This study focuses on the task of single tree segmentation in urban environments and improves the PDE-Net model to enhance its ability to process complex features. The original PDE-Net model has limitations when dealing with trees in a large area of land use. In this study, we propose an improved model, PDEC-Net, which combines the original model with the Encoder-Decoder structure, and introduces the Depthwise Separable Convolution layer to better capture the global and local relationships of point clouds. The experimental results show that the PDEC-Net model outperforms the original model in evaluation indicators such as Pre, Recall, and F1 score. Compared with the original PDE-Net, the PDEC-Net has increased Pre, Recall, and F1 score by 4.2%, 0.6%, and 3.2%, respectively. Meanwhile, the improved model also shows better adaptability when dealing with large-scale test sets. Therefore, the PDEC-Net model demonstrates stronger performance in single tree segmentation tasks, providing more powerful support for urban planning, management, and environmental monitoring.

This study addresses the challenge in traditional digital orthophoto map (DOM) generation methods, which typically require a large number of highly overlapping remote sensing images, making them unsuitable for acquiring high-resolution DOM over extensive areas using multirotor unmanned aerial vehicles (UAVs) at low flight altitudes. We propose a more efficient approach that utilizes a small number of low-overlap UAV images, eliminating the need for complex structure-from-motion (SfM) processes. The method employs the Oriented FAST and Rotated BRIEF (ORB) algorithm for feature matching, leveraging its advantages in speed, rotation invariance, and robustness under low-overlap conditions. Additionally, key steps such as camera distortion correction are incorporated. Through qualitative and quantitative assessments, this method demonstrates the capability to generate high-precision orthophoto images rapidly and with minimal human intervention, thus validating its feasibility.

In the context of promoting digital city infrastructure, achieving precise three dimension (3D) modeling of buildings has become increasingly vital. Oblique photogrammetry serves as a primary method for large-scale urban modeling. However, due to limitations in the capture angles and photo quality, the model generated by oblique photogrammetry often has some holes in the side of the building and the ground. To resolve this problem, this study introduces a method for building holes repair and modeling, utilizing point clouds generated from oblique photography reconstruction. The method integrates architectural knowledge and symmetry principles to devise a comprehensive scheme for repairing building surface point clouds. Based on this, the Poisson surface reconstruction algorithm are employed to reconstruct the building model, aiming to enhance both its accuracy and practicality.

With the development of unmanned aerial vehicles (UAV) and embedded development technology, more and more UAV remote sensing applications require real-time transmission of acquired image data or processed result data to ground workstations. In this case, the method, speed, and quality of data transmission will have a significant impact on the real-time performance of such UAV applications. In response to this issue, this study is based on an UAV ground target real-time recognition system, and discusses data transmission methods based on wireless local area network, Internet of Things technology and TCP/IP communication protocol. It explores data transmission methods based on 4G communication network internal network penetration and Cloud server combination, and conducts comparative experimental analysis on these methods. Finally, through experiments, it was found that the combination of 4G communication network and Cloud server is a better method for transmission performance. This method avoids the spatial distance limitations of traditional methods and achieves reliable and superior real-time image data transmission.

Leaf area index(LAI), defined as half of total leaf area per unit ground surface area, is a critical structural parameter. The objective of this paper is to validate MODIS LAI product(MOD15A2H) using decametric-scale sentinel-2 LAI and field measured LAI. The research was conducted in Yucheng station and the main crop types in Yucheng site are winter wheat and summer maize. After atmospheric correction and Snap software process, we obtain S2(Sentinel-2) 10m resolution LAI. The S2 10m LAI was validated by comparing with field measured LAI. The S2 10m LAI showed good agreement with field measured LAI(R2= 0.81, RMSE = 0.6). To convert the S2 10m LAI to the same 500m spacial resolution with MOD15A2H, we proposed a MODIS-Like upscaling method, the Sentinel-2 500m LAI was obtained. The MOD15A2H showed very good agreement with Sentinel-2 500m LAI(R2= 0.96, RMSE = 0.23). However, the comparison result of field measured LAI and MOD15A2H(R2= 0.68, RMSE = 0.58) was poor. Finally, we analyzed the time series of field measured LAI, sentinel-2 10m LAI, sentinel-2 500m LAI and MOD15A2H LAI.

Building extraction based on remote sensing can provide reliable geographic basic information and used in many fields. Deep learning has strong feature mining capabilities and is adept at solving image processing related problems such as image classification and object detection, thus having great potential in the field of building extraction. U-Net, as a deep convolutional neural network used for image segmentation tasks, can achieve precise pixel level segmentation. However, due to the limitations of its network structure, U-Net has a slight lack of accuracy in extracting buildings with small sizes, complex or fuzzy boundaries, and complex spatial distribution. For this reason, this study improves the U-Net network in these aspects: (1) The layers of the U-Net model are deepened for enhancing the feature extraction ability; (2) Fully convolutional network (FCN) decoder is introduced as an auxiliary loss function module to improve the efficiency and effect of the model training; (3) Elu activation function is introduced to improve the efficiency of model back propagation; and (4) Dice loss function is introduced to solve the problem of data imbalance. Compared to the original U-Net, the improved U-Net has better building extraction performance under complex spatial distribution and contours, with improvements of about 18.40%, 20.61%, and 19.69% in accuracy, recall, and F1 values, respectively.

Studies have shown that camera auto-exposure underestimates the LAI (leaf area index) measured by DHP (digital hemispheric photography) to varying degrees. To address this problem, this paper proposes the use of multi-exposure fusion to reconstruct information from canopy images to compensate for the loss of information caused by overexposure or underexposure of canopy images acquired by the camera in auto-exposure mode. By fusing a series of canopy images with different exposure times from the same canopy layer, the LAI is then calculated using DHP on the fused image. Experimental results show that the method improves the R2from 0.698 to 0.837 and reduces the RMSE from 0.87 to 0.37 compared with the automatic exposure mode of the camera, with LAI-2200 measurements as a reference. This method contributes to resolving the problem of underestimating LAI in DHP caused by the automatic exposure mode, thereby improving the accuracy of DHP.

In some scenes with complex terrain or features, the accuracy of the constructed three-dimensional (3D) based on oblique photogrammetry model is not ideal, and it often requires manual placement of ground control points (GCPs). What is the impact of GCPs on the accuracy of the constructed 3D model? This study designs a detailed experiment to explore this issue by collecting unmanned aerial vehicle (UAV) images and coordinates of control points in the testing area, adding different numbers of GCPs for 3D modeling, and finally conducting a systematic analysis of the accuracy of the 3D realistic model of the component from both qualitative and quantitative perspectives. The experiment shows that the effect of adding GCPs on the 3D model has been greatly improved, with the error in plane accuracy reduced to 1/14 of that without control points, and the horizontal and vertical deformations also decreased by about 10%. As the number of GCPs increases, the impact of enhancing 3D modeling precision will gradually diminish.

The semantic segmentation of vegetation point clouds has very important application value in the field of geosciences. It can distinguish vegetation regions from other regions, further classify and analyze the vegetation, and help us better understand the distribution and characteristics of vegetation to protect and manage natural resources. The PointNet and PointNet++ models use maximum pooling as the aggregation function, allowing the deep neural networks to classify unordered point clouds directly with high classification accuracy. However, their ability to extract spatial correlations and local features from point clouds is insufficient, which restricts the improvement of point clouds semantic segmentation accuracy and results in the poor processing of vegetation point clouds. To resolve this problem, this research designs the novel hierarchical point cloud transformer (HPCT) model, suitable for the semantic segmentation of multisource vegetation point clouds. Combined with deep learning techniques, different levels of features are processed hierarchically based on a hierarchical structure, and a Transformer module is combined in the feature extraction part, so as to obtain a larger receptive field and stronger semantic feature extraction capability. At the same time, we also propose a unified spatial scale sampling method for heterogeneous point cloud data input, which can be used not only for training and predicting the independent HPCT models with a single source of data, but also for training and predicting a unified HPCT model with multisource data. Semantic segmentation experiments are carried out on self-collected three-source data sets. The results show that the semantic segmentation performance of evaluation indicators (such asRecall,Pre,IoU, andOA) of the proposed HPCT model under the independent training and unified training on the three-source data exceed those of the PointNet, PointNet++, and PCT models, and even exceed some newly emerging models, such as PontCNN and DGCNN. The unified HPCT model has better segmentation performance than the independent HPCT model, with averageRecall,Pre,IoU, andOAindicators increasing by 1.07%, 1.73%, 4.33%, and 1.03%, respectively. We attribute this superior accuracy to the unified training with the three-source data. The averageRecall,Pre,IoU, andOAindicators of the unified HPCT model for the entire three-source data set exceed 96%, 98%, 95%, and 98%, respectively.

Landslides are common natural disasters that often cause serious impact and damage to human society. Since landslide disasters threaten people's production and life all the time, it is particularly important to predict the risk of landslides and to control landslide disasters. When studying landslide risk and deciding whether to treat the landslide, it is meaningful to classify and compare the risk of landslides so as to select those landslides with a higher degree of danger for priority treatment. The target of this paper is to extract factors related to landslide risk, and train a classification models for landslide risk. It employs ensemble machine learning algorithms to classify landslide hazards. Because the landslide feature has a large number of dimensions, this paper uses the PCA method to reduce the dimension. Due to the imbalance of the samples, this paper uses the SMOTE method to handle the imbalanced learning. The results of study show that the selected factors are highly related to landslide risk, the classification model in this paper has good accuracy.

Abstract In unmanned aerial vehicle (UAV) image‐processing applications, one needs to implement different parallel image‐enhancement algorithms on several high‐performance computing platforms utilizing various programming models. To speed up the parallelization procedure and improve its efficiency, the automatic parallel software package, Par4All, is applied in this work. We find that the performance of the original automatic parallelization algorithm produced with Par4All is inefficient. To resolve this problem, we propose different optimization approaches for Par4All based on Intel®'s Xeon Phi high‐performance computing platform that are based on the structural features of the image‐enhancement algorithms, which can further optimize the original parallel algorithm. These approaches mainly include: (1) Par4All automatic parallel search module optimization, (2) dynamic thread setting optimization, and (3) the collaborative parallelization of both CPU and many integrated core (MIC) processors. According to the results of the comparison experiments involving different algorithms, it is shown that the proposed optimization approaches for these kinds of algorithms can significantly improve the performance of automatic parallel algorithms. The acceleration ratio increases approximately by 30%, 70%, and 80% for the multiscale Retinex, Gaussian‐filtering and median‐filtering algorithms, respectively. As continuation and deepening of our previous research work, this research has the potential to be beneficial for other researchers in image‐processing applications with image‐enhancement algorithms.

Summary Image‐enhancement algorithms, for example, median filtering algorithms, Gaussian filtering algorithms, and the multiscale Retinex (MSR) algorithm, are widely used in unmanned aerial vehicle (UAV) image processing to resolve the problems of poor clarity, insufficient contrast, and weak adaptability of the aerial images. Aiming to improve the low efficiency of processing a large volume of UAV images using the MSR algorithm, this research realized a parallel MSR algorithm using the OpenMP programming model based on Intel's many integrated core (MIC) platform. First, the principle and serial implementation of the MSR algorithm were reviewed in detail, and the algorithm's hotspots were determined with the Intel VTune tool. Then, the corresponding parallel algorithm was designed and implemented. After checking the correctness of the parallel algorithm, systematical experiments on UAV images of different sizes were carried out. According to the experiments performed in the course of this work, the parallel MSR interpolation algorithm attained a speedup factor of 32 on two Intel MIC acceleration cards with 60 cores, which indicated that the parallel algorithm greatly reduced processing time and maximized speed and performance.

A growing number of GWSs over networks have emerged. However, with the increasing implementation of GWSs, the difficulty of service discovery increases as well. This paper focuses on providing highly accurate and efficient GWS discovery by incorporating several techniques such as information retrieval, semantic matching, and quality of service (QoS) constraints. Experimental results demonstrate that the proposed GWS search method not only can provide accurate search results, but also provides an enhanced user experience.

Now, the urbanization process of China is accelerating. Urban land price is of great interest for the government to make reasonable policies and keep the healthy development of land market. Based on the data source of dynamic monitoring system and the statistical yearbook of Chengdu city, we identifies the related factors influencing the comprehensive land price of Chengdu. Firstly, we identified the nine strongly correlative factors of land price of Chengdu city. Secondly, we derived the land price for prediction. Thirdly, we compared the predicted land price with the real land price in the period of 2014–2015. Finally, the comprehensive land price of Chengdu in the period of 2017–2018 was forecasted with random forests and neural network, respectively. According to the results, we found that the error of the random forests is much smaller than that of neural network. Thus, we utilized random forests to predict the comprehensive land price of Chengdu in the period of 2017–2018. Our results showed that the comprehensive land price of Chengdu in the next two years would be stable and rises slightly.

Traditional 3D map reconstruction methods based on unmanned aerial (UA) always relies on multi-camera optical equipment or additional space positioning equipment. All of these restrict the UA's application scenarios to some extent. Visual simultaneous localization and mapping (VSLAM), using the camera as the only external sensor, can construct a 3D map of its spatial environment while recording its own localization. This paper proposes a technique of fast 3D map reconstruction based on UAs by using VSLAM, with parallel computing based on CUDA, to achieve fast dense reconstruction based on a UA platform. This research belongs to one of the emerging but very exciting application areas using UAs. It helps to expand the application scope of UAs to some extent. From the experiments, it is demonstrated that the methodology presented in this paper works well, and could be applied to real world applications.

Directional brightness temperature (DBT) plays an important role in surface energy balance and urban climate. How to determinate the components' proportion efficiently is one of the key factors to accurate simulation of DBT. Usually, determining the proportion of each component in the sensor's field of view (FOV) uses the radiosity method. This approach is more complicated and highly accurate, however, the computational complexity increases dramatically with the decreasing splitting scale of the involved facet area. To solve this problem, this research uses message passing interface (MPI) programming model to design and implement a parallel Linux cluster-based components' proportion calculation method. The experimental results show that the speedup ratio of the algorithm increases with the decrease of the facet areas' splitting scales, and that the achieved speedup reaches to 100 times or so, i.e., the acceleration effect is satisfactory and meets the application's requirements.

This research proposes to build a universal, multi-layer, multi-level, multi-component system to achieve “low coupling, high cohesion” for remote sensing data distribution. Such a system can make the components reusable at a maximum extent and enhance the system's reusability and versatility. The object-relational spatial database and file management method based on Oracle GeoRaster is used to store multi-source RS image data. The entire system adopts the technologies like Web GIS and Java Web SSM framework. Through the test, the system can meet our expected requirements.

In this letter, a new method for denoising remote-sensing images based on partial differential equations (PDEs) is proposed. The method employs the similarity between the different band images in a multicomponent image. Initially, one of the noise-free images in multicomponent remote-sensing images as a prior is introduced into the PDE denoising method. To make use of the priors of the noise-free image in denoising, we construct a new smoothing term for the PDE so as to compute the total variation. The new smoothing term refers to a specific smoothing direction and a specific smoothing intensity of the reference image when denoising the noisy image. The proposed smoothing term is added as a new constraint into the PDE denoising method. Based on the proposed method, the similarity of the directions of the edges between the noisy image and the reference image enables the new algorithm to smooth out more noise and conserve more detail in the denoising process. We also present the discrete form of the proposed denoising model. Multispectral remote-sensing images and hyperspectral remote-sensing images are experimented in this letter. A better performance is achieved by the proposed method when compared with other methods.

How to manage and utilize huge amounts of spatial data more efficiently is very important for data sharing and applications. This paper presents the research work of a general model of data service in China Spatial Information Grid. This project is to provide a foundational infrastructure for sharing large quantity of earth observation data and earth science applications in grid environment. It is uneconomical to write different middleware for heterogeneous data sources, so how to provide a general model with standard interface becomes important. First we discuss the special problems of data service in SIG. Then we classify currently EO data providing modes into three types. In detail, we show layer design of this general model, and illustrate by query and access services, which are the most frequent operations for data service. Then we describe the implementation of this model, and analysis middleware and standard interface design to support multi-data combination.