Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
نویسندگان
چکیده
Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically land use/land cover (LULC) mapping. The becomes more challenging with the increasing number and complexity LULC classes. In this research, we generated new benchmark dataset from VHR Worldview-3 twelve distinct classes two different geographical locations. We evaluated performance architectures encoders to find best design create highly accurate maps. Our results showed that DeepLabv3+ architecture an ResNeXt50 encoder achieved metric values IoU 89.46%, F-1 score 94.35%, precision 94.25%, recall 94.49%. This could be used by other researchers mapping similar or regions. Moreover, our can as reference implementing models via supervised, semi- weakly-supervised deep learning models. addition, model transfer generalizability methodologies.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14184558