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Establishing up-to-date nationwide building maps is essential to understand urban dynamics, such as estimating population and urban planning and many other applications. However, an efficient and effective solution is yet to be developed. In this paper, for the first time we evaluate three state-of-the-art CNNs for detecting buildings across entire United States using aerial images. The three CNN...
Building extraction from remote sensing images is a longstanding topic in land use analysis and applications of remote sensing. Variations in shape and appearance of buildings, occlusions and other unpredictable factors increase the hardness of automatic building extraction. Numerous methods have been proposed during the last several decays, but most of these works are task oriented and lack of generalization...
New challenges in remote sensing impose the necessity of designing pixel classification methods that, once trained on a certain dataset, generalize to other areas of the earth. This may include regions where the appearance of the same type of objects is significantly different. In the literature it is common to use a single image and split it into training and test sets to train a classifier and assess...
Automatic and accurate detection of man-made objects, such as buildings, is one of the main problems that the remote sensing community has been focusing on for the last decades. In this paper, we propose a Conditional Random Field (CRF) formulation which is using edge/boundary localization priors towards accurate building detection. These edge priors have been integrated/fused with the classification...
We address the pixelwise classification of high-resolution aerial imagery. While convolutional neural networks (CNNs) are gaining increasing attention in image analysis, it is still challenging to adapt them to produce fine-grained classification maps. This is due to a well-known trade-off between recognition and localization: the impressive capability of CNNs to recognize meaningful objects comes...
Fusing different sensors with different data modalities is a common technique to improve land cover classification performance in remote sensing. However, all modalities are rarely available for all test data, and this missing data problem poses severe challenges for multi-modal learning. Inspired by recent successes in deep learning, we propose as a remedy a convolutional neural network architecture...
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