Feature extraction and analysis of landscape imaging using drones and machine vision

The development of drone and computer vision technologies has enabled automated landscape image analysis, unlocking new feature extraction capabilities. This paper presents an integrated framework leveraging aerial drone data and machine learning for landscape imaging. A diverse dataset of drone-captured scenery spanning urban and rural areas provides the foundation. Preprocessing techniques ready the images before quantitative color analysis and texture feature extraction reveal topographical patterns. Structural boundaries are identified through edge detection and Hough transforms. Deep convolutional neural networks semantically segment images into classified landscape regions. Weighted color block matching retrieves similar images by prioritizing salient areas. Experiments demonstrate 95% accuracy in landscape feature classification, with precision of 0.93, recall of 0.95, and F1 score of 0.94, outperforming existing methods. Extracted color, texture, spectral, and geometric patterns enable interpretation of terrain, vegetation health, human-made objects, and ecological processes. The proposed approach facilitates monitoring, decision-making, and optimization across agriculture, urban planning, conservation, and other applications. Overall, the results validate that combining drones, computer vision, and deep learning can automate the analysis of complex landscape images to generate actionable insights. This methodology pioneers the next generation of intelligent remote sensing systems for comprehending our living environments.