Green cities worldwide are converting to renewable clean energy from natural sources such as sunlight and wind due to the lack of traditional resources and the significant increase in environmental pollution. This paper presents an approach of two stages for photovoltaic (PV) potential estimation of solar panels mounted on buildings’ rooftops. The first stage is rooftop detection from satellite images using a series of image pre-processing algorithms, followed by applying machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). The second stage is the solar PV potential estimation using the PVWatts calculator, PVGIS, and ArcGIS. Satellite images for the B6 division of Madinaty City in Egypt were evaluated in this paper. The precision, recall, and F1-score of rooftop detection were 91.2%, 98.6%, and 94.7% from SVM, while those from NB were 86.6%, 98.3%, and 92.2%, respectively. About 290 rooftops were extracted, with a total area of 150,698 m² and a relative root mean square error of 10.6%. The usable area of rooftops was utilized to estimate the annual PV potential of 21.1, 24.9, and 22.9 GWh/year from the PVWatts calculator, PVGIS, and ArcGIS, respectively. According to the estimated PV potential, replacing traditional energy sources reduced the amount of CO₂ by an annual average value of 62%.

Unmanned aerial vehicles (UAVs) can provide valuable spatial information products for many projects across a wide range of applications. One of the major challenges in this discipline is the quality of positioning accuracy of the resulting mapping products in professional photogrammetric projects. This is especially true when using low-cost UAV systems equipped with GNSS receivers for navigation. In this study, the influence of UAV flight direction and camera orientation on positioning accuracy in an urban area on the west bank of the Euphrates river in Iraq was investigated. Positioning accuracy was tested in this study with different flight directions and camera orientation settings using a UAV autopilot app (Pix4Dcapture software (Ver. 4.11.0)). The different combinations of these two main parameters (camera orientation and flight direction) resulted in 11 different flight cases for which individual planimetric and vertical accuracies were evaluated. Eleven flight sets of dense point clouds, DEMs, and ortho-imagery were created in this way to compare the achieved positional accuracies. One set was created using the direct georeferencing method (without using GCPs), while the other ten sets were created using the indirect georeferencing approach based on ground truth measurements of five artificially created GCPs. Positional accuracy was found to vary depending on the user-defined flight plan settings, despite an approximately constant flight altitude. However, it was found that the horizontal accuracy achieved was better than the vertical accuracy for all flight sets. This study revealed that combining multiple sets of images with different flight directions and camera orientations can significantly improve the overall positional accuracy to reach several centimeters.

The automatic reconstruction of existing buildings has gained momentum through the integration of Building Information Modeling (BIM) into architecture, engineering, and construction (AEC) workflows. This study presents a hybrid methodology that combines deep learning with surface-based techniques to automate the generation of 3D models and 2D floor plans from unstructured indoor point clouds. The approach begins with point cloud preprocessing using voxel-based downsampling and robust statistical outlier removal. Room partitions are extracted via DBSCAN applied in the 2D space, followed by structural segmentation using the RandLA-Net deep learning model to classify key building components such as walls, floors, ceilings, columns, doors, and windows. To enhance segmentation fidelity, a density-based filtering technique is employed, and RANSAC is utilized to detect and fit planar primitives representing major surfaces. Wall-surface openings such as doors and windows are identified through local histogram analysis and interpolation in wall-aligned coordinate systems. The method supports complex indoor environments including Manhattan and non-Manhattan layouts, variable ceiling heights, and cluttered scenes with occlusions. The approach was validated using six datasets with varying architectural characteristics, and evaluated using completeness, correctness, and accuracy metrics. Results show a minimum completeness of 86.6%, correctness of 84.8%, and a maximum geometric error of 9.6 cm, demonstrating the robustness and generalizability of the proposed pipeline for automated as-built BIM reconstruction.