Semanticforestmy: a spatio-temporal remote sensing dataset for forest and non-forest semantic segmentation
Accurate classification of forest and non-forest regions in satellite imagery is vital for land monitoring. However, inconsistent annotations and limited standardised datasets hinder model comparability. SemanticForestMY addresses this issue with a high-resolution dataset derived from multi-temporal...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/132249/1/132249.pdf https://ir.uitm.edu.my/id/eprint/132249/ |
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| Summary: | Accurate classification of forest and non-forest regions in satellite imagery is vital for land monitoring. However, inconsistent annotations and limited standardised datasets hinder model comparability. SemanticForestMY addresses this issue with a high-resolution dataset derived from multi-temporal satellite data across three Malaysian regions. Annotations were produced using a hybrid method combining preprocessing, blob filtering, and manual correction. FCN32-VGG16 was used to benchmark performance, yielding 91.61% validation accuracy, a 94.52% F1-score, and an 89.60% IoU. These results validate the dataset's utility for deep learning segmentation. Future plans include multi-class expansion, seasonal coverage, and evaluation using advanced models. |
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