It is stated that 3D recording and modeling of heritage buildings entail accurate building models (as-built). However, this paper presents an analysis of the 3D modeling accuracy for the creation of historical building information models (HBIM), considering the complexity and the deformations of historical buildings, using point cloud data and BIM tools. The 3D modeling processes analyzed are based on a three-stage semi-automatic approach leading to the generation of HBIM, including manual and automatic processes. The three stages consist of: (a) optical and terrestrial laser scanning; (b) meshing processes; and finally (c) 3D solid modeling to be assembled into HBIM. Next, this approach analyzed the mesh deformations generated automatically in comparison to the initial point cloud data. The deformations and the accuracy evaluation have been undertaken using different commercial software. Finally, our modeling approach shows that it can improve the accuracy of the 3D models achieved using existing BIM technologies.

Land Use Land Cover Change (LULCC) and urban growth prediction and analysis are two of the best methods that can help decision-makers for better sustainable management and planning of socioeconomic development in the countries. In the present paper, the growth of urban land use was analyzed and predicted in all districts of the El Baha region (Kingdom of Saudi Arabia) based on high-resolution Landsat, 5, 7, and 8 satellite imagery during the period of study between 1985–2021. Using remote sensing techniques, the LULCC were obtained based on the maximum likelihood classification (MLC), where the geographic information system (GIS) had been used for mapping LULCC classes. Furthermore, Markov cellular automata (MCA) in Idrisi TerrSet was applied for assessing the future growth of urban land use between 2021–2047. The findings of the LULCC analysis based on the MLC indicate great socioeconomic development during the study period and that the urban expansion was at the expense of rangeland, forest and shrubland, and barren land and sand areas, with the contribution of each in the built-up area estimated to be around 9.1% (179.7 km²), 33.4% (656.3 km²) and 57.5% (1131.5 km²), respectively. The simulation of the future LULCC period 2021–2047 revealed a loss in rangeland, forest and shrubland, and barren land and sand by 565, 144 and 105 km², respectively, where rangeland is the most influenced, its land cover will decrease from 4002 to 3437 km². From the obtained results based on MCA, urban growth is predicted to be large and it is estimated at around 2607 km² until the year 2047 with a net increase of 811 km². The results obtained from this study may provide information to help decision-makers to implement efficient practices for future planning and management of the growth of urban land use, especially Saudi vision 2030.

Among the Saudi Vision 2030 programs is the Green Saudi Initiative, which aims to protect the environment, energy conversion, and sustainability projects to build a sustainable future. In the present paper, Green Infrastructures (GI) were assessed, analyzed, and mapped using GIS and Analytic Hierarchy Process-based-Multi-Criteria Decision-Making Method (AHP-MCDM). Ten criteria were selected to elaborate the GI suitability map (DEM, slope, topographic position index, rainfall, distance to the water lines, topographic wetness index, distance to the road, wind speed, housing income group high (high-income people) map, employment in the agricultural sector, and land use land change). The results revealed four classes of suitability: Poor, Fair, Good, and Excellent. The “Excellent” area for GI planning was estimated at 983 km² (9%), whereas the “Good” area covered 36% (3987 km²). The excellent and good areas for GI were more localized in the central part of the Al Baha region in the areas of Al Bahah, Elmandaq, Alatawlah, and the central part of Buljurshi. According to the obtained results, the southern part of the study is not suitable for GI planning; this is explained by the large area of barren land and sand. The results obtained by this research may help managers and decision-makers in future planning for GI areas in the Al Baha region.

Land cover assessment plays a vital role in both current and future planning and use of natural resources for sustainable development for any country. For the good practice of the vision 2030 in Al Baha region (south-western Saudi Arabia), land cover was assessed, classified, and analyzed using remote sensing databases and time series analysis combined with spatial analysis in geographic information system (GIS) based on high-resolution Landsat 8 OLI, Sentinel-2 satellite imagery between the period of study 2017/2018 and 2021/2022. Based on both an accuracy assessment and kappa test, the results indicate that Esri Sentinel-2 imagery gives the highest performance compared to Landsat 8 OLI with accuracy and kappa test equal to 87% and 84%, respectively. On the other hand, the land cover classification revealed that the large area of water bodies is localized on Alaqiq (1.45 km²), Baljurish (0.94 km²), and Elmelkhwah (1.57 km²). Furthermore, the built area of the Al Baha region between 2017 and 2021 was estimated to increase by 144 km² (from 516.5 to 661.07 km²), which is especially significant for the Qelwah district (from 16.97 to 44.16 km²) which demonstrated a decrease in bare ground area of approximately 320 km². The crop lands have been increased by 162.74% in the 2017–2021 period from 10.39 km² to 16.90 km², particularly at Qelwah and Elmelkhwah. Finally, the results obtained by this research can help decision-makers and managers for better natural resources management in the Al Baha region.