DEEP LEARNING APPROACH FOR URBAN MAPPING

Researchers in computer vision and machine learning are becoming increasingly interested in image semantic segmentation. Many methods based on convolutional neural networks (CNNs) have been proposed and have made considerable progress in the building extraction mission. This other methods can result in suboptimal segmentation outcomes. Recently, to extract buildings with a great precision, we propose a model which can recognize all the buildings and present them in mask with white and the other classes in black. This developed network, which is based on U-Net, will boost the model's sensitivity. This paper provides a deep learning approach for building detection on satellite imagery applied in Casablanca city, Firstly, to begin we describe the terminology of this field. Next, the main datasets exposed in this project which’s 1000 satellite imagery. Then, we train the model UNET for 25 epochs on the training and validation datasets and testing the pretrained weight model with some unseen satellite images. Finally, the experimental results show that the proposed model offers good performance obtained as a binary mask that extract all the buildings in the region of Casablanca with a higher accuracy and entirety to achieve an average F1 score on test data of 0.91.

B-FGC-Net: A Building Extraction Network from High Resolution Remote Sensing Imagery

Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net). The residual learning and spatial attention unit are introduced in the encoder of the B-FGC-Net, which simplifies the training of deep convolutional neural networks and highlights the spatial information representation of features. The global feature information awareness module is added to capture multiscale contextual information and integrate the global semantic information. The cross level feature recalibration module is used to bridge the semantic gap between low and high level features to complete the effective fusion of cross level information. The performance of the proposed method was tested on two public building datasets and compared with classical methods, such as UNet, LinkNet, and SegNet. Experimental results demonstrate that B-FGC-Net exhibits improved profitability of accurate extraction and information integration for both small and large scale buildings. The IoU scores of B-FGC-Net on WHU and INRIA Building datasets are 90.04% and 79.31%, respectively. B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images.

Progress Guidance Representation for Robust Interactive Extraction of Buildings from Remotely Sensed Images

Accurate building extraction from remotely sensed images is essential for topographic mapping, cadastral surveying and many other applications. Fully automatic segmentation methods still remain a great challenge due to the poor generalization ability and the inaccurate segmentation results. In this work, we are committed to robust click-based interactive building extraction in remote sensing imagery. We argue that stability is vital to an interactive segmentation system, and we observe that the distance of the newly added click to the boundaries of the previous segmentation mask contains progress guidance information of the interactive segmentation process. To promote the robustness of the interactive segmentation, we exploit this information with the previous segmentation mask, positive and negative clicks to form a progress guidance map, and feed it to a convolutional neural network (CNN) with the original RGB image, we name the network as PGR-Net. In addition, an adaptive zoom-in strategy and an iterative training scheme are proposed to further promote the stability of PGR-Net. Compared with the latest methods FCA and f-BRS, the proposed PGR-Net basically requires 1–2 fewer clicks to achieve the same segmentation results. Comprehensive experiments have demonstrated that the PGR-Net outperforms related state-of-the-art methods on five natural image datasets and three building datasets of remote sensing images.

Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review

Building extraction from remote sensing (RS) images is a fundamental task for geospatial applications, aiming to obtain morphology, location, and other information about buildings from RS images, which is significant for geographic monitoring and construction of human activity areas. In recent years, deep learning (DL) technology has made remarkable progress and breakthroughs in the field of RS and also become a central and state-of-the-art method for building extraction. This paper provides an overview over the developed DL-based building extraction methods from RS images. Firstly, we describe the DL technologies of this field as well as the loss function over semantic segmentation. Next, a description of important publicly available datasets and evaluation metrics directly related to the problem follows. Then, the main DL methods are reviewed, highlighting contributions and significance in the field. After that, comparative results on several publicly available datasets are given for the described methods, following up with a discussion. Finally, we point out a set of promising future works and draw our conclusions about building extraction based on DL techniques.