<![CDATA[Wetland loss endangers the ecosystem through loss of biodiversity, carbon sequestration and flood regulation potential. A precise determination of wetlands status is necessary to safeguard for their conservation and ensure sustainable management. Implementation This study aims to assess the performance of deep CNNs in wetland detection using high-resolution Google Earth image data in South Kalimantan province, Indonesia. The work adopts the Chopped Picture Method (CPM) and the use of sliding windows for data augmentation to improve the diversity of the dataset and reduce the computational cost. Two CNN models, VGG-16Net, and LeNet-5, were trained using a dataset comprising 220 satellite images, which we converted into 89,100 patches of 56×56 pixels. Performance was compared using accuracy, precision, recall, and F1-score. Experimental results show good levels of accuracy for the two architectures, but LeNet-5 provided more stable results between test locations, having a F1-score closer to 100% and spending less computational time (≈10s per epoch) than VGG-16Net (≈40s per epoch). These results validate that CPM significantly increases the variety of training data, making it possible for a CNN to correctly identify the vague and irregular shapes of wetlands with high accuracy. In addition to advancing environmental conservation strategies, the study highlights the contribution of informatics to large-scale, automated environmental monitoring, particularly in supporting wetland conservation, sustainable land-use planning, and climate adaptation efforts.]]>
