CCE-UNet: Forest and Water Body Coverage Detection Method Based on Deep Learning: A Case Study in Australia’s Nattai National Forest

Severe forest fires caused by extremely high temperatures have resulted in devastating disasters in the natural forest reserves of New South Wales, Australia. Traditional forest research methods primarily rely on manual field surveys, which have limited generalization capabilities. In order to monitor forest ecosystems more comprehensively and maintain the stability of the regional forest ecosystem, as well as to monitor post-disaster ecological restoration efforts, this study employed high-resolution remote sensing imagery and proposed a semantic segmentation architecture named CCE-UNet. This architecture focuses on the precise identification of forest coverage while simultaneously monitoring the distribution of water resources in the area. This architecture utilizes the Contextual Information Fusion Module (CIFM) and introduces the dual attention mechanism strategy to effectively filter background information and enhance image edge features. Meanwhile, it employs a multi-scale feature fusion algorithm to maximize the retention of image details and depth information, achieving precise segmentation of forests and water bodies. We have also trained seven semantic segmentation models as candidates. Experimental results show that the CCE-UNet architecture achieves the best performance, demonstrating optimal performance in forest and water body segmentation tasks, with the MIoU reaching 91.07% and the MPA reaching 95.15%. This study provides strong technical support for the detection of forest and water body coverage in the region and is conducive to the monitoring and protection of the forest ecosystem.