Abstract. Detection of Building edges is crucial for building information extraction and description. Extracting structures from large-scale aerial images has been utilized for years in cartography. With commercially available high-resolution satellites, many aerial photography usages can now employ satellite imagery. Edge detection is focused on pinpointing distinct transitions between greyscale image regions and attributing their origins to underlying physical processes. Detecting building boundaries from very high-resolution (VHR) remote sensing data is essential for many geo-related applications, such as urban planning and management, surveying and mapping, 3D reconstruction, motion recognition, image registration, image enhancement and restoration, image compression, and more. The rapid evolution of convolutional neural networks (CNNs) has led to substantial breakthroughs in edge detection in recent years. Sharp, localized changes in brightness characterize edges in digital images. In most cases, edge detection requires some kind of image smoothing and separation. Differentiation is an ill-conditioned problem, and smoothing leads to information loss. It is challenging to create an edge detection method that works everywhere and adapts to any future processing stages. Therefore, throughout the development of digital image processing, numerous edge detectors have been created, each with its own unique set of mathematical and algorithmic properties. Several edge detectors have been developed due to application needs and the subjective nature of edge definition and characterization. We propose a deep learning technique, particularly convolutional neural networks(CNNs), that offers a promising approach to automatically learn and extract features from very high-resolution remote sensing imagery, leading to more accurate and efficient building edge detection.

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