Drone Mapping and Geospatial Data for Solar Project Development in Nigeria

Introduction

Accurate site assessment is critical for the successful deployment of off-grid solar systems in Nigeria. Traditional ground-based surveys are often time-consuming, costly and constrained by terrain and accessibility challenges. Recent open-access research demonstrates that unmanned aerial vehicles (UAVs), commonly referred to as drones, can significantly improve the quality and efficiency of geospatial data collection for renewable energy planning. This article summarises findings from a peer-reviewed MDPI study on UAV adoption in Nigeria and examines its relevance to off-grid solar project development.

Background

Solar project developers require detailed information on terrain, land use, shading, infrastructure proximity and settlement layout. In many rural Nigerian contexts, up-to-date geospatial data is limited or unavailable. UAV-based mapping provides high-resolution imagery and digital elevation models that support more accurate system design and cost estimation. Nigeria has seen increasing use of drones across sectors such as agriculture, environmental monitoring and urban planning. Their application in renewable energy planning is now gaining attention.

Overview of the Referenced Study

Taiwo et al. (2024) published an open-access study in Remote Sensing (MDPI) assessing the suitability of multi-resolution remotely sensed data for UAV adoption in Nigeria. The study evaluated the accuracy, cost efficiency and practical applicability of UAV-derived data compared to satellite imagery. The research demonstrated that UAVs provide superior spatial resolution for site-specific analysis, particularly in semi-urban and rural Nigerian environments.

Key Findings

1. UAVs provide high-resolution spatial data. Drone imagery enables precise identification of shading obstacles, roof structures and terrain features relevant to solar system design. 2. UAV mapping improves project planning accuracy. Developers can optimise system sizing and component placement, reducing oversizing and unnecessary costs. 3. Cost efficiency improves at project scale. While initial equipment costs exist, UAV surveys reduce repeated field visits and long-term survey expenses. 4. UAV data supports infrastructure mapping. Roads, buildings and distribution layouts can be accurately mapped, supporting mini-grid network design. 5. Data integration enhances decision-making. UAV outputs integrate well with GIS platforms used for energy planning and feasibility assessments.

Relevance to Nigeria’s Off-Grid Solar Sector

For off-grid solar developers in Nigeria, drone mapping offers several advantages: Faster feasibility assessments for mini-grid sites Improved design accuracy for solar home system clustering Better planning for distribution networks Reduced logistical challenges in hard-to-reach communities These benefits are particularly relevant in regions with limited cadastral data or difficult terrain.

Implementation Challenges

Despite its potential, UAV adoption faces constraints: Regulatory requirements for drone operation Limited availability of trained UAV pilots Weather-related operational limitations Data processing and storage capacity needs Addressing these challenges requires coordination between regulators, developers and training institutions.

Policy and Practice Implications

The integration of UAV mapping into renewable energy planning can support Nigeria’s rural electrification goals by: Reducing project development risk Improving investment confidence Enhancing data-driven decision-making Supporting transparent and replicable site assessments Incorporating geospatial innovation into energy planning frameworks can accelerate off-grid deployment.

Further Reading

Taiwo, I.O., Adeyemi, G.A., Adebayo, O.S., et al. (2024). Fitness of Multi-Resolution Remotely Sensed Data for UAV Adoption in Nigeria. Remote Sensing, MDPI. (Open Access, CC BY 4.0).

Attribution and Licence

This article summarises findings from a peer-reviewed open-access publication licensed under Creative Commons Attribution (CC BY 4.0). Attribution is provided in accordance with the licence.