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%.

Green energy is increasingly used due to the lack of traditional resources and the increase in environmental pollution, which badly affects our planet in all aspects of life including air, plant life, seas, oceans, etc. In this context, buildings’ rooftops extraction approach for photovoltaic (PV) potential estimation is presented into two main phases. First, rooftops detection from satellite images using image pre-processing techniques and a machine learning algorithm. The pre-processing steps include gamma correction, shadow, vegetation masking, kmeans, and connected components. Support Vector Machine (SVM) algorithm is then applied to extract rooftops. Second, using two GIS-based methods, PVGIS and Solar Analyst Tool in ArcGIS, for PV estimation. Satellite images for a part of Madinaty city in Egypt were used to evaluate our approach. The accuracy assessment of SVM expressed by the precision and recall were 95.7% and 90%, respectively. The identifiable rooftops in the image were 112 rooftops with a total area of 26,131 m². The annual PV potential area was estimated to be 9.3 and 8.7 MWh/year using PVGIS and Solar Analyst Tool, respectively. PVGIS was more accurate as it uses more recent data from solar databases that exist in Africa. On the other hand, Solar Analyst Tool was less accurate as it depends on a digital elevation model with a resolution of 30 m. According to our calculations, the electric energy and the amount of CO₂ emission were compensated by an annual average value of 48% for using solar panels instead of the traditional sources of energy.

Simultaneous Localization and Mapping (SLAM) enables real-time six-degree-of-freedom (6-DOF) state estimation and spatial reconstruction in environments where global navigation satellite systems (GNSSs) are unavailable or unreliable. Light Detection and Ranging (LiDAR) is particularly suited to this task because it provides direct geometric measurements and remains largely unaffected by ambient illumination. This review presents an architecture-centered synthesis of LiDAR-based SLAM, covering LiDAR-only and LiDAR-inertial systems through a dual-axis, architecture-centered taxonomy. The first axis distinguishes sensing configuration, while the second distinguishes the principal registration, representation, coupling, and estimator structures within the corresponding branches. Core algorithmic modules, including front-end registration, state estimation, loop closure, map representation, and back-end optimization, are examined to determine how their interactions affect accuracy, computational latency, robustness, and scalability. Comparisons of representative systems are integrated within the corresponding methodological sections. Quantitative results are compared only when the same dataset subset, metric, and evaluation protocol are shared, while results obtained under non-equivalent conditions are used only as contextual evidence. The analysis identifies recurring trade-offs between low-latency recursive estimation and globally consistent graph-based optimization, as well as between sparse feature-based, direct, and learning-based representations. It also highlights the field’s progression toward continuous-time estimation, semantic scene understanding, adaptive map representations, neural implicit mapping, and uncertainty-aware sensor fusion. Finally, the review organizes future research around trustworthy estimation and recovery, environmental adaptability, persistent map maintenance, resource-bounded operation, and dependable interfaces with navigation and motion planning.

Maintaining accurate, georeferenced dense maps requires reliable global navigation satellite systems (GNSS) observations to constrain global drift. Degraded observations must be prevented from distorting trajectories and local maps as reception transitions from open sky to urban obstruction and complete signal denial indoors. This study develops and evaluates Dense LiDAR–Inertial–GNSS simultaneous localization and mapping (DLIG-SLAM) to adapt GNSS integration and local mapping as reception conditions change. Built on Dense Multi-Scan Adjustment, the framework estimates the local trajectory and dense map separately from the transformation that connects them to a global coordinate frame. GNSS observations are assessed separately for updating this transformation and correcting the estimator. An online switcher selects among integration strategies according to measurement reliability, estimator readiness, and trajectory consistency. Outdoor–indoor state changes reconfigure local mapping, while voxel size adapts to the observed scan point population. Evaluation was performed on four KITTI driving sequences and an urban canyon outdoor–indoor mission in Toronto, recorded with standalone single-point positioning GNSS. GNSS coupling through an inertial navigation estimate achieved the lowest three-dimensional trajectory root mean square error on three of the four KITTI sequences. Online switching did not consistently outperform fixed integration strategies in the KITTI trajectory evaluation. In the Toronto dataset, inertial navigation-mediated coupling achieved the lowest horizontal georeferencing root mean square error of 1.29 m, while full-navigation factor graph smoothing achieved the lowest three-dimensional georeferencing root mean square error (RMSE) of 1.71 m. Online switching achieved the lowest cloud-to-cloud root mean square distance of 6.1 cm against a terrestrial laser scanning reference within the evaluated facade and ground floor region. Controlled experiments also evaluated the effect of separate GNSS admission paths and global transformation policies. The results support adapting GNSS integration to changing reception conditions and mapping objectives, as stronger coupling did not uniformly improve trajectory accuracy, global placement, and local geometric agreement.