Drones, also known as unmanned aerial vehicles (UAVs), have gained numerous applications due to their low cost, ease of use, vertical takeover and landing, and ability to operate in high-risk or hard-to-reach areas. The contribution of this review is that of building the bridge between drone technology and its application and advancements in the field of Geographic Information System (GIS). The integration of drones and GIS is valuable as it reduces costs and improves accessibility for geospatial data collection. Traditional methods involving aircraft for aerial photography are expensive, requiring the hiring of aircraft, pilots, and photographers. Drones equipped with advanced cameras and artificial intelligence software can replace the conventional technique and at the same time, be economical and time-efficient. The integration of drones and GIS is expected to bring revolutionary benefits in the fields of precision agriculture, urban planning, emergency health response, disaster management, the development of smart cities, food delivery, etc. In this paper, a state-of-the-art review of the deployment of drone-integrated GIS applications in different fields is presented. Numerous techniques and associated challenges related to their development, formulation, implementation, and regulation are highlighted. It has been concluded that drone-integration solutions in GIS improve efficiency and accuracy, enhance the decision-making process, and facilitate better real-time monitoring. The findings of this review paper are intended to help and benefit researchers, business developers, emergency service providers, industrialists, and policymakers.

The United Nations (UN) Sustainable Development Goals (SDGs) serve as a blueprint for securing a sustainable, healthy, and just future for people and the environment. Through the implementation of various policies and initiatives for Vision 2030, the Kingdom of Saudi Arabia has significantly advanced its SDGs. Geographic information systems (GIS) and remote sensing (RS) technologies can play vital roles in tracking and assessing the progress of various government measures. This study investigated the potential of satellite-based RS and GIS technologies for planning, evaluating, and monitoring the status of SDGs. The significance of GIS in Saudi Vision 2030 was examined through a comprehensive literature review and expert interviews. In addition, we reviewed a case study to discuss the role and challenges of utilizing GIS big data for achieving SDGs in Saudi Arabia. Furthermore, we explored the use of large datasets from community scientists and satellite monitoring of SDGs. Overall, we aimed to provide insightful recommendations regarding the utilization of GIS in the effective monitoring of the progress of the SDGs in achieving Saudi Vision 2030. This can aid decision-makers and country leaders in developing assessment frameworks.

Currently, natural hazards are a significant concern as they contribute to increased vulnerability, environmental degradation, and loss of life, among other consequences. Climate change and human activities are key factors that contribute to various natural hazards such as floods, landslides, droughts, and deforestation. Assam state in India experiences annual floods that significantly impact the local environment. In 2022, the flooding affected approximately 1.9 million people and 2930 villages, resulting in the loss of 54 lives. This study utilized the Google Earth Engine (GEE) cloud-computing platform to investigate the extent of flood inundation and deforestation, analyzing pre-flood and post-flood C band Sentinel-1 GRD datasets. Identifying pre- and post-flood areas was conducted using Landsat 8–9 OLI/TIRS datasets and the modified and normalized difference water index (MNDWI). The districts of Cachar, Kokrajhar, Jorhat, Kamrup, and Dhubri were the most affected by floods and deforestation. The 2022 Assam flood encompassed approximately 24,507.27 km² of vegetation loss and 33,902.49 km² of flood inundation out of a total area of 78,438 km². The most affected areas were the riverine regions, the capital city Dispur, Guwahati, southern parts of Assam, and certain eastern regions. Flood hazards exacerbate environmental degradation and deforestation, making satellite-based information crucial for hazard and disaster management solutions. The findings of this research can contribute to raising awareness, planning, and implementing future disaster management strategies to protect both the environment and human life.

Urban heat islands (UHIs) pose a growing challenge in rapidly urbanizing areas, necessitating effective mitigation strategies to enhance environmental sustainability and human well-being. This study examined the role of vegetation in regulating urban microclimates, focusing on its ability to mitigate the effects of UHIs, promote thermal comfort, and enhance urban esthetics. The study drew on existing research that employed spatial analysis and Geographic Information Systems (GIS) to explore the relationship between vegetation metrics and reductions in surface temperature. Municipal initiatives in Khobar, Saudi Arabia, including tree-planting programs and street humanization projects, aimed to improve urban esthetics and pedestrian experiences. Although these efforts enhanced urban livability, they lacked a comprehensive ecological perspective, emphasizing the need for strategies that integrate thermal comfort, environmental resilience, and broader sustainability goals. The analysis demonstrated the societal and environmental benefits of tree-planting activities and linked urban vegetation plans to the achievement of Sustainable Development Goals (SDGs). The results highlighted the importance of incorporating green infrastructure in urban development to mitigate the effects of UHIs, improve air quality, and enhance overall urban livability. This paper proposed a framework for sustainable urban design, offering practical insights for policymakers and urban planners working to create resilient, environmentally conscious communities in extreme climates.

Energy and environmental challenges are a major concern across the world and the urban residential building sector, being one of the main stakeholders in energy consumption and greenhouse gas emissions, needs to be more energy efficient and reduce carbon emissions. While it is easier to design net zero energy homes, existing home stocks are a major challenge for energy retrofitting. Two key challenges are determining the extent of retrofitting required, and developing knowledge-based effective policies that can be applied en-masse to housing stocks and neighborhoods. To overcome these challenges, it is essential to gather critical data about qualities of existing buildings including their age, geo-location, construction type, as well as electro-mechanical and occupancy parameters of each dwelling. The objective of this study was to develop a GIS-based model embedded with critical data of residential buildings to facilitate evidence-based retrofit programs for urban neighborhoods. A model based on a bottom-up approach was proposed in which information gathered from all stakeholders was inputted into one database that can be used for decision-making. A geo-located case study to validate a proposed GIS-based residential retrofitting model sample size of 74 residential buildings in the city of Riyadh was statistically analyzed and used. The results indicate behavior-based patterns, with a strong positive correlation (r = 0.606) between the number of occupants and number of household appliances, while regression analysis showed high occupancy rates do not necessarily result in high utility costs at the end of the month, and there is no statistical difference in the average monthly cost of gas between partial and fully occupied houses. Furthermore, neither the type of building, height, age, nor occupancy status play a significant role in the average energy consumed. Additionally, the GIS-based model was validated and found to be effective for energy-use mapping and gathering critical data for analyzing energy consumption patterns at neighborhood scale, making it useful for municipalities to develop effective policies aimed at energy efficient and smart neighborhoods, based on a recommended list of most effective energy-saving retrofit measures.