Deep Fully Convolutional Networks for Cadastral Boundary Detection from UAV Images

2019 ◽  
Vol 11 (14) ◽  
pp. 1725 ◽  
Author(s):  
Xue Xia ◽  
Claudio Persello ◽  
Mila Koeva
Keyword(s):  
Urban Areas ◽  
Visual Cues ◽  
Field Work ◽  
Boundary Detection ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Automated Method ◽  
Study Sites ◽  
Uav Images ◽  
High Level

There is a growing demand for cheap and fast cadastral mapping methods to face the challenge of 70% global unregistered land rights. As traditional on-site field surveying is time-consuming and labor intensive, imagery-based cadastral mapping has in recent years been advocated by fit-for-purpose (FFP) land administration. However, owing to the semantic gap between the high-level cadastral boundary concept and low-level visual cues in the imagery, improving the accuracy of automatic boundary delineation remains a major challenge. In this research, we use imageries acquired by Unmanned Aerial Vehicles (UAV) to explore the potential of deep Fully Convolutional Networks (FCNs) for cadastral boundary detection in urban and semi-urban areas. We test the performance of FCNs against other state-of-the-art techniques, including Multi-Resolution Segmentation (MRS) and Globalized Probability of Boundary (gPb) in two case study sites in Rwanda. Experimental results show that FCNs outperformed MRS and gPb in both study areas and achieved an average accuracy of 0.79 in precision, 0.37 in recall and 0.50 in F-score. In conclusion, FCNs are able to effectively extract cadastral boundaries, especially when a large proportion of cadastral boundaries are visible. This automated method could minimize manual digitization and reduce field work, thus facilitating the current cadastral mapping and updating practices.

2D INVERSION OF DIPOLE-DIPOLE AXIAL ARRAY WITH LINEAR CAPACITIVE ELECTRODES FOR ENGINEERING TASKS

2019 ◽  
Vol 13 (2) ◽  
pp. 60-68
Author(s):  
A. Yu. Tatarskiy ◽  
A. F. Volynin
Keyword(s):  
Urban Areas ◽  
Geophysical Methods ◽  
Electrode Array ◽  
Field Work ◽  
Geological Structure ◽  
Industrial Facilities ◽  
Data Inversion ◽  
2D Data ◽  
High Level ◽  
2D Resistivity

Abstract: at present, the range of tasks solved by geophysical methods is expanding. This is facilitated by the progress in the development of geophysical equipment, methods for performing field work and data processing. At the same time, the conditions of research are complicated. Often it is the site of industrial facilities or urban areas. The main factors complicating the implementation of the geophysical researches at urban areas are the high level of industrial noise and solid coverage. The article discusses the possibility of using resistance technology with 2D data inversion when solving engineering problems in urban areas. The conditions of the use of the measurement technique with capacitive electrodes for approximation by grounded electrode array at a direct current are shown. The results of the comparison of the dipole-dipole method with capacitive electrode array and its equivalent with galvanic grounded electrodes are presented. To assess the effectiveness of using of the capacitive electrodes array for solving engineering tasks in the city, the authors carried out researches on a site of regularly emerging subsidence of pavement in the center of St. Petersburg. According to the research results, a 2D resistivity section of the pavement deformation was constructed. The results were interpreted using geological information. The studies revealed a spatial correlation of the identified anomalies of specific electrical resistances with the local site of the embankment pavement destruction. The possible causes of the identified anomalies are described. The results of electrical prospecting with linear capacitive electrodes can be used for detailing engineering geological structure of urbanized areas.

scholarly journals Detecting and Analyzing the Increase of High-Rising Buildings to Monitor the Dynamic of the Xiong’an New Area

2020 ◽  
Vol 12 (11) ◽  
pp. 4355
Author(s):  
Liwei Li ◽  
Jinming Zhu ◽  
Lianru Gao ◽  
Gang Cheng ◽  
Bing Zhang
Keyword(s):  
Spatial Distribution ◽  
Urban Areas ◽  
Driving Forces ◽  
Study Region ◽  
Convolutional Networks ◽  
North East ◽  
The North ◽  
Fully Convolutional Networks ◽  
Transportation Accessibility ◽  
Urban Dynamic

As an effort to monitor the urban dynamic of the Xiong’an new area, this paper proposed a novel procedure to detect the increase of High-Rising Buildings (HRBs) from multi-temporal Sentinel-2 data based on Fully Convolutional Networks. The procedure was applied to detect the increase of HRBs between 2017 and 2019 in 39 counties in the center of the Xiong’an new area. The detected increases were validated and then analyzed in terms of their quantities, spatial distribution and driving forces at the county level. The results indicate that our method can effectively detect the increase of HRBs in large urban areas. The quantity and spatial distribution of the increased HRBs varies a lot in the 39 counties. Most of the increase is located in the north-east and the mid-west of the study region. As to the driving forces, it seems that no single factor can fully explain the increase. Among the five selected factors, Gross Domestic Product (GDP) and transportation accessibility have clear high impacts than others. Number of Permanent Residents (NPR) and policy follow as the secondary group. The terrain has the lowest influence on the increase. Our method provides a useful tool to dynamically monitor HRBs in large areas and also the increase of HRBs can be employed as a new indicator to characterize urban development.

scholarly journals Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss

2021 ◽  
Vol 13 (16) ◽  
pp. 3119
Author(s):  
Chao Wang ◽  
Xing Qiu ◽  
Hai Huan ◽  
Shuai Wang ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Benchmark Datasets ◽  
High Level ◽  
Very High

Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbacks, including incomplete extraction of different sizes of buildings and inaccurate boundary prediction. It is attributed to a deficiency in the global context-aware and inaccurate correlation mining in the spatial context as well as failure to consider the relative positional relationship between pixels and boundaries. Hence, a detection method for earthquake-damaged buildings based on the object contextual representations (OCR) and boundary enhanced loss (BE loss) was proposed. At first, the OCR module was separately embedded into high-level feature extractions of the two networks DeepLabv3+ and UNet in order to enhance the feature representation; in addition, a novel loss function, that is, BE loss, was designed according to the distance between the pixels and boundaries to force the networks to pay more attention to the learning of the boundary pixels. Finally, two improved networks (including OB-DeepLabv3+ and OB-UNet) were established according to the two strategies. To verify the performance of the proposed method, two benchmark datasets (including YSH and HTI) for detecting earthquake-damaged buildings were constructed according to the post-earthquake images in China and Haiti in 2010, respectively. The experimental results show that both the embedment of the OCR module and application of BE loss contribute to significantly increasing the detection accuracy of earthquake-damaged buildings and the two proposed networks are feasible and effective.

scholarly journals Salient Object Detection Combining a Self-Attention Module and a Feature Pyramid Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1702
Author(s):  
Guangyu Ren ◽  
Tianhong Dai ◽  
Panagiotis Barmpoutis ◽  
Tania Stathaki
Keyword(s):  
Object Detection ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Salient Object Detection ◽  
Object Localization ◽  
Salient Object ◽  
Convolutional Networks ◽  
Multi Scale ◽  
Fully Convolutional Networks ◽  
High Level

Salient object detection has achieved great improvements by using the Fully Convolutional Networks (FCNs). However, the FCN-based U-shape architecture may cause dilution problems in the high-level semantic information during the up-sample operations in the top-down pathway. Thus, it can weaken the ability of salient object localization and produce degraded boundaries. To this end, in order to overcome this limitation, we propose a novel pyramid self-attention module (PSAM) and the adoption of an independent feature-complementing strategy. In PSAM, self-attention layers are equipped after multi-scale pyramid features to capture richer high-level features and bring larger receptive fields to the model. In addition, a channel-wise attention module is also employed to reduce the redundant features of the FPN and provide refined results. Experimental analysis demonstrates that the proposed PSAM effectively contributes to the whole model so that it outperforms state-of-the-art results over five challenging datasets. Finally, quantitative results show that PSAM generates accurate predictions and integral salient maps, which can provide further help to other computer vision tasks, such as object detection and semantic segmentation.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

scholarly journals Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips using Recurrent Fully Convolutional Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Jeremy M. Webb ◽  
Duane D. Meixner ◽  
Shaheeda A. Adusei ◽  
Eric C. Polley ◽  
Mostafa Fatemi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks
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