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

## TL;DR

This paper presents a deep learning approach for efficiently mapping land cover features like buildings and roads from aerial images.

## Contribution

The study introduces a novel deep learning pipeline that improves the accuracy and speed of land cover classification in aerial imagery.

## Key findings

- The DeepLabV3 ResNet50 model achieved the highest accuracy of 94.77% for land cover classification.
- The proposed approach outperformed traditional UNet models in prediction scores across all classes.
- ResNet50 UNet and DeepLabV3 ResNet50 models showed better performance metrics than Vanilla-UNet.

## 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 the experimental results, the ResNet50 UNet model achieved an accuracy of 94.37%, the DeepLabV3 ResNet50 model achieved an accuracy of 94.77%, and the Vanilla-UNet model achieved an accuracy of 91.31%. The accuracy, precision, recall, and F1-score of DeepLabV3 and ResNet50 are higher than those of the other two models. The proposed approach is also compared to the existing UNet approach, and the proposed approaches have produced greater probability prediction scores than the conventional UNet model for all classes. Our approach outperforms model DeepLabV3 ResNet50 on aerial image datasets based on the performance.