TARGETS MASK U-NET FOR WIND TURBINES DETECTION IN REMOTE SENSING IMAGES

7 citations as recorded by crossref.

1. Sugarcane Planting Area Classification, Extraction and Accuracy Comparison Based on Chinese High-Resolution Remote Sensing Satellite Data: A Case Study of Ningming Sugarcane Demonstration Area, Chongzuo City, Guangxi L. Linjiang et al. https://doi.org/10.1088/1755-1315/502/1/012041
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3. A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS O. de Carvalho et al. https://doi.org/10.3390/rs15051240
4. Semantic-to-Instance Segmentation of Time-Invariant Offshore Wind Farms Using Sentinel-1 Time Series and Time-Shift Augmentation O. de Carvalho et al. https://doi.org/10.3390/en18051127
5. DAWindNet: A Deep CNN for Domain Adaptively Extracting Wind Turbine From Satellite HRI Z. Zeng et al. https://doi.org/10.1109/JSTARS.2025.3529293
6. Identifying wind turbines from multiresolution and multibackground remote sensing imagery Y. Zhai et al. https://doi.org/10.1016/j.jag.2023.103613
7. A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis M. Kleebauer et al. https://doi.org/10.3390/ijgi14060232