Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region
Recent advances in very high resolution PlanetScope imagery and deep-learning techniques have enabled effective mapping of small water bodies (SWBs), including ponds and ditches. SWBs typically occupy a minor proportion of remote-sensing imagery. This creates significant class imbalance that introdu...
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MDPI AG
2025-05-01
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| author | Pu Zhou Giles Foody Yihang Zhang Yalan Wang Xia Wang Sisi Li Laiyin Shen Yun Du Xiaodong Li |
| author_facet | Pu Zhou Giles Foody Yihang Zhang Yalan Wang Xia Wang Sisi Li Laiyin Shen Yun Du Xiaodong Li |
| author_sort | Pu Zhou |
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| description | Recent advances in very high resolution PlanetScope imagery and deep-learning techniques have enabled effective mapping of small water bodies (SWBs), including ponds and ditches. SWBs typically occupy a minor proportion of remote-sensing imagery. This creates significant class imbalance that introduces bias in trained models. Most existing deep-learning approaches fail to adequately address this imbalance. Such an imbalance introduces bias in trained models. Most existing deep-learning approaches fail to adequately address the inter-class (water vs. non-water) and intra-class (SWBs vs. large water bodies) simultaneously. Consequently, they show poor detection of SWBs. To address these challenges, we propose an area-based weighted binary cross-entropy (AWBCE) loss function. AWBCE dynamically weights water bodies according to their size during model training. We evaluated our approach through large-scale SWB mapping in the middle and east of Hubei Province, China. The models were trained on 14,509 manually annotated PlanetScope image patches (512 × 512 pixels each). We implemented the AWBCE loss function in State-of-the-Art segmentation models (UNet, DeepLabV3+, HRNet, LANet, UNetFormer, and LETNet) and evaluated them using overall accuracy, F1-score, intersection over union, and Matthews correlation coefficient as accuracy metrics. The AWBCE loss function consistently improved performance, achieving better boundary accuracy and higher scores across all metrics. Quantitative and visual comparisons demonstrated AWBCE’s superiority over other imbalance-focused loss functions (weighted BCE, Dice, and Focal losses). These findings emphasize the importance of specialized approaches for comprehensive SWB mapping using high-resolution PlanetScope imagery in low-latitude regions. |
| format | Article |
| id | doaj-art-a1cce091f1db4696ae9da01a7a5a0c9a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-a1cce091f1db4696ae9da01a7a5a0c9a2025-08-20T02:22:59ZengMDPI AGRemote Sensing2072-42922025-05-011711186810.3390/rs17111868Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude RegionPu Zhou0Giles Foody1Yihang Zhang2Yalan Wang3Xia Wang4Sisi Li5Laiyin Shen6Yun Du7Xiaodong Li8Key Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaSchool of Geography, University of Nottingham, Nottingham NG7 2RD, UKKey Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaCAS Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaHubei Water Resources and Hydropower Science and Technology Promotion Center, Hubei Water Resources Research Institute, Wuhan 430070, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaRecent advances in very high resolution PlanetScope imagery and deep-learning techniques have enabled effective mapping of small water bodies (SWBs), including ponds and ditches. SWBs typically occupy a minor proportion of remote-sensing imagery. This creates significant class imbalance that introduces bias in trained models. Most existing deep-learning approaches fail to adequately address this imbalance. Such an imbalance introduces bias in trained models. Most existing deep-learning approaches fail to adequately address the inter-class (water vs. non-water) and intra-class (SWBs vs. large water bodies) simultaneously. Consequently, they show poor detection of SWBs. To address these challenges, we propose an area-based weighted binary cross-entropy (AWBCE) loss function. AWBCE dynamically weights water bodies according to their size during model training. We evaluated our approach through large-scale SWB mapping in the middle and east of Hubei Province, China. The models were trained on 14,509 manually annotated PlanetScope image patches (512 × 512 pixels each). We implemented the AWBCE loss function in State-of-the-Art segmentation models (UNet, DeepLabV3+, HRNet, LANet, UNetFormer, and LETNet) and evaluated them using overall accuracy, F1-score, intersection over union, and Matthews correlation coefficient as accuracy metrics. The AWBCE loss function consistently improved performance, achieving better boundary accuracy and higher scores across all metrics. Quantitative and visual comparisons demonstrated AWBCE’s superiority over other imbalance-focused loss functions (weighted BCE, Dice, and Focal losses). These findings emphasize the importance of specialized approaches for comprehensive SWB mapping using high-resolution PlanetScope imagery in low-latitude regions.https://www.mdpi.com/2072-4292/17/11/1868PlanetScopedeep learningloss functionsmall water bodiesclass imbalance problemarea-based weighted binary cross-entropy (AWBCE) |
| spellingShingle | Pu Zhou Giles Foody Yihang Zhang Yalan Wang Xia Wang Sisi Li Laiyin Shen Yun Du Xiaodong Li Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region Remote Sensing PlanetScope deep learning loss function small water bodies class imbalance problem area-based weighted binary cross-entropy (AWBCE) |
| title | Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region |
| title_full | Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region |
| title_fullStr | Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region |
| title_full_unstemmed | Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region |
| title_short | Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region |
| title_sort | using an area weighted loss function to address class imbalance in deep learning based mapping of small water bodies in a low latitude region |
| topic | PlanetScope deep learning loss function small water bodies class imbalance problem area-based weighted binary cross-entropy (AWBCE) |
| url | https://www.mdpi.com/2072-4292/17/11/1868 |
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