Using Deep learning and GIS applications to Extract Features from Remote Sensing Data
Abstrak
In recent years, artificial intelligence (AI) has advanced quickly, equal or perhaps even outperforming human accuracy in tasks like picture recognition, reading comprehension, and text translation. Large-scale opportunities that were not previously available are now had been created by the confluence of AI and GIS with remote sensing data processing. The motivation of current study is to incorporate deep learning models that implemented through ArcGIS Pro tools particularly convolutional neural networks (CNNs), identifying complex patterns and features in high-resolution image. An automated Deep Learning model type Mask R-CNN had applied to extract model for training objects. The overall accuracy metric improved the performance of the current work accurately with less error when calculating RMSE criteria.
Topik & Kata Kunci
Penulis (1)
Faten Azeez Mustafa
Akses Cepat
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- 2025
- Sumber Database
- DOAJ
- DOI
- 10.33899/iqjoss.v22i2.54069
- Akses
- Open Access ✓