Deep Learning Small Water Body Mapping by Transfer Learning from Sentinel-2 to PlanetScope
Abstract
1. Introduction
2. Study Area, Data and Methods
2.1. Study Area
2.2. Data Sources
2.2.1. Sentinel-2 Data
2.2.2. PlanetScope Data
2.3. Dataset Pre-Processing
2.4. Model Architecture and Training
2.4.1. Deep Learning Network for Small Water Body Segmentation
2.4.2. Model Training Strategy and Comparison
2.5. Accuracy Assessment
3. Results
3.1. Five-Fold Cross-Validation of Fine-Tuning Strategies and From-Scratch Training Strategies Using PlanetScope
3.2. Comparison Between Pre-Training and Fine-Tuning
3.3. Comparison Fine-Tuning with From-Scratch Training Using PlanetScope
3.4. Validating the Generalization Capability of Transfer Learning Models Across Diverse Networks
4. Discussion
4.1. Impact of the Pre-Trained Data in Transfer Learning
4.2. The Impact of Model Hyperparameters
4.3. The Impact of Model Complexity
4.4. Limitation and Future Works
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
ID | Image Acquisition Times | Image Location (Upper Left Corner) | Image Size in Pixels (Height, Width) |
---|---|---|---|
1 | 26 May 2017 | 117.68°N, 32.18°E | 3973 × 4804 |
2 | 17 July 2017 | 111.97°N, 29.86°E | 4018 × 4160 |
3 | 23 July 2017 | 111.96°N, 32.02°E | 4695 × 4367 |
4 | 2 January 2018 | 109.50°N, 23.08°E | 5073 × 3855 |
5 | 12 January 2018 | 119.07°N, 32.02°E | 5198 × 3958 |
6 | 28 March 2018 | 115.17°N, 31.91°E | 5207 × 3871 |
7 | 1 April 2018 | 116.21°N, 28.15°E | 1754 × 6995 |
8 | 8 April 2018 | 116.54°N, 30.72°E | 3733 × 4193 |
9 | 7 June 2018 | 112.62°N, 30.97°E | 6668 × 5668 |
10 | 28 June 2018 | 113.28°N, 30.17°E | 2864 × 3762 |
11 | 8 September 2018 | 114.47°N, 31.08°E | 5667 × 4054 |
12 | 14 March 2019 | 111.52°N, 30.69°E | 4002 × 3668 |
13 | 6 April 2019 | 111.48°N, 32.57°E | 3668 × 4001 |
14 | 24 August 2019 | 111.81°N, 29.94°E | 3335 × 3334 |
15 | 20 March 2020 | 112.25°N, 31.79°E | 2431 × 2611 |
16 | 26 April 2020 | 111.51°N, 30.56°E | 4961 × 4146 |
17 | 20 May 2020 | 119.11°N, 31.55°E | 5423 × 3196 |
18 | 11 October 2020 | 116.79°N, 31.71°E | 4257 × 4471 |
19 | 24 October 2020 | 118.90°N, 32.53°E | 3423 × 3220 |
20 | 10 November 2020 | 117.84°N, 31.37°E | 6616 × 3300 |
21 | 12 November 2020 | 117.31°N, 32.50°E | 8691 × 4745 |
22 | 30 November 2020 | 117.54°N, 31.22°E | 5145 × 3255 |
23 | 7 May 2021 | 117.87°N, 31.00°E | 5223 × 4104 |
24 | 26 September 2021 | 118.23°N, 32.52°E | 5251 × 3879 |
25 | 26 May 2017 | 118.97°N, 32.00°E | 4862 × 3882 |
26 | 17 July 2017 | 113.75°N, 31.18°E | 3723 × 5249 |
Appendix B
Layer Name | Output Size | VMamba | MambaOut |
---|---|---|---|
Stem | 64 × 64 | ||
Stage1 | 64 × 64 | ||
Stage2 | 32 × 32 | ||
Stage3 | 16 × 16 | ||
Stage4 | 8 × 8 | ||
Layer Name | Output Size | CoAtNet | MaxViT |
---|---|---|---|
Stem | 128 × 128 | ||
Stage1 | 64 × 64 | ||
Stage2 | 32 × 32 | ||
Stage3 | 16 × 16 | ||
Stage4 | 8 × 8 | ||
Layer Name | Output Size | Swin Transformer | ConvNeXt |
---|---|---|---|
Stem | 64 × 64 | ||
Stage1 | |||
Stage2 | 32 × 32 | ||
Stage3 | 16 × 16 | ||
Stage4 | 8 × 8 | ||
Layer Name | Output Size | ResNet | Xception |
---|---|---|---|
Stem | 128 × 128 | ||
Stage1 | 64 × 64 | ||
Stage2 | 32 × 32 | ||
Stage3 | 16 × 16 | ||
Stage4 | 8 × 8 | ||
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PlanetScope Band | Band Name | Wavelength (fwhm) | Interoperable with Sentinel-2 |
---|---|---|---|
1 | Coastal Blue | 443 (20) | Yes—with Sentinel-2 band 1 |
2 | Blue | 490 (50) | Yes—with Sentinel-2 band 2 |
3 | Green I | 531 (36) | No equivalent with Sentinel-2 |
4 | Green | 565 (36) | Yes—with Sentinel-2 band 3 |
5 | Yellow | 610 (20) | No equivalent with Sentinel-2 |
6 | Red | 665 (31) | Yes—with Sentinel-2 band 4 |
7 | Red Edge | 705 (15) | Yes—with Sentinel-2 band 5 |
8 | NIR | 865 (40) | Yes—with Sentinel-2 band 8a |
Name of Strategy | Pre-Training | From-Scratch Training | Fine-Tuning |
---|---|---|---|
Random seed | 42 | ||
Batch size | 16 | ||
Minimum epochs | 30 | 50 | |
Maximum epochs | 60 | 100 | |
Early stop epochs | 10 | ||
Learning rate scheduler | LinearLR + CosineAnnealingLR | ||
Warmup epochs | 10 | ||
Maximum learning rate | 1 × 10−3 | 1 × 10−4 (backbone: 1 × 10−5) | |
Minimum learning rate | 1 × 10−4 | 1 × 10−5 (backbone: 1 × 10−6) | |
Optimizer | AdamW | ||
Weight decay | 1 × 10−2 | 1 × 10−3 | |
Betas | (0.9, 0.999) | ||
Loss function | 0.5 × Focal Weighted Cross Entropy Loss + 0.5 × Focal Dice Loss 1 | ||
Label smoothing | 0.05 | ||
Evaluation metrics (combine score) | 0.5 × Mean Intersection over Union + 0.3 × Generalized Dice Score + 0.15 × Kappa + 0.05 × F1 score |
Strategy | ID | VMamba | MambaOut | CoAtNet | MaxViT | SwinT | ConvNeXt | ResNet | Xception |
---|---|---|---|---|---|---|---|---|---|
From-scratch training using PlanetScope | 1 | 0.8220 | 0.8160 | 0.8152 | 0.8149 | 0.7964 | 0.8157 | 0.8067 | 0.8085 |
2 | 0.8200 | 0.8144 | 0.8170 | 0.8101 | 0.8118 | 0.8120 | 0.8053 | 0.8047 | |
3 | 0.8265 | 0.8181 | 0.8161 | 0.8128 | 0.7955 | 0.8157 | 0.8067 | 0.8064 | |
4 | 0.8219 | 0.8186 | 0.8142 | 0.8133 | 0.7966 | 0.8160 | 0.8070 | 0.8057 | |
5 | 0.8249 | 0.8212 | 0.8106 | 0.8185 | 0.8169 | 0.8181 | 0.8094 | 0.8107 | |
Fine-tuning | 1 | 0.8338 | 0.8265 | 0.8232 | 0.8237 | 0.8263 | 0.8270 | 0.8226 | 0.8219 |
2 | 0.8299 | 0.8230 | 0.8201 | 0.8217 | 0.8238 | 0.8247 | 0.8193 | 0.8195 | |
3 | 0.8335 | 0.8256 | 0.8239 | 0.8244 | 0.8262 | 0.8282 | 0.8226 | 0.8214 | |
4 | 0.8341 | 0.8270 | 0.8244 | 0.8239 | 0.8273 | 0.8290 | 0.8246 | 0.8213 | |
5 | 0.8375 | 0.8320 | 0.8255 | 0.8292 | 0.8313 | 0.8321 | 0.8274 | 0.8256 |
Strategy | Backbone Networks | Evaluation Indicators | ||||
---|---|---|---|---|---|---|
MIoU | OA | F1 | PA | UA | ||
Pre-training | VMamba | 0.7498 | 0.9405 | 0.7049 | 0.8497 | 0.6473 |
MambaOut | 0.7624 | 0.9415 | 0.7235 | 0.8257 | 0.7037 | |
CoAtNet | 0.7687 | 0.9470 | 0.7302 | 0.8156 | 0.7086 | |
MaxViT | 0.7310 | 0.9276 | 0.6811 | 0.8344 | 0.6326 | |
SwinT | 0.7459 | 0.9353 | 0.7009 | 0.8401 | 0.6556 | |
ConvNeXt | 0.7546 | 0.9414 | 0.7116 | 0.8315 | 0.6781 | |
ResNet | 0.7848 | 0.9528 | 0.7533 | 0.8490 | 0.7070 | |
Xception | 0.7938 | 0.9555 | 0.7668 | 0.8457 | 0.7290 | |
Fine-tuning | VMamba | 0.8710 | 0.9781 | 0.8661 | 0.9117 | 0.8288 |
MambaOut | 0.8619 | 0.9764 | 0.8551 | 0.8989 | 0.8214 | |
CoAtNet | 0.8615 | 0.9761 | 0.8547 | 0.9154 | 0.8071 | |
MaxViT | 0.8587 | 0.9756 | 0.8511 | 0.9026 | 0.8120 | |
SwinT | 0.8648 | 0.9769 | 0.8587 | 0.8968 | 0.8287 | |
ConvNeXt | 0.8587 | 0.9762 | 0.8507 | 0.8829 | 0.8301 | |
ResNet | 0.8595 | 0.9754 | 0.8527 | 0.9184 | 0.8017 | |
Xception | 0.8610 | 0.9758 | 0.8544 | 0.9153 | 0.8072 |
Strategy | Backbone Networks | Evaluation Indicators | ||||
---|---|---|---|---|---|---|
MIoU | OA | F1 | PA | UA | ||
From-scratch training using PlanetScope | VMamba | 0.8520 | 0.9737 | 0.8437 | 0.9230 | 0.7832 |
MambaOut | 0.8501 | 0.9736 | 0.8408 | 0.9157 | 0.7839 | |
CoAtNet | 0.8504 | 0.9737 | 0.8409 | 0.9067 | 0.7922 | |
MaxViT | 0.8394 | 0.9709 | 0.8274 | 0.9084 | 0.7710 | |
SwinT | 0.8436 | 0.9722 | 0.8326 | 0.9309 | 0.7603 | |
ConvNeXt | 0.8469 | 0.9730 | 0.8370 | 0.9019 | 0.7890 | |
ResNet | 0.8436 | 0.9715 | 0.8333 | 0.9360 | 0.7587 | |
Xception | 0.8470 | 0.9727 | 0.8374 | 0.9246 | 0.7728 |
Methods | Evaluation Indicators | |||
---|---|---|---|---|
OA | F1 | PA | UA | |
Otsu | 0.7577 | 0.7061 | 0.5581 | 0.9608 |
Edge-Otsu | 0.7901 | 0.7576 | 0.6287 | 0.9528 |
Random Forest | 0.8501 | 0.8336 | 0.7198 | 0.9901 |
VMamba | 0.9825 | 0.9832 | 0.9820 | 0.9844 |
MambaOut | 0.9325 | 0.9349 | 0.9293 | 0.9406 |
CoAtNet | 0.9282 | 0.9297 | 0.9114 | 0.9489 |
MaxViT | 0.9369 | 0.9364 | 0.8910 | 0.9867 |
SwinT | 0.9338 | 0.9337 | 0.8934 | 0.9777 |
ConvNeXt | 0.9357 | 0.9377 | 0.9281 | 0.9474 |
ResNet | 0.9544 | 0.9556 | 0.9413 | 0.9704 |
Xception | 0.9563 | 0.9568 | 0.9281 | 0.9873 |
Backbone Networks | Evaluation Indicators | |||
---|---|---|---|---|
OA | F1 | PA | UA | |
Otsu | 0.7396 | 0.6878 | 0.5736 | 0.8588 |
Edge-Otsu | 0.5755 | 0.6955 | 0.9698 | 0.5422 |
Random Forest | 0.7585 | 0.6878 | 0.5321 | 0.9724 |
VMamba | 0.9604 | 0.9615 | 0.9887 | 0.9357 |
MambaOut | 0.9302 | 0.9319 | 0.9547 | 0.9101 |
CoAtNet | 0.9396 | 0.9420 | 0.9811 | 0.9059 |
MaxViT | 0.9302 | 0.9314 | 0.9472 | 0.9161 |
SwinT | 0.8925 | 0.9012 | 0.9811 | 0.8333 |
ConvNeXt | 0.9283 | 0.9307 | 0.9623 | 0.9011 |
ResNet | 0.8208 | 0.8382 | 0.9283 | 0.7640 |
Xception | 0.7472 | 0.7900 | 0.9509 | 0.6756 |
Backbones | Params. (M) | Weight Size (MB) | FLOPs (GFLOPs) | Training Time (10 Epoch) (Minute) |
---|---|---|---|---|
VMamba | 35.23 | 134.49 | 19.46 | 40 |
MambaOut | 25.91 | 98.93 | 18.96 | 36 |
CoAtNet | 30.05 | 114.81 | 19.03 | 32 |
MaxViT | 31.92 | 122.02 | 19.79 | 36 |
SwinT | 30.90 | 117.97 | 18.67 | 30 |
ConvNeXt | 31.20 | 119.09 | 18.93 | 30 |
ResNet | 29.78 | 113.68 | 19.58 | 28 |
Xception | 44.74 | 170.81 | 28.40 | 28 |
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Share and Cite
Li, Y.; Zhou, P.; Wang, Y.; Li, X.; Zhang, Y.; Li, X. Deep Learning Small Water Body Mapping by Transfer Learning from Sentinel-2 to PlanetScope. Remote Sens. 2025, 17, 2738. https://doi.org/10.3390/rs17152738
Li Y, Zhou P, Wang Y, Li X, Zhang Y, Li X. Deep Learning Small Water Body Mapping by Transfer Learning from Sentinel-2 to PlanetScope. Remote Sensing. 2025; 17(15):2738. https://doi.org/10.3390/rs17152738
Chicago/Turabian StyleLi, Yuyang, Pu Zhou, Yalan Wang, Xiang Li, Yihang Zhang, and Xiaodong Li. 2025. "Deep Learning Small Water Body Mapping by Transfer Learning from Sentinel-2 to PlanetScope" Remote Sensing 17, no. 15: 2738. https://doi.org/10.3390/rs17152738
APA StyleLi, Y., Zhou, P., Wang, Y., Li, X., Zhang, Y., & Li, X. (2025). Deep Learning Small Water Body Mapping by Transfer Learning from Sentinel-2 to PlanetScope. Remote Sensing, 17(15), 2738. https://doi.org/10.3390/rs17152738