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.
MBZUAI is developing AI technologies to improve understanding and conservation of marine environments. AI algorithms analyze data from satellite imagery, ROVs, and sensors to identify patterns and trends, with machine learning models predicting oceanographic phenomena. Computer vision automates the identification of marine organisms, aiding in biodiversity assessments and ecosystem health evaluations. Why it matters: This research supports sustainability goals, such as improving biodiversity assessments and enabling faster responses to environmental events in the region's sensitive marine ecosystems.
VentureOne, part of Abu Dhabi's Advanced Technology Research Council (ATRC), launched Nabat, a climate tech venture using AI and robotics for ecosystem restoration. Nabat employs drones, AI-powered software, and flexible seeding to conserve and restore mangroves, aiming to cover thousands of hectares in the UAE over seven years. Their technology enables precision mapping, seeding, and monitoring in remote areas. Why it matters: This initiative showcases the UAE's commitment to using advanced technology for environmental conservation and climate resilience, particularly in preserving vital ecosystems like mangroves, while also highlighting the growing AI startup ecosystem in the region.
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.
MBZUAI, in partnership with IBM Research, is developing GeoChat+, a vision-language model (VLM) for multi-modal, temporal remote sensing image analysis. GeoChat+ builds on the previous GeoChat model, enhancing capabilities with multi-modal images from various Earth observation systems like Sentinel-1, Sentinel-2, Landsat, and high-resolution imagery. GeoChat+ will integrate data from multiple satellites at different times to detect environmental changes and analyze the impact on soil quality, air quality, and erosion. Why it matters: This advancement promises to revolutionize geographic data analysis, providing detailed reports for high-risk regions and aiding reforestation efforts.