A new study uses the UNet++ deep learning model and Sentinel-2 satellite data to monitor mangrove dynamics in the UAE from 2017 to 2024. The model achieved a mean Intersection over Union (mIoU) of 87.8% on the validation set. Results indicate a significant increase in mangrove area, primarily in Abu Dhabi, contributing to enhanced carbon sequestration across the UAE.
Keywords
Mangroves · Deep Learning · UNet++ · Sentinel-2 · UAE
This paper introduces a deep vision-based framework for predicting coastal floods under climate change, addressing the challenges of limited training data and high-dimensional output. The framework employs and compares various deep learning models, including a custom compact CNN architecture, against geostatistical and traditional machine learning methods. A new synthetic dataset of flood inundation maps for Abu Dhabi's coast is also provided to benchmark future models.
Researchers have developed a CNN-based deep learning model for predicting coastal flooding in cities under various sea-level rise scenarios. The model utilizes a vision-based, low-resource DL framework and is trained on datasets from Abu Dhabi and San Francisco. Results show a 20% reduction in mean absolute error compared to existing methods, demonstrating potential for scalable coastal flood management.
This paper introduces a novel two-step method for predicting urban expansion using time-series satellite imagery. The approach combines semantic image segmentation with a CNN-LSTM model to learn temporal features. Experiments on satellite images from Riyadh, Jeddah, and Dammam in Saudi Arabia demonstrate improved performance compared to existing methods based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.