Airborne laser scanning has been a valuable tool for forestry applications since it began to be used commercially. Thanks to the high 3D resolution provided by the Light Detection and Ranging (LiDAR) point cloud, it has provided great convenience in complex 3D modeling processes needed for forestry applications such as forest inventory, forest management, determination of carbon stocks and the characterization of biodiversity. LiDAR data provides a new dimension in forestry applications with its high 3D resolution and multiple return characteristics. The extraction of woodland areas from the LiDAR point cloud has great importance for automating the determination of tree heights, species and stand frequency which will be used for generating canopy height models (CHM). In this study, woodland areas in the urban scene were automatically extracted by using the multiple return properties of the LiDAR point cloud. The proposed approach consists of three major steps namely pre-processing, parameter calculation and k-d tree search for trees which were implemented in MATLAB. In the first step, multiple return points have been identified from the LiDAR point cloud, which will be then used to determine possible tree locations. Then, by using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, neighborhood relations among the multi return points which were extracted from the initial point cloud data, were formed and a rule-based filter was applied by taking advantage of neighborhood relations. In addition, the initial point cloud was filtered with the Cloth Simulation Filtering (CSF) algorithm to separate ground and non-ground points where non-ground points used to extract trees. In the second step, non-vegetation points were removed by applying a threshold based on the change of curvature and planarity parameters, which are derived from the filtered non–ground point cloud. In the last step, in order to extract trees, a k-d tree structure was created from the filtered non-ground points to find nearest neighbors of each multi return point within a given diameter in the k-d tree structure. In order to evaluate the accuracy of the approach, the extracted boundaries were compared with the manually digitized woodland boundaries from the true orthophoto of the study area using correctness, completeness and quality metrics.

Three-dimensional (3D) spatial information of object points is a vital requirement for many disciplines. Laser scanning technology and techniques based on image matching have been used extensively to produce 3D dense point clouds. These data are used frequently in various applications, such as the generation of digital surface model (DSM)/digital terrain model (DTM), extracting objects (e.g., buildings, trees, and roads), 3D modelling, and detecting changes. The aim of this study was to extract the building roof points automatically from the 3D point cloud data created via the image matching techniques with optical aerial images (with red, green, and blue band (RGB) and infrared (IR)). In the first stage of the study, as an alternative to laser scanning technology, which is more expensive than optical imaging systems, the 3D point clouds were produced by matching high-resolution images using a Semi Global Matching algorithm. The normalized difference vegetation index (NDVI) values for each point were calculated using the spectral information (RGB + IR) in the 3D point cloud data, and the points that represented the vegetation cover were determined using these values. In the second stage, existing ground and non-ground points that were free of vegetation cover were determined within the point cloud. Subsequently, only the points on the roof of the building were detected automatically using the proposed algorithm. Thus, points of the roofs of buildings located in areas with different topographic characteristics were detected automatically detected using only images. It was determined that the average values of correctness (Corr), completeness (Comp), and quality (Q) of the pixel-based accuracy analysis metrics were 95%, 98%, and 93%, respectively, in the selected test areas. According to the results of the accuracy analysis, it is clear that the proposed algorithm is very successful in automatic extraction of building roof points.

Building extraction from remotely sensed images plays an important role in many applications such as updating geographical information system, change detection, urban planning, disaster management and 3D building modeling. Automatic extraction of buildings from aerial images is not an easy task because of background complexity, lighting conditions and vegetation cover that reduces separability or visibility of buildings. As a result, automatic building extraction can be a complex process for computer vision and image processing techniques. In order to overcome this difficulty region-based active contour model was used to automatically detect the boundary of buildings for this study. To extract object boundaries, the model grows or shrinks the initial contour in the image. The main objective of this paper is making active contours algorithm perform without user interaction and to detect automatically initial contours to segment buildings with a software coded in Matlab. This task carried out by morphological operations, band ratio and thresholding methods. In this study, high resolution aerial images with 8 cm ground sampling distance (GSD) were used. Three separate test zones were selected with varying building level of detail on these images. Finally, it was assessed the accuracy of segmented buildings using Correctness, Completeness and Quality metrics by comparing the results images and manually digitized reference image. The proposed approach for building extraction from images was shown to be 98% accurate on buildings with simple geometry and homogeneous roof textures. However accuracy of extracted buildings with heterogeneous roof textures and lighting, and complex geometry is 89%. The results clearly show that automatically calculated initial contour positions work in accordance with the active contour algorithm and easily extraction of the buildings boundaries.

Earthquakes are natural disasters that cannot be determined precisely where and when they will occur. In cases where precautions are insufficient, large losses of life and property can occur. Minimizing the loss of life after an earthquake depends on the rapid identification of collapsed structures and the urgent delivery of rescue teams to heavily damaged structures. Within the scope of the study, damaged and destroyed buildings were automatically detected in a very short time after the earthquake by using the LiDAR point cloud data obtained with a single period flight and the cadastral map of the region. With the algorithm produced, the robust, damaged and destroyed building classes were detected with 98.98% and 98.56% accuracy rates by considering 3D geometric changes in two different study areas. After obtaining LiDAR data, the detection of damaged and collapsed buildings can be performed within two hours. These findings demonstrate the potential of the proposed approach to effectively detect damaged and destroyed buildings after a disaster. The map of damaged and collapsed buildings after the earthquake is produced without being dependent on radiometric changes. This process is carried out in a very short time with a high accuracy rate, which reveals the superiority of the study compared to the literature. The studies conducted with satellite images without 3D analysis capabilities, it is not possible to detect collapsed buildings with undamaged roofs. In contrast, the proposed LiDAR-based method can detect such damaged structures with high accuracy. The method accelerates the planning and implementation of post-earthquake rescue and relief operations. In addition, the fact that it can prevent loss of life due to late interventions increases the importance of the proposed study.

Accurate individual tree health assessment is essential for effective forest monitoring, particularly in complex forest stands where neighboring crowns introduce shadow effects and spectral mixing. This study proposes an adaptive Region of Interest (ROI) approach dynamically scaled according to individual tree crown metrics, integrating UAV-derived multispectral imagery and LiDAR point clouds acquired from a Scots pine forest in Kozağaç village, Gümüşhane, Türkiye. Individual trees were automatically detected using a Canopy Height Model (CHM), and crown width, crown area, and spatial parameters were extracted to generate dynamically scaled ROI windows. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) were then calculated within these windows to classify individual trees as unhealthy, moderately healthy, or healthy. Compared with fixed crown area windows, the proposed adaptive ROI approach improved all classification metrics for both NDVI and NDRE. Correctness increased from 79% to 81% for NDVI and from 63% to 66% for NDRE. Similarly, completeness increased from 68% to 81% and from 59% to 64%, IoU from 52% to 68% and from 42% to 48%, and F1 scores from 68% to 80% and from 59% to 65%, respectively. These findings demonstrate the potential of adaptive ROI sampling to improve individual tree health classification and support UAV-based forest health monitoring.

Rapid urbanization has led to an increase in construction activities, making it necessary that buildings are constructed in compliance with approved projects and current legal regulations. Buildings constructed in accordance with expert approved designs can maintain their structural integrity, particularly during natural disasters. This plays a significant role in ensuring the safety of life and property. In addition, buildings constructed based on approved projects provide important economic contributions to the public budget through taxation systems. In this context, the regular monitoring of urban areas and keeping spatial data up to date are of great importance for planning, inspection, and legal property management. This study aims to automatically detect illegal buildings that are constructed in violation of approved projects or are not legally permitted. Within this scope, building detection was carried out using three dimensional (3D) data obtained by Unmanned Aerial Vehicles (UAVs), and the results were analyzed with respect to cadastral data. As input data, a 3D point cloud produced from optical images acquired during single period UAV flights and 1:1000 scale base maps obtained from the Arsin Municipality of Trabzon were used. After eliminating vegetation and ground points, building roof points were automatically identified. Then, building polygons and roof points are analyzed in a three dimensional environment using a rule based approach to automatically identify illegal structures. The proposed approach successfully detected all the buildings that were manually identified by the municipality within the study area and additionally identified 7 extra illegal structures or building extensions. Preventive measures based on automatic illegal building detection may help reduce the number of people affected by natural disasters and prevent or reduce property related tax losses faced by public institutions.