Efficient geospatial mapping of buildings, woodlands, water and roads from aerial imagery using deep learning.

ABSTRACT: As more aerial imagery becomes readily available, massive volumes of data are being gathered constantly. Several groups can benefit from the data provided by this geographical imagery. However, it is time-consuming to manually analyze each image to gain information on land cover. This research suggests using deep learning methods for precise and rapid pixel-by-pixel classification of aerial imagery for land cover analysis, which would be a significant step forward in resolving this issue. The suggested method has several steps, such as the augmentation and transformation of data, the selection of deep learning models, and the final prediction. The study uses the three most popular deep learning models (Vanilla-UNet, ResNet50 UNet, and DeepLabV3 ResNet50) for the experiments. According to t

SUBMITTER: Abbas S 

PROVIDER: S-EPMC11232583 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature