Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that use of textural data during the object image classification approach can considerably enhance land use classification performance. Moreover, the results showed higher overall accuracy (86.02%) in the o object based method than pixel based (79.06%) in urban extractions. The object based performed much more capabilities than pixel based.
The emergence of oil fields and subsequent changes in adjacent land use are known to affect settlements and communities. Everywhere the industry emerges, there is little understanding about the impact of oil fields on land use in the surrounding areas. The oil industry in Iraq is one of the most important industries and is almost the main industry in the Iraqi economic sector, and it is very clear that this industry is spread over large areas, and at the same time adjoins with population communities linked to it developmentally.
The rapid development and expansion of oil extraction activities in various regions has led to many challenges related to land-use planning and management. Here, the problem of research arises on th
... Show MoreThis paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe
... Show MoreAn oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification
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his project try to explain the using ability of spatial techniques for land cover change detection on regional level with the time parameter and did select for explain these abilities study case (Hewaizah marsh ) . this area apply to many big changes with the time. These changes made action on characters and behaviors of this area as well as all activities in it . This Project concerting to recognize the Using importance of remote sensing and GIS Methodology in data collecting for the changes of land use and the methodology for the analyses and getting the results for the next using as a base data for development and drawing the plans as well as in regional planning .This project focus on practical
... Show MoreIn this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa
... Show MoreThe purpose of this study is to describe the extent and nature of informal tenure practices in urban areas in Iraq, through undertaking a rapid assessment in Baghdad city. The UN-HABITAT 2008 publication Secure Land Rights for All discusses the importance of access and rights to land throughout the developing world. Secure land rights are critical to development and poverty reduction, and the greatest challenge in providing secure land rights are in urban areas, where overcrowding can lead to a number of informal tenure practices ranging from individually unregistered or unauthorised housing, to large informal settlements. Access to land is a fundamental basis for human shelter, food production, and other economic activity. Secur
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