Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark (original) (raw)
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Semantic Segmentation of Aerial Images Using U-Net Architecture
Iraqi Journal for Electrical and Electronic Engineering, 2022
Arial images are very high resolution. The automation for map generation and semantic segmentation of aerial images are challenging problems in semantic segmentation. The semantic segmentation process does not give us precise details of the remote sensing images due to the low resolution of the aerial images. Hence, we propose an algorithm U-Net Architecture to solve this problem. It is classified into two paths. The compression path (also called: the encoder) is the first path and is used to capture the image’s context. The encoder is just a convolutional and maximal pooling layer stack. The symmetric expanding path (also called: the decoder) is the second path, which is used to enable exact localization by transposed convolutions. This task is commonly referred to as dense prediction, which is completely connected to each other and also with the former neurons which gives rise to dense layers. Thus it is an end-to-end fully convolutional network (FCN), i.e. it only contains convol...
Detecting Spatial Information from Satellite Imagery using Deep Learning for Semantic Segmentation
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Detecting spatial information from satellite imagery using deep learning for semantic segmentation is an important field that is significantly growing due to its importance in applications such as the automated generation of vector maps, urban planning, and geographic information systems. In this research, the utilization of deep learning for the semantic segmentation of spatial information from satellite imagery is explored. The objective is to devise an efficient and precise method for detecting and categorizing diverse features on the Earth's surface, including road networks, building footprints, water bodies, vegetation, and land cover which can be used in automatic map production. The proposed technique entails training a deep convolutional neural network to detect spatial features from a small dataset of satellite imagery, followed by a segmentation process to classify the various spatial features. This study conducts various experiments on satellite imagery to achieve high accuracy rates that outperform traditional image processing techniques. In addition, this project also compares various models such as networks with U-shaped architecture U-Net and modified U-Net (Inception ResNetV2U-Net) with various spatial features. Both Implemented models achieved higher results than other relevant research papers. Although the Inception ResNetV2U-Net model produced slightly better results than U-Net, with a validation accuracy of 87.5% and a validation coefficient of 87%, the U-Net model achieved also high validation accuracy and coefficient of 86.5% and 84%, respectively. Additionally, the U-Net model exhibited significantly improved and better training and validation loss than ResNetV2U-Net. Furthermore, the U-Net model showed a shorter average prediction time of satellite imagery. Therefore, the U-Net model is proven to be more suitable for detecting spatial information from small satellite datasets.
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E3S Web of Conferences
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