scholarly journals Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

2020 ◽  
Vol 12 (9) ◽  
pp. 1444 ◽  
Author(s):  
Abolfazl Abdollahi ◽  
Biswajeet Pradhan ◽  
Nagesh Shukla ◽  
Subrata Chakraborty ◽  
Abdullah Alamri
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Traffic Management ◽  
Google Earth ◽  
Future Research ◽  
Learning Approaches ◽  
Road Extraction ◽  
Research Subjects ◽  
Remote Sensing Images ◽  
Generative Adversarial Network

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.

scholarly journals Road Extraction from Remotely Sensed Data: A Review

2021 ◽  
Author(s):  
Mohd Jawed Khan ◽  
Pankaj Pratap Singh
Keyword(s):  
Deep Learning ◽  
Traffic Management ◽  
Remotely Sensed ◽  
Future Research ◽  
Learning Approaches ◽  
Road Extraction ◽  
Remotely Sensed Data ◽  
Remotely Sensed Imagery ◽  
Urban Rural ◽  
Future Research Directions

Up-to-date road networks are crucial and challenging in computer vision tasks. Road extraction is yet important for vehicle navigation, urban-rural planning, disaster relief, traffic management, road monitoring and others. Road network maps facilitate a great number of applications in our everyday life. Therefore, a systematic review of deep learning approaches applied to remotely sensed imagery for road extraction is conducted in this paper. Four main types of deep learning approaches, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models are presented in this paper. We also compare these various deep learning models applied to remotely sensed imagery to show their performances in extracting road parts from high-resolution remote sensed imagery. Later future research directions and research gaps are described.

scholarly journals Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

2020 ◽  
Vol 12 (18) ◽  
pp. 2985 ◽  
Author(s):  
Yeneng Lin ◽  
Dongyun Xu ◽  
Nan Wang ◽  
Zhou Shi ◽  
Qiuxiao Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Network Models ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Proposed Model ◽  
Wide Range ◽  
Network Modules ◽  
Very High

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

scholarly journals An Oil Well Dataset Derived from Satellite-Based Remote Sensing

2021 ◽  
Vol 13 (6) ◽  
pp. 1132
Author(s):  
Zhibao Wang ◽  
Lu Bai ◽  
Guangfu Song ◽  
Jie Zhang ◽  
Jinhua Tao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
State Of The Art ◽  
Google Earth ◽  
Oil Wells ◽  
Oil Well ◽  
Optical Remote Sensing ◽  
Learning Models ◽  
Remote Sensing Images

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

Combined Improved Dirichlet Models and Deep Learning Models for Road Extraction from Remote Sensing Images

2021 ◽  
pp. 1-20
Author(s):  
Ziyi Chen ◽  
Cheng Wang ◽  
Jonathan Li ◽  
Bineng Zhong ◽  
Jixiang Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Road Extraction ◽  
Learning Models ◽  
Remote Sensing Images

scholarly journals WSGAN: An Improved Generative Adversarial Network for Remote Sensing Image Road Network Extraction by Weakly Supervised Processing

2021 ◽  
Vol 13 (13) ◽  
pp. 2506
Author(s):  
Anna Hu ◽  
Siqiong Chen ◽  
Liang Wu ◽  
Zhong Xie ◽  
Qinjun Qiu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Road Network ◽  
Road Networks ◽  
Google Earth ◽  
Binary Image ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Weakly Supervised

Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The pair segmentation images are not easy to prepare. Thus, to alleviate the burden of acquiring large quantities of training images, this study designed an improved generative adversarial network to extract road networks through a weakly supervised process named WSGAN. The proposed method is divided into two steps: generating the mapping image and post-processing the binary image. During the generation of the mapping image, unlike other road extraction methods, this method overcomes the limitations of manually annotated segmentation images and uses mapping images that can be easily obtained from public data sets. The residual network block and Wasserstein generative adversarial network with gradient penalty loss were used in the mapping network to improve the retention of high-frequency information. In the binary image post-processing, this study used the dilation and erosion method to remove salt-and-pepper noise and obtain more accurate results. By comparing the generated road network results, the Intersection over Union scores reached 0.84, the detection accuracy of this method reached 97.83%, the precision reached 92.00%, and the recall rate reached 91.67%. The experiments used a public dataset from Google Earth screenshots. Benefiting from the powerful prediction ability of GAN, the experiments show that the proposed method performs well at extracting road networks from remote sensing images, even if the roads are covered by the shadows of buildings or trees.

An Improved Deep-Learning Model for Road Extraction from Very-High-Resolution Remote Sensing Images

2021 ◽  
Author(s):  
Wangyao Shen ◽  
Yunping Chen ◽  
Yuanlei Cheng ◽  
Kangzhuo Yang ◽  
Xiang Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Model ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Deep Learning Model ◽  
Very High

scholarly journals Building Extraction from Very-High-Resolution Remote Sensing Images Using Semi-Supervised Semantic Edge Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2187
Author(s):  
Liegang Xia ◽  
Xiongbo Zhang ◽  
Junxia Zhang ◽  
Haiping Yang ◽  
Tingting Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Edge Detection ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Google Earth ◽  
Remote Sensing Images ◽  
Building Roof ◽  
Social Applications

The automated detection of buildings in remote sensing images enables understanding the distribution information of buildings, which is indispensable for many geographic and social applications, such as urban planning, change monitoring and population estimation. The performance of deep learning in images often depends on a large number of manually labeled samples, the production of which is time-consuming and expensive. Thus, this study focuses on reducing the number of labeled samples used and proposing a semi-supervised deep learning approach based on an edge detection network (SDLED), which is the first to introduce semi-supervised learning to the edge detection neural network for extracting building roof boundaries from high-resolution remote sensing images. This approach uses a small number of labeled samples and abundant unlabeled images for joint training. An expert-level semantic edge segmentation model is trained based on labeled samples, which guides unlabeled images to generate pseudo-labels automatically. The inaccurate label sets and manually labeled samples are used to update the semantic edge model together. Particularly, we modified the semantic segmentation network D-LinkNet to obtain high-quality pseudo-labels. Specifically, the main network architecture of D-LinkNet is retained while the multi-scale fusion is added in its second half to improve its performance on edge detection. The SDLED was tested on high-spatial-resolution remote sensing images taken from Google Earth. Results show that the SDLED performs better than the fully supervised method. Moreover, when the trained models were used to predict buildings in the neighboring counties, our approach was superior to the supervised way, with line IoU improvement of at least 6.47% and F1 score improvement of at least 7.49%.

scholarly journals A Two-Stage Deep Learning Registration Method for Remote Sensing Images Based on Sub-Image Matching

2021 ◽  
Vol 13 (17) ◽  
pp. 3443
Author(s):  
Yuan Chen ◽  
Jie Jiang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Matching ◽  
Google Earth ◽  
Geometric Transformation ◽  
Remote Sensing Images ◽  
Two Stage ◽  
Registration Method ◽  
Transformation Parameters ◽  
The Impact

The registration of multi-temporal remote sensing images with abundant information and complex changes is an important preprocessing step for subsequent applications. This paper presents a novel two-stage deep learning registration method based on sub-image matching. Unlike the conventional registration framework, the proposed network learns the mapping between matched sub-images and the geometric transformation parameters directly. In the first stage, the matching of sub-images (MSI), sub-images cropped from the images are matched through the corresponding heatmaps, which are made of the predicted similarity of each sub-image pairs. The second stage, the estimation of transformation parameters (ETP), a network with weight structure and position embedding estimates the global transformation parameters from the matched pairs. The network can deal with an uncertain number of matched sub-image inputs and reduce the impact of outliers. Furthermore, the sample sharing training strategy and the augmentation based on the bounding rectangle are introduced. We evaluated our method by comparing the conventional and deep learning methods qualitatively and quantitatively on Google Earth, ISPRS, and WHU Building Datasets. The experiments showed that our method obtained the probability of correct keypoints (PCK) of over 99% at α = 0.05 (α: the normalized distance threshold) and achieved a maximum increase of 16.8% at α = 0.01, compared with the latest method. The results demonstrated that our method has good robustness and improved the precision in the registration of optical remote sensing images with great variation.

scholarly journals Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss

2019 ◽  
Vol 11 (9) ◽  
pp. 1015 ◽  
Author(s):  
Hao He ◽  
Dongfang Yang ◽  
Shicheng Wang ◽  
Shuyang Wang ◽  
Yongfei Li
Keyword(s):  
Remote Sensing ◽  
Traffic Management ◽  
Structural Similarity ◽  
Semantic Segmentation ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Segmentation Methods ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.

scholarly journals Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2881 ◽  
Author(s):  
Leandro Parente ◽  
Evandro Taquary ◽  
Ana Silva ◽  
Carlos Souza ◽  
Laerte Ferreira
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Deep Learning ◽  
Random Forest ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Google Earth ◽  
Landsat 8 ◽  
Learning Approaches ◽  
Validation Data

The rapid growth of satellites orbiting the planet is generating massive amounts of data for Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region. The deep learning algorithms were implemented using TensorFlow, while the random forest utilized the Google Earth Engine platform. The accuracy assessment presented F1 scores for U-Net, LSTM, and random forest of, respectively, 96.94%, 98.83%, and 95.53% in the validation data, and 94.06%, 87.97%, and 82.57% in the test data, indicating a better classification efficiency using the deep learning approaches. Although the use of deep learning algorithms depends on a high investment in calibration samples and the generalization of these methods requires further investigations, our results suggest that the neural network architectures developed in this study can be used to map large geographic regions that consider a wide variety of satellite data (e.g., PlanetScope, Sentinel-2, Landsat-8).

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