LIDAR is a recent and progressive technology for collecting data from surface that operates based on the laser length measurements. High planimetry and altimetry accuracy of the obtained LIDAR point-cloud, as well as the ability to record intensity are the reasons to utilize LIDAR data for detecting objects. Extracting roads as both important urban objects and connection channels of a country is vital significantly. In this paper, a hierarchical approach was proposed for extracting the main road network with acceptable precision. The proposed method eliminated non-road objects by using range and intensity data and applying some filters successively. Also, it prevented to produce gap and fracture in the road network. In this regard, firstly, three features were produced by specifying a threshold on the last intensity pulse and utilizing the last range pulses to obtain nDSM, as well as producing slope with normal vectors. The linear convolution of the produced feature layers was computed to obtain an initial road class. Subsequently, it was tried to remove noises from the initial road network and improve detection results according to the road geometrical characteristics. Finally, the skeleton morphological filter and Fourier features were used to smooth roads boundaries and to eliminate byroads. The evaluation results of the road extraction using our proposed approach achieved 80.56% Correctness and 77.82% Completeness. Generally, we tried to use all parameters that are useful for separating roads from other objects in order to extract the main road network with high accuracy and speed by applying them successively.

This study describes the verification of Wind Atlas Analysis and Application program (WAsP) modelled average wind speeds in a complex terrain. WAsP model was run using data collected at 3 masts: Kalkumpei, Nyiru and Sirima using cup anemometers and wind vanes for the entire 2009 calendar year and verified using data collected by WindTracer LIDAR (light detection and ranging) for 2 weeks from 11th to 24th July 2009. Evaluating WAsP mean wind speed map using LIDAR data showed that Nyiru station provides the best data to model mean wind speed over the wind farm domain with a mean difference of 0.16 m/s, root mean square error of 0.85 m/s and Index of Agreement of 0.61. Construction of a 310 MW windfarm has commenced at this site. Once completed, the windfarm will be operating 365 vestas V52-850kW turbines.

LIDAR as a powerful system has been known in remote sensing techniques for 3D data acquisition and modeling of the earth’s surface. 3D reconstruction of buildings, as the most important component of 3D city models, using LIDAR point cloud has been considered in this study and a new data-driven method is proposed for 3D buildings modeling based on City GML standards. In particular, this paper focuses on the generation of an Enhanced Level of Details 1 (E-LOD1) of buildings containing multi-level flat-roof structures. An important primary step to reconstruct the buildings is to identify and separate building points from other points such as ground and vegetation points. For this, a multi-agent strategy is proposed for simultaneous extraction of buildings and segmentation of roof points from LIDAR point cloud. Next, using a new method named “Grid Erosion” the edge points of roof segments are detected. Then, a RANSAC based technique is employed for approximation of lines. Finally, by modeling of the rooves and walls, the 3D buildings model is reconstructed. The proposed method has been applied on the LIDAR data over the Vaihingen city, Germany. The results of both visual and quantitative assessments indicate that the proposed method could successfully extract the buildings from LIDAR data and generate the building models. The main advantage of this method is the capability of segmentation and reconstruction of the flat buildings containing parallel roof structures even with very small height differences (e.g. 50 cm). In model reconstruction step, the dominant errors are close to 30 cm that are calculated in horizontal distance.

The main problems of hyper spectral data are large number of bands, high dependency between them and different signal to noise ratio in each band. To reduce dimensions of the feature space, minimizing noise and spectral dependence between bands, the MNF method has been applied to achieve better results in this paper. By applying this algorithm, the 144 bands of hyper spectral data were reduced to 19 suitable bands. Then from LIDAR data, the image height and intensity of the return signal received from the first and the last pulse of the laser were examined by LIDAR sensor. At last, the 19 spectral bands extracted from hyper spectral data have been fusion with 4 images of LIDAR data at the pixel level to create 23 suitable spectral bands. In order to detect and extract any study feature of the area on 23 spectral bands, seven different SVM methods were applied and finally by majority voting in the decision-making level between 7 obtained results, the class of each pixel was turned out. Morphology closing transform for repairing buildings and Hough transform for reconstructing the network effects of the fragmentation of land transportation were used on the results of pixels basis SVM method to regulate man-made side structure as well as the individual pixels which reduced. The results in this paper indicates the 99. 52% overall accuracy and. 958 kappa efficiency which compared to the GRSS chosen institution method. 0. 6 Kappa coefficient has been improved. Used data are air-borne LIDAR and hyper spectral scenes requested and downloaded from the organized of a recent contest in data fusion domain.

During the last decade airborne laser scanning (LIDAR) has become a mature technology which is now widely accepted for 3D data collection. Nevertheless, these systems have the disadvantage of not representing the desirable bare terrain, but the visible surface including vegetation and buildings. To generate high quality bare terrain using LIDAR data, the most important and difficult step is filtering, by which non-terrain 3D objects such as buildings and trees are eliminated while keeping terrain points for quality digital terrain modeling. The main goal of this paper is to investigate and compare the potential of procedures for clustering of LIDAR data for 3D object extraction. The study aims at a comparison of K-means clustering, SOM and fuzzy C-means clustering applied on range laser images. For evaluating the potential of each technique, the confusion matrix concept is employed and the accuracy evaluation is carried out both qualitatively and quantitatively.

The classification of Digital Surface Model (DSM) images derived from LIDAR sensors is a challenging task, particularly when distinct ground classes with identical height information must be distinguished. However, DSM images contain valuable spatial information that can be utilized to enhance classification accuracy. This paper proposes a novel strategy, called Multishape Morphological Two-Stage Convolutional Neural Network (MM2CNN), for DSM classification to achieve accurate classified land-cover maps. The proposed method combines the strengths of multishape morphological profiles (MMPs) and a two-stage CNN model as a smart algorithm to effectively discriminate between different land covers from a single-band DSM image. More precisely, the CNN, as a smart method, is used to learn hierarchical rich representations of the data, while the MMPs are used to extract spatial information from the DSM imagery. The approach involves generating MMPs with three structuring elements, training three parallel CNN models, and then stacking and feeding the probability maps to a second-stage CNN to predict the final pixel labels. Experimental results on the Trento benchmark DSM image show that the suggested technique achieves an overall accuracy of 97.32% in a reasonable time, outperforming some other DSM classification methods. The success of the MM2CNN technique demonstrates the potential of integrating MMPs with CNN for precise DSM classification, which has a wide range of applications in environmental investigations.

Estimation of riparian forest structural attributes, such as the Leaf Area Index (LAI), is an important step in identifying the amount of water use in riparian forest areas. In this study, small-footprint LIDAR data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the Upper San Pedro River Basin, Arizona, USA. Four metrics (tree height, height of median energy, ground return ratio, and canopy return ratio) were derived by synthetically constructing a large footprint LIDAR waveform from small-footprint LIDAR data which were compared to ground-based high-resolution Intelligent Laser Ranging and Imaging System (ILRIS) scanner images. These four metrics were incorporated into a stepwise regression procedure to predict field-derived LAI for different age classes of cottonwoods. This research applied the Penman-Monteith model to estimate transpiration of the cottonwood clusters using LIDAR-derived canopy metrics. These transpiration estimates compared very well to ground-based sap flux transpiration estimates indicating LIDAR-derived LA! can be used to improve riparian cottonwood water use estimates.

Remotely sensed data are used to estimate structural parameters in order to reduce the cost and time required for forest inventory. Nowadays, due to the launch of numerous remote sensing systems, a variety of remotely sensed datasets including LIDAR, radar, and optical images are available in different spatial and spectral resolutions. Therefore, it is important to evaluate and compare the performance of these datasets in retrieving structural parameters. Such comparison, however, has rarely been conducted in previous studies. Consequently, this study aims to compare different remotely sensed datasets used for structural parameter estimation. For this purpose, textural information extracted from Worldview-2 (WV-2) and SPOT-5 images and statistical attributes calculated for LIDAR-derived data were individually utilized to model the structural parameters of a Pinus radiataplantation. Comparing the results, while the WV-2 data are suitable to estimate stocking and mean diameter at breast height (DBH), mean height and stand volume were estimated more accurately by LIDAR-derived data. Meanwhile, there was no significant difference among remotely sensed datasets for basal area estimation. Finally, it was shown that the mean height and mean DBH were estimated more accurately than density-related structural parameters comprising of basal area, stand volume and stocking with more than 20% estimation error.

Consider an open quantum system S , interacting with its environment E. Whether the reduced dynamics of the system can be given by a linear map, or not, is an important subject, in the theory of open quantum systems. Dominy, Shabani and LIDAR have proposed a general framework for linear Hermitian reduced dynamics. They have considered the case that both the system and the environment are finite dimensional. Their framework can be generalized to include the case that the environment is infinite dimensional too. In this paper, after demonstrating this generalization, we discuss the role of the convexity of the set, of possible initial states of the system-environment, in their framework. Next, we give a proof for the existence of the operator sum representation, for arbitrary linear Hermitian map. This proof enables us to prove the Choi-Jamiołkowski and the Jamiołkowski isomorphisms, straightforwardly.