Building change detection by W-shape resunet++ network with triple attention mechanism

Building change detection in high resolution remote sensing images is one of the most important and applied topics in urban management and urban planning. Different environmental illumination conditions and registration problem are the most error resource in the bitemporal images that will cause pseudochanges in results. On the other hand, the use of deep learning technologies especially convolutional neural networks (CNNs) has been successful and considered, but usually causes the loss of shape and detail at the edges. Accordingly, we propose a W-shape ResUnet++ network in which images with different environmental conditions enter the network independently. ResUnet++ is a network with residual blocks, triple attention blocks and Atrous Spatial Pyramidal Pooling. ResUnet++ is used on both sides of the network to extract deeper and discriminator features. This improves the channel and spatial inter-dependencies, while at the same time reducing the computational cost. After that, the Euclidean distance between the features is computed and the deconvolution is done. Also, a dual loss function is designed that used the weighted binary cross entropy to solve the unbalance between the changed and unchanged data in change detection training data and in the second part, we used the mask–boundary consistency constraints that the condition of converging the edges of the training data and the predicted edge in the loss function has been added. We implemented the proposed method on two remote sensing datasets and then compared the results with state-of-the-art methods. The F1 score improved 1.52 % and 4.22 % by using the proposed model in the first and second dataset, respectively

Evaluation of the potential of aerial thermal imagery to generate 3D point clouds

This research evaluates the ability of thermal images obtained from aerial platforms to produce 3D point clouds. In this study, the thermal camera is first calibrated. Then, in order to avoid data redundancy, the key frames of the obtained thermal video are separated from other frames. Afterwards, the point clouds are generated and then the thermal ortho image is created from the key frames. The evaluation is done using visible orthophoto, ground control points and the linearity of the edges of buildings extracted from thermal images. The results of this study show that the thermal ortho image matches the visible ortho image with a good accuracy in the study area. Moreover, the standard deviation of the edges of the buildings has been calculated for a number of reconstructed buildings in thermal ortho with proper dispersion. 77% of the measurements taken from the edges of the buildings coincide with a straight line with an accuracy of better than two pixels, and about half of these values are extracted with an accuracy of better than a pixel

Decision-based fusion of pansharpened VHR satellite images using two-level rolling self-guidance filtering and edge information

Pan-sharpening (PS) fuses low-resolution multispectral (LR MS) images with high-resolution panchromatic (HR PAN) bands to produce HR MS data. Current PS methods either better maintain the spectral information of MS images, or better transfer the PAN spatial details to the MS bands. In this study, we propose a decision-based fusion method that integrates two basic pan-sharpened very-high-resolution (VHR) satellite imageries taking advantage of both images simultaneously. It uses two-level rolling self-guidance filtering (RSGF) and Canny edge detection. The method is tested on Worldview (WV)-2 and WV-4 VHR satellite images on the San Fransisco and New York areas, using four PS algorithms. Results indicate that the proposed method increased the overall spectral-spatial quality of the base pan-sharpened images by 7.2% and 9.8% for the San Fransisco and New York areas, respectively. Our method therefore effectively addresses decision-level fusion of different base pan-sharpened images

Improving semantic segmentation of high-resolution remote sensing images using Wasserstein generative adversarial network

Semantic segmentation of remote sensing images with high spatial resolution has many applications in a wide range of problems in this field. In recent years, the use of advanced techniques based on fully convolutional neural networks have achieved high and impressive accuracies. However, the labels of different classes are estimated independently in this method. In general, the segmentation effect is too coarse to take the relationship between pixels into account. On the other hand, due to the use of convolution filters and limitations of calculations, the field of view information of these filters will be limited in deep layers. In this study, a method based on generative adversarial network (GAN) is proposed to strengthen spatial vicinity in the output segmentation map. The segmentation model receive assistance from the GAN model in the form of a higher order potential loss. Furthermore, for better stability and performance in model training the Wasserstein GAN is used for optimization of the model. We successfully show an increase in semantic segmentation accuracy using the challenging ISPRS Vaihingen benchmark dataset

Automatic road crack recognition based on deep learning networks from UAV imagery

Roads are one of the essential transportation infrastructures that get damaged over time and affect economic development and social activities. Therefore, accurate and rapid recognition of road damage such as cracks is necessary to prevent further damage and repair it in time. The traditional methods for recognizing cracks are using survey vehicles equipped with various sensors, visual inspection of the road surface, and recognition algorithms in image processing. However, performing recognition operations using these methods is associated with high costs and low accuracy and speed. In recent years, the use of deep learning networks in object recognition and visual applications has increased, and these networks have become a suitable alternative to traditional methods. In this paper, the YOLOv4 deep learning network is used to recognize four types of cracks transverse, longitudinal, alligator, and oblique cracks utilizing a set of 2000 RGB visible images. The proposed network with multiple convolutional layers extracts accurate semantic feature maps from input images and classifies road cracks into four classes. This network performs the recognition process with an error of 1% in the training phase and 77% F1-Score, 80% precision, 80% mean average precision (mAP), 77% recall, and 81% intersection over union (IoU) in the testing phase. These results demonstrate the acceptable accuracy and appropriate performance of the model in road crack recognition

Empowering students with geospatial solutions through challenge-based learning

Today, the field of Geospatial Solutions primarily focuses on spatial and mapping data, analysis, and technologies that primarily revolve around place and space. It is considered more as a tool or means rather than the ultimate objective of various interdisciplinary activities, where minimal attention is given to theoretical aspects, equations, and underlying principles of the subject. Conversely, despite advancements in science and technology and a broader audience for geospatial subjects, it is predominantly taught conventionally, disregarding the diverse needs and expectations of students. In recent years, there has been an exploration of innovative educational methods to utilize new pedagogical frameworks and enhance academic performance among students. The present study aims to develop a framework and provide guidelines for the integration of Challenge Based Learning into Geomatics education. This framework consists of three interconnected phases: engage, investigate, and act. Subsequently, an educational pilot program is created and implemented to apply the designed framework to key topics such as food security and cultural heritage. Finally, the project refines the educational framework based on real pilot attempts and evaluation results, identifying potential issues and making necessary adjustments. The designed framework and the attained results are made publicly available for reference and utilization.</p

AUTOMATIC ROAD CRACK RECOGNITION BASED ON DEEP LEARNING NETWORKS FROM UAV IMAGERY

Roads are one of the essential transportation infrastructures that get damaged over time and affect economic development and social activities. Therefore, accurate and rapid recognition of road damage such as cracks is necessary to prevent further damage and repair it in time. The traditional methods for recognizing cracks are using survey vehicles equipped with various sensors, visual inspection of the road surface, and recognition algorithms in image processing. However, performing recognition operations using these methods is associated with high costs and low accuracy and speed. In recent years, the use of deep learning networks in object recognition and visual applications has increased, and these networks have become a suitable alternative to traditional methods. In this paper, the YOLOv4 deep learning network is used to recognize four types of cracks transverse, longitudinal, alligator, and oblique cracks utilizing a set of 2000 RGB visible images. The proposed network with multiple convolutional layers extracts accurate semantic feature maps from input images and classifies road cracks into four classes. This network performs the recognition process with an error of 1% in the training phase and 77% F1-Score, 80% precision, 80% mean average precision (mAP), 77% recall, and 81% intersection over union (IoU) in the testing phase. These results demonstrate the acceptable accuracy and appropriate performance of the model in road crack recognition

ARCHITECTURAL HERITAGE 3D MODELLING USING UNMANNED AERIAL VEHICLES MULTI-VIEW IMAGING

Today, Architectural Heritage 3D models are created using Unmanned Aerial Vehicles (UAV) imagery and processing through Computer Vision (CV) methods. They are becoming more acceptable as reliable sources for study, documentation, diagnostics, intervention planning, monitoring, and management decision-making. The Deir-e-Kaj caravanserai, located in Qom, Iran, is a massive and half-destroyed architectural heritage that belongs to the Seljuk era. The obstructed access due to illegal deep excavations and the extensive demolished structure did not allow for a complete mapping using traditional and terrestrial techniques. Considering the condition and vulnerability of the artifact, it looks necessary to use a safe, non-destructive, and remote method for 3D documenting. The literature review shows in most of the research UAVs are used for acquiring nadir images, which are combined with the terrestrially acquired data for complete 3D modelling. In this case, a multi-view UAV imaging strategy is considered for the as-is 3D modelling of Deire-e-Kaj. Three different imaging angles are designed and used to carry out the comprehensive and all-needed data acquisition. The nadir images are acquired to cover the plan and enclosure, and the horizontal and oblique images cover the façades and interior spaces of the artifact. Adopting a suitable photogrammetric process based on the SfM workflow allows for obtaining an accurate, high-quality, and textured 3D model of the caravanserai. Accuracy evaluation of the result using Ground Control Points shows a total accuracy of &plusmn;1 cm. This study demonstrates the efficiency of multi-view UAV photogrammetry as a rapid, safe, and precise method to create a complete 3D model of massive, hard-to-access, and vulnerable Architectural Heritage