6. Training Data Ablation
2.7. Straight-Edge Post-Processing Methodology
2.8. Visual Quality and Map Accuracy Assessment
3. Results
3.1. Training Dataset Analysis
3.2. Model Performance
3.3. Training Data Ablation Analysis
3.4. Straight-Edge Post-Processing Method
3.5. Statewide Beaver-Influenced Floodplain Inundation Detection
3.6. Visual Quality Assessment
3.7. Map Accuracy Assessment
4. Discussion
4.1. Training Dataset Analysis
4.2. Model Performance
4.3. Training Data Ablation Analysis
4.4. Straight-Edge Post-Processing Method
4.5. Statewide Beaver-Influenced Floodplain Inundation Detection
4.6. Visual Quality Assessment
4.7. Map Accuracy Assessment
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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| Acquisition Year | Leaf-On/Off | Spatial Resolution (m) | Georeferenced (Yes/No) | Spectral Bands | Cloud Cover | Area Imaged | Originators | Used in This Study? |
|---|---|---|---|---|---|---|---|---|
| 1934 | Off | 1 | N | Panchromatic | n/a | Statewide | Fairchild Aerial Survey, Inc. for the State Planning Board | No |
| 1951-52 | On | 1 | N | Panchromatic | n/a | Statewide | Robinson Aerial Surveys, Inc., for the U.S. Department of Agriculture, Agriculture Stabilization and Marketing Service | No |
| 1965 | Off | 1 | N | Panchromatic | n/a | Statewide | Keystone Aerial Surveys, Inc., for the Department of Public Works | No |
| 1970 | Off | 1 | N | Panchromatic | n/a | Statewide | Keystone Aerial Survey, Inc. for the State Department of Transportation | No |
| 1985-86 | Off | 1 | N | Panchromatic | n/a | Statewide | Aero Graphics Corp., Bohemia, NY | No |
| 1990 | Off | 1 | Y | Panchromatic | n/a | Statewide | DEEP, U.S. Geological Survey | Training Inferencing |
| 2004 | Off | 0.30 | Y | Panchromatic | n/a | Statewide | DEEP, Aero-Metric, Inc. | Training Inferencing |
| 2006 | On | 1 | Y | RGB | 10 | Statewide | SDA-FSA-APFO Aerial Photography Field Office | Training Inferencing |
| 2008 | On | 1 | Y | RGB, NIR | 10 | Statewide | USDA-FSA-APFO Aerial Photography Field Office | Training Inferencing |
| 2010 | On | 1 | Y | RGB, NIR | 10 | Statewide | USDA-FSA-APFO Aerial Photography Field Office | Training Inferencing |
| 2012 | Off | 0.30 | Y | RGB, NIR | 0 | Statewide | Photo Science, State of Connecticut Department of Emergency Services and Public Protection | Training Inferencing |
| 2012 | On | 1 | Y | RGB, NIR | 10 | Statewide | USDA-FSA-APFO Aerial Photography Field Office | Training Inferencing |
| 2014 | On | 1 | Y | RGB, NIR | 10 | Statewide | USDA-FSA-APFO Aerial Photography Field Office | Training Inferencing |
| 2016 | Off | 0.08 | Y | RGB, NIR | 0 | Statewide | The Sanborn Map Company, State of Connecticut Department of Emergency Services and Public Protection | Training Inferencing |
| 2016 | On | 0.60 | Y | RGB, NIR | 10 | Statewide | USDA-FSA-APFO Aerial Photography Field Office | Training Inferencing |
| 2018 | On | 0.60 | Y | RGB, NIR | 10 | Statewide | USDA-FSA-APFO Aerial Photography Field Office | Training Inferencing |
| 2019 | Off | 0.15 | Y | RGB, NIR | 0 | Statewide | Quantum Spatial Inc., State of Connecticut Department of Emergency Services and Public Protection | Training Inferencing |
| 2021 | On | 0.6 | Y | RGB, NIR | - | Statewide | USDA-FSA-APFO Aerial Photography Field Office | Inferencing |
| 2023 | Off | 0.08 | Y | RGB, NIR | - | Statewide | Office of Policy and Management | Inferencing |
| 2023 | On | 0.6 | Y | RGB, NIR | - | Statewide | USDA-FSA-APFO Aerial Photography Field Office | No |
| Architecture | Model Variants | Parameters | Citation | ||
|---|---|---|---|---|---|
| Transformer | Semantic | SegFormer | B0-Finetuned | 3.4 M | Xie et al., 2021 [42] |
| SegFormer | B1-Finetuned | 13.0 M | |||
| SegFormer | B2-Finetuned | 25.0 M | |||
| SegFormer | B3-Finetuned | 45.0 M | |||
| SegFormer | B4-Finetuned | 60.0 M | |||
| Convolutional Neural Nets | U-Net++ | ResNet-18 | 11.7 M | Zhou et al., 2018 [43] | |
| U-Net++ | ResNet-34 | 21.8 M | |||
| U-Net++ | ResNet-50 | 25.6 M | |||
| U-Net++ | ResNet-101 | 44.6 M | |||
| Instance | YOLOv8 | YOLOv8n-Seg | 3.2 M | Ultralytics, 2023 | |
| YOLOv8 | YOLOv8s-Seg | 11.2 M | |||
| YOLOv8 | YOLOv8m-Seg | 25.9 M | |||
| YOLOv8 | YOLOv8l-Seg | 43.7 M | |||
| YOLOv8 | YOLOv8x-Seg | 68.2 M | |||
| Model | Epochs | BIFI Precision | BIFI Recall | BIFI F1 | BIFI IoU |
|---|---|---|---|---|---|
| SegFormer B0-Finetuned | 61 | 0.6471 | 0.5081 | 0.5202 | 0.4191 |
| SegFormer B1-Finetuned | 34 | 0.6898 | 0.6898 | 0.5425 | 0.4389 |
| SegFormer B2-Finetuned | 34 | 0.6988 | 0.5753 | 0.5855 | 0.4825 |
| SegFormer B3-Finetuned | 13 | 0.7417 | 0.6992 | 0.6834 | 0.5775 |
| SegFormer B4-Finetuned | 14 | 0.6907 | 0.5392 | 0.5551 | 0.4527 |
| U-Net++ ResNet-18 | 9 | 0.8042 | 0.7706 | 0.7605 | 0.6650 |
| U-Net++ ResNet-34 | 29 | 0.8193 | 0.7886 | 0.7802 | 0.6942 |
| U-Net++ ResNet-50 | 22 | 0.7977 | 0.7828 | 0.7582 | 0.6628 |
| U-Net++ ResNet-101 | 16 | 0.8090 | 0.7633 | 0.7572 | 0.6625 |
| YOLOv8n-seg | 42 | 0.7765 | 0.8350 | 0.7816 | 0.6983 |
| YOLOv8s-seg | 57 | 0.7955 | 0.8550 | 0.8029 | 0.7225 |
| YOLOv8m-seg | 20 | 0.7917 | 0.8486 | 0.7985 | 0.7211 |
| YOLOv8l-seg | 25 | 0.7964 | 0.8577 | 0.8059 | 0.7259 |
| YOLOv8x-seg | 21 | 0.7901 | 0.8342 | 0.7947 | 0.7210 |
| Ipswich, MA | Umpqua River, Oregon | |
|---|---|---|
| Percentage true positive | 3.25 | 0.00 |
| Percentage false positive | 0.40 | 0.00 |
| Percentage false negative | 0.65 | 0.10 |
| Percentage true negative | 95.70 | 99.90 |
| Percentage true positive | 13.27 |
| Percentage false positive | 86.73 |
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© 2025 by the authors. Published by MDPI on behalf of the International Society for Photogrammetry and Remote Sensing. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
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
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 StyleZocco, 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 StyleZocco, 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

