scholarly journals SEMANTIC SEGMENTATION AND UNREGISTERED BUILDING DETECTION FROM UAV IMAGES USING A DECONVOLUTIONAL NETWORK

2018 ◽  
Vol XLII-2 ◽  
pp. 419-424
Author(s):  
S. Ham ◽  
Y. Oh ◽  
K. Choi ◽  
I. Lee
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Semantic Segmentation ◽  
Geospatial Data ◽  
Aerial Images ◽  
Building Detection ◽  
Urban Management ◽  
Probability Map ◽  
Gis Data ◽  
Uav Images ◽  
Uav Image

Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.

scholarly journals A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3921 ◽  
Author(s):  
Wuttichai Boonpook ◽  
Yumin Tan ◽  
Yinghua Ye ◽  
Peerapong Torteeka ◽  
Kritanai Torsri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Building Detection ◽  
Network Training ◽  
Building Information ◽  
Open Standard ◽  
Uav Images ◽  
Uav Image

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

scholarly journals IMPROVING 3D MESH QUALITY USING MULTI-DIRECTIONAL UAV IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 419-423 ◽  
Author(s):  
J. Kang ◽  
S. Lee ◽  
S. Yeon ◽  
S. Park
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Spatial Information ◽  
Point Clouds ◽  
Aerial Images ◽  
Mesh Quality ◽  
High Quality ◽  
3D Mesh ◽  
Uav Images ◽  
Oblique Images ◽  
Real Building

<p><strong>Abstract.</strong> With the increasing use of 3D spatial information, updated 3D spatial information is required and unmanned aerial vehicles (UAVs) are being used to accomplish this task. In general, 3D spatial information is constructed by generating meshes through point clouds extracted from many images. However, generating a mesh that is comparable to a real building is difficult because UAV images use relatively cheap sensors compared to aerial images. In this study, Nadir images with various overlapping degrees were obtained and point clouds generated to compare the reproducibility of actual buildings. This led to the confirmation that the use of Nadir images alone does not fully complement the occlusion even when the images are taken with high overlapping degrees. To complement such limitations of unidirectional images, oblique images were used in addition to create point clouds, and the occlusion was found to have been complemented. Furthermore, through its conversion, high quality 3D spatial information data could be obtained.</p>

scholarly journals Using Unmanned Aerial Vehicles (UAV) for mornitoring the construction progress of Ho Chi Minh City urban railway project

2015 ◽  
Vol 18 (2) ◽  
pp. 104-112
Author(s):  
Nhat Huu Nguyen ◽  
Tam Minh Dao ◽  
Trung Van Le ◽  
Chon Trung Le
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicles ◽  
Aerial Images ◽  
3D Objects ◽  
Ground Control Points ◽  
Aerial Vehicles ◽  
High Resolution Imagery ◽  
Gis Data ◽  
High Flexibility ◽  
Railway Construction Project

This paper describes a new approach for monitoring the construction progress of the Urban Railway Construction Project “Metro line1 - Ben Thanh - Suoi Tien” by using Unmanned Aerial Vehicles (UAV) to capture high resolution imagery at different stages of the project. The advantage of the AscTec Falcon 8 systems lies in their high flexibility and efficiency in capturing the surface of an area from a low flight altitude. In addition, further information such as orthoimages, elevation models and 3D objects can easily be processed by Pix4Dmapper software. The Ground Control Points (GCPs) and GIS data were used to compare the achieved accuracy of UAV method. This study shows the feasibility of using an UAV system for acquiring the high resolution aerial images and the new opportunities for managing construction progress over time.

scholarly journals Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 993
Author(s):  
Yingying Kong ◽  
Bowen Zhang ◽  
Biyuan Yan ◽  
Yanjuan Liu ◽  
Henry Leung ◽  
...  
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Region Of Interest ◽  
Semantic Segmentation ◽  
Geographical Information ◽  
Aerial Image ◽  
Surface Model ◽  
Uav Images ◽  
Uav Image ◽  
Multiple Metrics

Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM). We introduce an Affiliated Fusion Conditional Random Field (AF-CRF), which combines the information of visual pictures and DSM, and a multi-scale strategy with attention to improve the segmenting results. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics.

Discovering Potential Illegal Construction Within Building Roofs from UAV Images Using Semantic Segmentation and Object-Based Change Detection

2021 ◽  
Vol 87 (4) ◽  
pp. 263-271
Author(s):  
Yang Liu ◽  
Yujie Sun ◽  
Shikang Tao ◽  
Min Wang ◽  
Qian Shen ◽  
...  
Keyword(s):  
Change Detection ◽  
Semantic Segmentation ◽  
Small Object ◽  
Detection Scheme ◽  
Building Roof ◽  
Object Based ◽  
Detection Stage ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Uav Image

A novel potential illegal construction (PIC) detection method by bitemporal unmanned aerial vehicle (UAV ) image comparison (change detection) within building roof areas is proposed. In this method, roofs are first extracted from UAV images using a depth-channel improved UNet model. A two-step change detection scheme is then implemented for PIC detection. In the change detection stage, roofs with appearance, disappearance, and shape changes are first extracted by morphological analysis. Subroof primitives are then obtained by roof-constrained image segmentation within the remaining roof areas, and object-based iteratively reweighted multivariate alteration detection (IR-MAD ) is implemented to extract the small PICs from the subroof primitives. The proposed method organically combines deep learning and object-based image analysis, which can identify entire roof changes and locate small object changes within the roofs. Experiments show that the proposed method has better accuracy compared with the other counterparts, including the original IR-MAD, change vector analysis, and principal components analysis-K-means.

scholarly journals Seeding Crop Detection Framework Using Prototypical Network Method in UAV Images

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Di Zhang ◽  
Feng Pan ◽  
Qi Diao ◽  
Xiaoxue Feng ◽  
Weixing Li ◽  
...  
Keyword(s):  
Aerial Images ◽  
Large Field ◽  
Small Scale ◽  
Agricultural Insurance ◽  
Aerial Vehicle ◽  
Simple Linear Iterative Clustering ◽  
Evaluation Dataset ◽  
Uav Images ◽  
Uav Image ◽  
Candidate Regions

With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the problem of locating chili seedling crops in large-field UAV images is processed. Two problems are encountered in the location process: a small number of samples and objects in UAV images are similar on a small scale, which increases the location difficulty. A detection framework based on a prototypical network to detect crops in UAV aerial images is proposed. In particular, a method of subcategory slicing is applied to solve the problem, in which objects in aerial images have similarities at a smaller scale. The detection framework is divided into two parts: training and detection. In the training process, crop images are sliced into subcategories, and then these subcategory patch images and background category images are used to train the prototype network. In the detection process, a simple linear iterative clustering superpixel segmentation method is used to generate candidate regions in the UAV image. The location method uses a prototypical network to recognize nine patch images extracted simultaneously. To train and evaluate the proposed method, we construct an evaluation dataset by collecting the images of chilies in a seedling stage by an UAV. We achieve a location accuracy of 96.46%. This study proposes a seedling crop detection framework based on few-shot learning that does not require the use of labeled boxes. It reduces the workload of manual annotation and meets the location needs of seedling crops.

scholarly journals Demolished building detection from aerial imagery using deep learning

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Shu Su ◽  
Takahiko Nawata
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Detection Rate ◽  
Semantic Segmentation ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Tree Cover ◽  
Building Detection ◽  
Classification Problems ◽  
Novel Approach

<p><strong>Abstract.</strong> In this paper, we present a novel approach for demolished building detection using bi-temporal aerial images and building boundary polygon data. The building boundary polygon data can enable the proposed method to distinguish buildings from non-buildings. Moreover, it can enable the exclusion of non-building changes such as those caused by changes in tree cover, roads, and vegetation. The results of demolished building detection can be achieved by using the building-base. The proposed method classifies each building as demolished or undemolished. The architectures, which based on U-Net and VGG19, are implemented for realizing automatic demolished building detection. The result suggested that U-Net is a useful architecture for image classification problems as well as for semantic segmentation tasks. In order to verify the effectiveness of proposed method, the detection performance is evaluated using images of an entire city. The results suggest that the proposed method can accurately detect demolished buildings with a low mis-detection rate and low over-detection rate.</p>

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

scholarly journals Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3813
Author(s):  
Athanasios Anagnostis ◽  
Aristotelis C. Tagarakis ◽  
Dimitrios Kateris ◽  
Vasileios Moysiadis ◽  
Claus Grøn Sørensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Automated Detection ◽  
Aerial Images ◽  
Training Dataset ◽  
Field Boundary ◽  
Different Seasons ◽  
Detection And Localization ◽  
Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

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