Integrated Land Atmosphere Monitoring in Rivers State Nigeria with UAV

Automatic data analysis systems in the IoT were crucial to our investigation. A major use of drones was monitoring land atmosphere, especially in cities. Analysing metropolitan areas with pollutants and animal habitats required this monitoring. Data analysis helped identify river irregularities, lowering the likelihood of ecological disasters and floods. Continuous monitoring allowed urbanisation impact assessments and environmental conservation. This study presented an end-to-end system where drone users measured and the U-Net network segmentation mask was augmented by image processing techniques. The system segmented with a neural network and overlaid the mask over edge-detected images. All pixels under the mask were clustered to establish river or bank affiliation. Additionally, several measurements from the same location were compared and analysed for variations. The system architecture automated activities using graphics processing algorithms, resulting in more accurate segmentation. We used VGG16 to encode data from southern Polish rivers and achieved a Dice coefficient of 0.8524. This strategy was especially useful in Rivers State, Nigeria, where oil bunkering and unlawful refining caused widespread black soot. These activities substantially harmed air quality and public health, necessitating accurate and regular environmental monitoring. The UAV-based integrated land-atmosphere monitoring system detected and analysed these environmental issues, showing the major impact of industrial activity on the local environment and helping design appropriate mitigation methods.