Airborne Light Detection and Ranging (LiDAR) data have been increasingly used for classification of urban areas in the last decades. Classification of urban areas is especially crucial to separate the area into classes for urban planning, mapping, and change detection monitoring purposes. In this study, an airborne LiDAR data of a complex urban area from Bergama District, İzmir, Turkey were classified into four classes; buildings, trees, asphalt road, and ground. Random Forest (RF) supervised classification method is selected as classification algorithm and pixel-wise classification was performed. Ground truth of the area was generated by digitizing classes into features to select training data and to validate the results. The selected study area from Bergama district is complex in urban planning of buildings, road, and ground. The buildings are very close to each other, and trees are also very close to buildings and sometimes cover the rooftops of buildings. The most challenging part of this study is to generate ground truth in such a complex area. According to the obtained classification results, the overall accuracy of the results is found as 70,20%. The experimental results showed that the algorithm promises reliable results to classify airborne LiDAR data into classes in a complex urban area.
The extraction of building roof planes from lidar data has become a popular research topic with random sample consensus (RANSAC) being one of the most commonly adopted algorithms. RANSAC extracts full planes, which is problematic when there are other points outside the plane boundary but within the plane space. This study proposes an improved RANSAC (I‐RANSAC) algorithm by removing points that do not belong to the roof plane. I‐RANSAC selects a random point from the extracted roof plane and then searches for its neighbours within a given threshold to identify and remove outliers. The new algorithm was tested with 14 buildings from two datasets, where quality control measures showed significant improvement over standard RANSAC.
After the declaration of Ankara as the capital city of Turkey in 1923, the size of the city was identified to be insufficient to cope with the developmental and spatial needs of the city. In this study, the analysis and detection of land cover changes were conducted for the last three decades with ten-year time interval by using remotely sensed satellite data in Ankara to monitor the change in land cover, and growth and development of the city. Four classes; manmade area, land area, green area, and water area were created for each year images to assess change in land cover in central neighborhoods of Ankara. Maximum Likelihood Classifier (MLC) and Random Forest (RF) algorithms were performed and classification results were compared. Overall classification accuracy and overall kappa statistics computed as 85%-92% and between 0.78-0.87 for MLC algorithm, respectively. Comparing with MLC algorithm, RF algorithm’s performance was unsatisfied. As a second step of this study, administrative data of Ankara such as population, land use types, number of buildings and flats, and spatial development relationships were analyzed in integration with remote sensing data results to analyses land development in Ankara.
Terrain models play a key role in many applications, such as hydrological modeling, volume calculation, wire and pipeline route planning as well as many engineering applications. While terrain models can be generated from traditional data sources, an advanced and recently popular geospatial technology, Light Detection and Ranging (LiDAR) data, is also a source for generating high-density terrain models in the last decades. The main advantage of LiDAR technology over traditional data sources is that it generates 3D point clouds directly so that the representation of the surfaces is obtained fast. On the other hand, before terrain modeling, ground points need to be extracted by point labeling in the 3D point cloud. In this study, a new algorithm is proposed for automatic ground point extraction from airborne LiDAR data for urban areas. The proposed algorithm is mainly based on height information of the points in the dataset and labels ground points comparing height differences in local windows. The algorithm does not require any user input threshold and a neighborhood definition. The proposed ground extraction algorithm was tested with three different urban area LiDAR data. The quality control basically performed qualitatively by visual inspection and quantitatively by calculation of overall accuracy, which is conduct by comparing the proposed algorithm results with data provider's ground classification and Cloth Simulation Filtering (CSF) algorithm's results. The overall accuracy of the proposed algorithm is found between 95%-98%. The experimental results showed that the algorithm promises reliable results to extract ground points from airborne LiDAR data for urban areas.
The observation of the juxtaposition of formal and informal urban settlements in the commonwealth and sub-Saharan developing countries is been trending as a common mundane phenomenon in emerging and fast-growing cities. In Dar es Salaam for example, one of the largest, high-density, and populous businesses cities in Tanzania; dichotomy of informal and formal land rights is ubiquitous in peri-urban areas and its urban vicinities where land evolves from village to urban. The dichotomy of urban settlements occurs when the public authorities do not satisfactorily provide public services which are customarily attributed to poor governance and policies formulated to guide market forces, urban management, and growth. Different strategies and approaches have been applied by the government for at least providing proper infrastructure; however, most of the approaches are not well successful and deliver the expected results following high cost of implementation. To understand the spatial dynamics of urban typology, population density and land cover maps of Dar es Salaam were used to comprehend the developmental characteristics of Dar es Salaam urban land transformation and change detection of built-up area. According to the analysis of the maps, rapid urbanization and dramatical growth in built-up area especially between 1990-2000 years were easily observed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.