This study modeled the urban growth in the Greater Cairo Region (GCR), one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC) maps (1984, 2003 and 2014) were produced from satellite images by using Support Vector Machines (SVM). Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change) and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modeled to predict the future scenarios for year 2025 using Land Change Modeler (LCM) embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.

The World Health Organization has reported that the number of worldwide urban residents is expected to reach 70% of the total world population by 2050. In the face of challenges brought about by the demographic transition, there is an urgent need to improve the accuracy of urban land-use mappings to more efficiently inform about urban planning processes. Decision-makers rely on accurate urban mappings to properly assess current plans and to develop new ones. This study investigates the effects of including conventional spectral signatures acquired by different sensors on the classification of airborne LiDAR (Light Detection and Ranging) point clouds using multiple feature spaces. The proposed method applied three machine learning algorithms—ML (Maximum Likelihood), SVM (Support Vector Machines), and MLP (Multilayer Perceptron Neural Network)—to classify LiDAR point clouds of a residential urban area after being geo-registered to aerial photos. The overall classification accuracy passed 97%, with height as the only geometric feature in the classifying space. Misclassifications occurred among different classes due to independent acquisition of aerial and LiDAR data as well as shadow and orthorectification problems from aerial images. Nevertheless, the outcomes are promising as they surpassed those achieved with large geometric feature spaces and are encouraging since the approach is computationally reasonable and integrates radiometric properties from affordable sensors.

Registration of aerial images to enrich 3D LIght Detection and Ranging (LiDAR) points with radiometric information can enhance the capability of object detection, scene classification, and semantic segmentation. However, airborne LiDAR data may not always come with on-board optical images collected during the same flight mission. Indirect geo-referencing can be adopted, if ancillary imagery data is found available. Nevertheless, automatic recognition of control primitives in LiDAR and imagery datasets becomes challenging, especially when they are collected on different dates. This paper proposes a generic registration mechanism based on using the Phase Congruency (PC) model and scene abstraction to overcome the stated challenges. The approach relies on the use of a PC measure to compute the image moments that determine the study scene's edges. Potential candidate points can be identified based on thresholding the image moments' values. A Shape Context Descriptor (SCD) is adopted to automatically pair symmetric candidate points so as to produce a final set of control points. Coordinate transformation parameters between the two datasets were estimated using a Least Squares (LS) adjustment for four registration models: first (affine), second, third order polynomials, and Direct Linear Transform (DLT) models. Datasets covering different urban landscapes were used to examine the proposed workflow. The Root Mean Square Error (RMSE) of the registration is between one to two pixels. The proposed workflow is found to be computationally efficient especially with small-sized datasets, and generic enough to be applied in registering various imagery data and LiDAR point clouds.

The United Nations’ world urbanization prospects of 2019 anticipate the world urban population to increase to 4.9 billion versus the world rural population dropping by $\sim 28\mathrm{million}$ between 2005 and 2030. Meanwhile, more than half the world’s population resides in metropolitan cities, and the proportion is projected to reach two-thirds by 2050.¹

In light of the ongoing urban sprawl reported in recent studies, accurate urban mapping is essential for assessing current status and evolve new policies, to overcome various social, environmental, and economic consequence. Imagery and LiDAR data integration densifies remotely sensed data with radiometric and geometric characteristics, respectively, for a precise segregation of different urban features. This study integrated aerial and LiDAR images using point primitives, which were obtained from running the Phase Congruency model as an image filter to detect edges and corner. The main objective is to study the effect of applying the filter at different spatial resolutions on the registration accuracy and processing time. The detected edge/corner points that are mutual in both datasets, were identified as candidate points. The Shape Context Descriptor method paired-up candidate points as final points based on a minimum correlation of 95%. Affine, second and third order polynomials, in addition to the Direct Linear Transformation models were applied for the image registration process using the two sets of final points. The models were solved using Least Squares adjustments, and validated by a set of 55 checkpoints. It was observed that with the decrease in spatial resolution, on one hand, the registration accuracy did not significantly vary. However, the consistency of the model development and model validation accuracies were enhanced, especially with the third order polynomial model. On the other hand, the number of candidate points decreased; consequently, the processing time significantly declined. The 3D LiDAR points were visualised based on the Red, Green, and Blue radiometric values that were inherited from the aerial photo. The qualitative inspection was very satisfactory, especially when examining the scene’s tiny details. In spite of the interactivity in determining the candidate points, the proposed procedure overcomes the dissimilarity between datasets in terms of acquisition technique and time, and widens the tolerance of accepting points as candidates by including points that are not traditionally considered (i.e. road intersections).