The Role of Geospatial Techniques for Renewable Hydrogen Value Chain: A Systematic Review of Current Status, Challenges and Future Developments

Abstract

The clean energy transition has elevated renewable hydrogen as a key energy vector, yet challenges in cost-competitiveness and infrastructure planning persist. This study conducts a PRISMA-based systematic review of recent geospatial applications across the hydrogen value chain—production, storage, transport, and end-use. Bibliometric analysis reveals a strong focus on production (48%), with less attention to storage (12%) and end-uses (18%). Geographic Information Systems (GIS) dominate (80%), primarily for siting, potential assessment, and infrastructure planning, while other techniques such as geophysics and real-time monitoring are emerging. Identified research gaps include fragmented and low-resolution data, lack of harmonization, and high computational demands, which are independent from the phase in the hydrogen value chain. Promising areas for future research include hydrological resource mapping for electrolysis, offshore infrastructure clustering, and spatialized levelized cost modeling. The review concludes with a call for high-resolution, AI-enabled geospatial frameworks to support automated, location-specific decision-making and scalable renewable hydrogen deployment.

1. Introduction

The Clean Energy Transition (CET) is one of the great challenges that society will have to face in the coming years. It must address two key issues, the first being the rapid growth in energy demand. According to data from the International Energy Agency, world energy demand will increase by 4.3% in 2024 and is expected to grow at a rate of 4% until 2027 [1]. Most of the new demand will come from emerging economies [2] and the expansion of new technologies such as Artificial Intelligence (AI), which require large data centers [3]. The second issue is climate change and combating its effects. Analysis of climate data conducted annually by Copernicus indicates a temperature increase in 2024 of 1.6 °C above pre-industrial levels [4], for the first time exceeding the 1.5 °C threshold established in the Paris Agreement [5]. This increase in temperature is associated with the rise in Greenhouse Gas (GHG) emissions, mainly caused by the consumption of fossil fuels [6]. For this reason, the decarbonization strategy of the energy sector is based on electrification [7] and sustainable energy carriers such as renewable hydrogen [8].

In this context, solar and wind power are clean renewable energy sources but are inherently intermittent, necessitating storage systems to ensure a continuous supply [9]. In addition, direct electrification is not viable for all applications [10] due to high power and energy requirements. For these reasons, renewable hydrogen appears as a promising complementary energy vector [11]. Renewable hydrogen is the term used for defining hydrogen produced by electrolysis using water as the raw material and electricity from renewable energy sources as the energy source. Electrolysis is characterized as a completely environmentally friendly process [12]. In addition, the use of renewable hydrogen is also environmentally sustainable, since as a product it only emits water vapor [13].

Water and renewable energy sources are available across the Earth, although with an uneven distribution. Therefore, renewable hydrogen would facilitate a sustainable energy transition based on distributed energy systems [14]. Distributed systems are more efficient than traditional centralized systems due to reduced transportation losses [15] and the distance between production and consumption. The incorporation of renewable hydrogen to distributed energy systems implies an additional advantage, by adding resilience to the energy system. Additionally, renewable hydrogen can meet electricity, mobility, and thermal energy demands, serving as a substitute for natural gas [16]. Local deployment of renewable hydrogen would improve the holistic management of the energy system, as the entire Hydrogen Value Chain (HVC) would be available in the same region [17]. Mochi, P. et al. [18] explore the benefits, challenges, and implementation strategies associated with the development of a local electricity hydrogen market. This research emphasizes the link between sustainable energy vectors, such as electricity and renewable hydrogen, and the need for integrated optimization frameworks that support the development of local energy hubs to accelerate the CET.

Despite the advantages that renewable hydrogen provides, it still must overcome several barriers to become a real competitive energy alternative. These challenges can be classified in technological, economic, environmental, legal, and social. On the one hand, technical barriers stem from hydrogen properties such as lower energy density per volume unit and molecule size [19], which complicate hydrogen storage [20] and transport [21], and contribute to material embrittlement [22]. On the other hand, the low technological development of the components required for renewable hydrogen production and use requires further research to improve the efficiency of the process [23]. Therefore, new techniques are needed to improve manufacturing quality and inspect products [24], as well as to optimize large-scale hydrogen transport and storage configurations [25]. As for economic barriers, the low market share does not allow taking advantage of the economy of scale benefits [26]. It is important to identify applications and locations with characteristics that will increase the Return on Investment (ROI) to guide deployment strategies [27]. From an environmental perspective, hydrogen requires approximately 9 L of water per unit mass of hydrogen, so large plants can have a significant impact in water-scarce regions [28]. At the regulatory level, there is a need for tailored hydrogen regulations that reflect local conditions. Finally, social acceptance is also a key aspect. Technologies to improve safety [29], as well as the training of qualified professionals in the energy sector and in other fields [30], can help to overcome these social barriers.

In the early stages of renewable hydrogen deployment, it is important that decision-making processes consider as much information as possible at the local level. This ensures that solutions are tailored to the specific location, and that installations are cost-effective. In this regard, geospatial techniques are considered to add value to the deployment of renewable hydrogen, since they enable the collection, analysis, interpretation, management, and visualization of location-related information [31]. However, they are also tools and techniques that enable the spatial characteristics of objects or phenomena to be analyzed in an agile, fast, and automated manner [32].

Geospatial techniques are central to the CET due to their cross-cutting nature and ability to integrate multiple perspectives. They enable evaluation of the CET beyond purely economic criteria. Geospatial techniques have proven to be very useful in locating and estimating all types of renewable resources. Monforti, F. et al. [33] have carried out a literature review that compiles studies related to Geographic Information Systems (GIS) on the potential of agricultural residues to meet the European renewable energy targets. Geospatial techniques are used for harmonizing data from the different EU-27 countries. They also enable statistical analyses to estimate the energy potential of agricultural residues, reaching approximately 1500 PJ/yr. Adediji, P.A. et al. [34] perform a compilation of studies related to solar and wind energy. Specifically, they focus on the application of geospatial techniques to predict suitable locations for wind and solar power plants. GIS is used to process multiple layers of information and to integrate Multi-Criteria Decision-Making (MCDM) methods, which help identify and rank the best resource areas. However, geospatial techniques also have applications in other areas of the CET, such as the integration of renewable energies and the decarbonization of urban environments. Li, Y. and Feng, H. [35] analyzed 204 studies applied to net-zero energy buildings. The objective was to identify the applicability of geospatial techniques, the challenges, and future lines of development. The identified challenges are related to data resolution, interoperability, and lack of standardization. Recent systematic reviews such as the one conducted by Isbaex, C. et al. [36] demonstrate the applicability and usefulness of GIS applications in many critical sectors in the CET, such as maritime ports. The results obtained from the bibliometric analysis reveal a growth of 8.59% in scientific publications related to environmental monitoring, Machine Learning (ML), and digitization. Specifically, in the case of the maritime sector, they detected a need for reinforcement in interoperability and integration of data. The objective would be to facilitate the integration of ML with GIS. Avtar, R. et al. [37] demonstrate that the combination of GIS with other geospatial techniques such as remote sensing has great applicability in CET, especially when assessing the energy resource. The results obtained show that Landsat can be effective in deriving potential distribution of general shallow geothermal resources or LiDAR can be useful in aiding wind energy development. Finally, CET offers opportunities for geophysical techniques. Zang, M. and Li, Y. [38] analyzed how the CET not only requires clean energy, but also critical materials such as metals, which require further development of mining. In prospecting for new resources, geophysics plays a key role.

The literature reviews analyzed demonstrate the interest, potential, and transversality of geospatial techniques in CET. Hydrogen has become a key pillar for achieving the decarbonization goals. Deploying hydrogen technologies requires robust spatial analysis to optimize resources and locate facilities efficiently across the entire HVC, from production to storage, transport, distribution, and final consumption. Therefore, integrating geospatial tools into the HVC is essential for informed and strategic decision-making aligned with CET objectives. Nevertheless, as this is a newer technology, comprehensive literature reviews that assess the current state of the art and explore the potential application of geospatial techniques remain limited. Steidl, S. et al. [39] analyze 26 peer-review papers where GIS is used for the integration of hydrogen into the urban environment. The aim is to analyze the temporal and spatial resolution and transparency of the research conducted as they are key aspects of social acceptance. Serna, S. et al. [40] analyzed a total of 28 peer-review papers focused on the application of MCDM for hydrogen production site selection decision-making. The research classifies studies at different spatial resolutions and in different methods. It detects a gap in the transparency of the selection of weights included in the MCDMs, as well as in the provenance of the data and the integration algorithms.

The literature reviews on the application of geospatial techniques to renewable hydrogen focus primarily on GIS techniques for production and include fewer than 30 research papers. In turn, the HVC is not in the same state of development, making it essential to analyze which geospatial techniques are best suited to the current state of the art. Therefore, a literature review covering a broader range of geospatial techniques classified according to the stages of the HVC is needed. This review aims to answer the following research questions: (i) Which geospatial techniques have application in the different stages of the HVC? (ii) What challenges are identified in the existing literature? (iii) What solutions have been proposed to overcome these challenges? (iv) What future research directions are recommended in this field?

After a review of the state-of-the-art reviews, studies that explicitly integrate geospatial techniques in the HVC within a single comprehensive analysis were not found. Therefore, this research offers a holistic analysis of geospatial techniques across each stage of the HVC, identifying associated barriers and challenges. In addition, it proposes recommendations and applications of such techniques with potential application, which are currently not being exploited in the renewable hydrogen field yet. Additionally, the following specific contributions are considered:

• A systematic literature review covering 177 research papers published between 2019 and 2025.
• A bibliometric analysis of geospatial techniques (beyond just GIS) applied across the stages of the renewable HVC, identifying the most relevant methods per HVC stage.
• For each HVC stage, the most commonly used geospatial techniques are outlined, along with their limitations and recommended future research directions.
• A cross-cutting category is included to address applications related to key issues such as safety, leak detection, or training.

The paper is organized as follows. Section 2 describes the database and methods used to ensure the review can be replicated. Section 3 presents the results and Section 4 discusses the main findings, including the current state of the art, key challenges, and future directions. Finally, Section 5 summarizes the main conclusions of the research.

2. Materials and Methods

This section outlines the methodology used to conduct a systematic literature review aimed at comprehensively addressing the research questions formulated in Section 1. This review includes studies that apply geospatial techniques across all stages of the HVC.

Table 1. Description of the main geospatial techniques included in the systematic review.

Geospatial technique	Description	Maturity Level	Ref.
GIS	GIS is a computer system that collects, stores, manages, analyzes, displays, and applies geographic information, as well as a general technique for analyzing and managing mass spatial data.	Mature	[41]
Remote sensing	Remote sensing refers to the acquisition of information on a given target without making contact with the target. It uses the entire electromagnetic spectrum, ranging from short wavelengths (for example, ultraviolet) to long wavelengths (microwaves).	Mature	[42]
Topographic survey	Topographic surveys are generally used for the representation of the Earth’s undulating topography, commonly known as relief features of the Earth’s surface.	Mature	[43]
Photogrammetry	Photogrammetry provides the ability to build three-dimensional (3D) models from two-dimensional (2D) images.	Mature	[44]
Computational vision (CV)	CV is a broad term mainly used to refer to processing image and video data. CV aims to enable machines to perceive, observe, and understand the physical world as if they have human eyes.	Emerging	[45]
Real-time monitoring or Internet of Things (IoT) (Absolute or relative coordinates)	Real-time monitoring and IoT allow real-time data collection, contributing to more proactive planning and early detection of issues before they cause unplanned downtime and revenue loss. Whenever georeferenced data is considered either by absolute coordinates (latitude/longitude by Global Position System (GPS)) or relative coordinates (distance to a reference point).	Emerging	[46]
Geo-visualization	Geo-visualization techniques developed are able to provide information about the different spatial data for users in a visual way and also help to understand better the spatial information using a cognitive approach.	Experimental	[47]
Geophysics	Geophysics analyzes the distribution of physical properties in the subsurface for a wide range of geological, engineering, and environmental applications at different scales. Seismic, electrical, magnetic, and electromagnetic methods are among the most applied and well-established geophysical techniques.	Mature	[48]

provides a summary of the principal geospatial methods identified.

Studies focusing on non-renewable hydrogen and/or do not include any of the geospatial techniques listed in Table 1 were excluded from the systematic review. Table 1 classifies geospatial tools by maturity level into three categories: mature, emerging, and experimental. This classification provides an overview of the current status of each technique to support a clearer understanding of the conclusions of the research conducted. A technique is considered mature if it is proven, demonstrated, and widely available for commercial use. Emerging techniques have been validated in controlled environments or at the prototype level but lack standardized application. Finally, experimental techniques are those with no application beyond the laboratory scale.

Scopus and Web of Science (WoS) have been used as reference databases. The search strategy consisted of an initial global search that included all terms referring to geospatial techniques, as well as to the different technologies within the HVC. The specific queries were as follows:

• Geospatial techniques: TITLE-ABS-KEY. (“geospatial” OR “geographic information system” OR “spatial analysis” OR “spatial data” OR “spatial modeling” OR “geospatial data” OR “terrain modeling” OR “LiDAR” OR “InSAR” OR “satellite image” OR “geophysics” OR “Remote Sensing” OR “Earth Observation” OR “GIS” OR “IoT” OR “topographic survey” OR “classical surveying” OR “land surveying” OR “topographic mapping” OR “geodetic survey” OR “photogrammetry” OR “aerial photogrammetry” OR “computer vision” OR “UAV photogrammetry” OR “drone photogrammetry” OR “Object detection” OR “hyperspectral imaging” OR “thermal imaging” OR “digital elevation model” OR “sensor network” OR “geovisualization” OR “3D GIS” OR “real time monitoring” OR “augmented vision” OR “virtual reality” OR “BIM” OR “ground penetrating radar”)
• HVC: TITLE-ABS-KEY. (“production” OR “storage” OR “fuel cell” OR “end-users” OR “transport” OR “cross-cutting” OR “Supply Chain” OR “Value Chain” OR “Infrastructure” OR “mobility” OR “use”).

To ensure that the results are complete, different synonyms such as “hydrogen” OR “green hydrogen” OR “renewable hydrogen” OR “Hydrogen Economy” have been used.

To ensure that the research is replicable and updatable as the field evolves, this study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology [49]. Figure 1 presents the flowchart outlining the selection and refinement process of the research included in the systematic literature review.

An initial total of 3632 documents (SCOPUS: 1946 and WoS: 1686) were retrieved based on the search terms without applying restrictions. To align with the scope of this review, which focuses on recent developments, the dataset was limited to publications from 2019 to 2025. This refinement reduced the number of relevant documents to 2188 (SCOPUS: 1182 and WoS: 1006). This initial filtering step reveals that over 60% of the research utilizing geospatial techniques in the hydrogen value chain (HVC) has been published within the last six years, underscoring both the novelty and the growing importance of the topic. Papers indexed as “Book Chapter”, “Original review”, ‘Article’ and “Conference Paper” and using English as language were considered. Therefore, 658 documents were excluded from the initial identification (SCOPUS: 265 and WoS: 393). The database was then limited to those areas related to the use of renewable hydrogen as an energy carrier or feedstock. Subject areas such as “Medicine”, “Health Professions”, “Pharmacology, Toxicology and Pharmaceutics” and “Biochemistry, Genetics and Molecular Biology” were excluded. The 769 duplicate documents were eliminated, resulting in an eligibility-prepared database of 596 records. Finally, the abstract and the introduction, methodology, and conclusions sections of the 596 eligible studies were reviewed in detail, and it was determined that 419 studies were outside the scope of the review. A study was considered outside the scope when: (i) it does not mention or use a geospatial technique or perform a spatial analysis; and (ii) it focuses on hydrogen technologies in a specific location but where spatial analysis has no impact on the results. Therefore, 177 studies were reviewed in detail and included in the review. All authors of the review have participated in the refinement process independently. To ensure consistency and reduce bias, a shared resource platform was used. Discrepancies in inclusion or exclusion decisions were analyzed individually and resolved through discussion and consensus. Regular meetings were held to maintain consistent evaluation criteria among all authors. Therefore, the final selection of reviewed articles was the result of the consolidation of individual analyses and the agreement by all authors with them.

3. Results

After the PRISMA refinement process, 177 full papers were analyzed in depth. Research was classified according to the different stages of HVC: production, storage, transportation and distribution, end-uses, and cross-cutting issues. The review detected a few studies dealing with different categories, so it was decided to include some hybrid categories such as production/storage and production/transportation. The results section has been divided into two sections for the purpose of a comprehensive review, both quantitative (Section 3.1) and qualitative (Section 3.2). Section 3.1 shows a bibliometric analysis with the objective of quantitatively analyzing the areas of knowledge, research trends, search keywords, as well as the most relevant bibliographic sources. Section 3.2 performs a qualitative review of the different research under analysis. This review is divided according to the different applications in the HVC. In this way, the most recurrent geospatial applications and techniques in the literature are identified, as well as the challenges and future recommendations.

3.1. Bibliometric Analysis

Bibliometric analysis facilitates the quantitative evaluation of scholarly output, enabling the identification of thematic trends, research gaps, influential publication sources, and interconnections among different areas of study. The bibliometric analysis has been divided into two parts as recommended by Donthu, N. et al. [50] in their guidelines published in 2021. Section 3.1.1 shows the performance analysis, while Section 3.1.2. shows the science mapping.

3.1.1. Performance Analysis

Performance analysis is very useful to evaluate the productivity and impact of the articles included in the review. Figure 2 and

Table 2. Number of publications and citations per year on the application of geospatial techniques to HVC. The data presented in this table correspond to those illustrated in Figure 2.

		2019	2020	2021	2022	2023	2024	2025
	Cross-cutting issues	0	0	1	3	1	9	7
	End-uses	2	7	3	4	2	9	5
	Transportation	1	1	1	0	1	5	1
	Storage	3	0	2	1	7	5	4
	Production/Transportation	0	1	2	0	0	0	1
	Production/Storage	1	1	0	0	0	1	0
	Production	4	2	9	15	10	25	20
	Number of citations	716	728	1900	859	923	1525	698

show the number of publications per year classified by HVC stages and the number of citations per year.

An upward trajectory in annual publication volume is observed between 2019 and 2025, with a marked increase in the growth rate commencing in 2024. This trend is largely driven by increased CET funding initiated in 2020, as a response to the COVID-19 crisis and the EU’s recognition of the strategic vulnerability posed by its reliance on natural gas. In this context, major programs such as NextGenerationEU (2020) and RePowerEU (2022) were launched, positioning renewable hydrogen as a central pillar. NextGenerationEU allocated approximately €250 billion to accelerate investments in renewable energy, clean technologies, and energy efficiency. RePowerEU further established ambitious targets of achieving 10 million tons of domestically produced renewable hydrogen and importing an additional 10 million tons annually by 2030 [51].

The research carried out during the first years of these programs has begun to be published from the year 2024, since the number of publications for the period 2024–2025 represents 52% of the total number of publications for the review period. In terms of the stages of the value chain, the production stage represents 48% of the total number of publications, followed by end-uses (18%) and storage (12%). This distribution across value chain stages provides context for the analysis in Figure 3, which presents the percentage breakdown of publications according to the geospatial techniques outlined in Table 1. Seventy-nine percent of the total number of publications corresponds to the application of GIS.

Therefore, by analyzing Figure 2 and Figure 3 together, it can be concluded that GIS has a significant impact on HVC specifically in the production and end-use stages, as will be discussed in detail in Section 3.2.

Table 3. Annual distribution of included papers (2019–2025), by publisher (in bold) and journal (in regular font), relate to the application of geospatial techniques applied to the renewable HVC.

Publisher/Journal	2019	2020	2021	2022	2023	2024	2025
ACS Publications						1
Environmental Science and Technology						1
Elsevier	10	10	14	18	16	45	30
Advances in Applied Energy				1		1	1
Applied Energy	1	1	1	1	2	1	3
Energy	1		1		1	2
Energy Conversion and Management	1		1	1	1	3	2
Energy For Sustainable Development				1
Energy Reports						2	2
Energy Strategy Reviews			1			1	1
eTransportation						1
Fuel			1	2			1
International Journal of Hydrogen Energy	4	8	5	8	8	28	17
Journal of Cleaner Production			3	1		1
Journal of Energy Storage				1			1
Journal of Power Sources			1				1
Renewable And Sustainable Energy Reviews	1			1	1	2
Renewable Energy	2	1		1		1
Renewable Energy Focus					1	1
Science of the Total Environment					1
Smart Energy							1
Sustainable Energy Technologies and Assessments					1	1
Frontiers					1
Frontiers In Earth Science					1
IEEE			1			1	1
IEEE Access			1				1
IEEE Sensors Journal						1
MDPI	1	1	2	2	2	4	5
Energies	1	1	1	1		2	2
Hydrogen							1
Sensors						1
Sustainability			1	1	2	1	2
PLOS					1
Plos ONE					1
SAGE						1
Energy Exploration and Exploitation						1
Springer							1
Energy Sustainability and Society							1
WILEY		1	1	3	1	2	1
Energy Technology				1	1
Fuel Cells		1
Geochemistry Geophysics Geosystems			1			2	1
International Journal of Energy Research				2
Total Articles Published per Year	11	12	18	23	21	54	38

shows the annual distribution of publications classified by publisher and journal. Elsevier and Multidisciplinary Digital Publishing Institute (MDPI) dominate the publication landscape, contributing 80.8%, and 9.6% of the total reviewed articles, respectively. Although the studies are distributed across 34 journals, nearly half (49.7%) were published in the International Journal of Hydrogen Energy (78 articles) and Applied Energy (10 articles).

3.1.2. Science Mapping

Science mapping examines the relationships between research constituents [50]. VOSviewer 1.6.20 [52] has been used to analyze the generated database and perform keyword co-occurrence analysis. In this analysis, we used a full counting method in VOSviewer, setting a minimum keyword occurrence of 8. A series of 57 keywords met this criterion filtering out 2267 keywords as shown in Figure 4.

Figure 4 presents a keyword co-occurrence network, where each node represents one of the 57 identified keywords. Node size reflects the frequency of each keyword in the database, while links indicate co-occurrence within the same document. The thickness of each link corresponds to the strength of co-occurrence, with thicker links denoting higher frequency. Colors represent thematic clusters, grouping keywords by HVC stage: red is assigned to “production”, green to “storage”, blue to “transport and distribution”; and yellow and purple are assigned to “end uses” and “geospatial techniques”, respectively.

Table 4. Clustering hotspot in the role of geospatial techniques for renewable hydrogen value chain.

Cluster	Cluster Research Hotspot	Keywords in the Cluster
1 (Red color)	Production	hydrogen production; electrolysis; water electrolysis; generation; renewable energy; renewable energies; renewable energy source; solar energy; solar power generation; photovoltaic system; wind power; wind energy; green hydrogen; alternative energy; fossil fuels; energy; wind
2 (Green color)	Storage	energy storage; storage; hydrogen storage; digital storage; underground hydrogen;
3 (Blue color)	Transport and distribution	supply chains; optimization; performance; model
4 (Yellow color)	End-uses	fuel cells; electricity; power; hydrogen energy; hydrogen fuels; hydrogen economy; hydrogen; hydrogen refueling stations; hydrogen energy; hydrogen supply; chains
5 (Purple color)	Geospatial techniques	cost benefit analysis; costs; techno-economics; techno-economic; analysis; energy policy; levelized costs; cost effectiveness; decision-making; sensitivity analysis; information systems; information use; system; internet of things; GIS; spatial analysis; site selection; location; selection; multicriteria decision-making; multicriteria system; geographic information; geographic information system; geo-spatial

shows the five clusters into which keywords have been grouped. The objective of the cluster analysis is to detect those hotspots most relevant in HVC and in geospatial techniques, and the relationships between them. Therefore, the research hotspots are focused on HVC from Cluster 1 to Cluster 4 on HVC stages (production, storage, transport, and end-uses) and Cluster 5 is focused on geospatial techniques. A cluster for cross-cutting issues has not been considered since the co-occurrence of the keywords is less than 8.

3.2. Current Status, Challenges and Future Developments

This section compiles the in-depth qualitative analysis of the role of geospatial techniques in the HVC. The section has been structured following the HVC as follows: Section 3.2.1 presents the geospatial techniques applied to renewable hydrogen production. Section 3.2.2 elaborates on the role of geospatial techniques in the storage stage. Section 3.2.3 is dedicated to the transportation and distribution of renewable hydrogen. Section 3.2.4 discusses the latest advances in renewable hydrogen end uses. Finally, Section 3.2.5 compiles those studies devoted to aspects that are transversal to the whole HVC and that are key to the development of renewable hydrogen such as hydrogen valleys, safety, leak detection or training.

3.2.1. Renewable Hydrogen Production

Hydrogen production is widely recognized as the most technically complex and economically influential stage of the renewable HVC. This stage not only determines the feasibility of large-scale deployment but also significantly influences the competitiveness of renewable hydrogen against fossil-based alternatives. In this context, geospatial analysis emerges as a fundamental tool for guiding strategic decisions, particularly concerning the location, configuration, and optimization of hydrogen production infrastructure.

The systematic literature review reveals that 48% of the studies identified focus specifically on the application of geospatial techniques to the production stage. A large proportion of these studies highlight the use of GIS, frequently in combination with MCDM methods such as the Analytic Hierarchy Process (AHP) or Best-Worst Method (BWM). These integrated approaches have demonstrated significant value in identifying optimal sites for hydrogen production facilities, considering spatial variables such as solar irradiance, wind potential, land slope, land use, water availability, and proximity to grid and transport infrastructure. For instance, Amrani, S. et al. [53] applied GIS-AHP to the region of Morocco to map the most suitable zones for Proton Exchange Membrane (PEM) electrolysis powered by Photovoltaic (PV) and Concentrated Solar Power (CSP), combining different criteria such as elevation models, or solar radiation datasets among others. Based on the suitability ranking maps, two representative sites, one from PV scenario and one from CSP scenario, were chosen for a detailed techno-economic analysis. The results obtained demonstrate Morocco’s competitiveness as they obtain a price per kilogram of hydrogen at these locations of $9.816 (PV scenario) and $5.86 (CSP scenario). However, these approaches are often hindered by the lack of standardized and high-resolution spatial data across countries, as well as the computational complexity involved in large-scale spatial modeling.

Beyond traditional GIS applications, advanced spatial modeling that integrates remote sensing and geospatial data analytics has expanded the potential for precision in evaluating renewable energy inputs. Through satellite imagery, terrain data, and climatic databases, these techniques enable the assessment of solar and wind availability, terrain suitability, and even environmental constraints relevant to hydrogen production. For example, Kakavand, A. et al. [54] applied a GIS-based approach to evaluate solar and wind potential for renewable hydrogen and ammonia production in Iran. It selects four locations in which it compares three different scenarios with different solar and wind energy potential in which it performs a techno-economic analysis in detail.

In more recent developments, the integration of IoT sensor technologies with GIS platforms has opened promising pathways for real-time monitoring and control in hydrogen production systems. These systems can dynamically track key variables such as electrolyzer performance, gas quality, and operational parameters, allowing for automation and predictive maintenance. Yavari, A. et al. [55] used IoT sensors to monitor electrolyzer arrays and integrated spatial data for dynamic system optimization. Maaloum, V. et al. [56] further demonstrated integrated GIS, sensors, and satellite analysis that can be very useful to precise wind resources and thereby improve the techno-economic models of hydrogen plants. Nonetheless, the implementation of such systems is still constrained by the cost and maintenance demands of sensor networks, particularly in remote or offshore locations, and the necessity for robust data-cleaning algorithms to manage inaccuracies and sensor drift.

Complementing these monitoring capabilities, clustering techniques and big data analytics are gaining traction for identifying micro-regions with high hydrogen production potential. For instance, Tahir, M. M. et al. [57] developed a GIS-based geospatial clustering model for Pakistan that combines six critical geospatial factors: land availability, water and road access, water stress, slope, and proximity to protected regions. By applying these exclusion criteria and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms within GIS environments, researchers can detect large-scale facilities equipped with PV and electrolyzer systems. These clustering methods support broader spatial planning and scenario modeling, but their accuracy depends heavily on the availability of harmonized, high-quality, and up-to-date datasets from multiple domains.

An important trend that emerges from the literature concerns the alignment between geospatially optimal regions and the technological specificity of the production method. For instance, PEM and Alkaline Electrolysis (AEL) technologies are frequently assessed in inland or semi-arid zones with high solar potential, such as parts of northern Africa, Australia, Chile, and southwestern United States, where water access is possible through nearby aquifers or desalination infrastructure. In contrast, studies on Solid Oxide Electrolysis Cells (SOEC) are predominantly located in coastal or industrial port areas, such as Saudi Arabia, Northern Europe, and East Asia, where high-temperature waste heat or industrial synergies may improve system efficiency. Likewise, emerging approaches such as photocatalytic hydrogen production tend to focus on hyper-arid zones with extreme solar radiation (e.g., Namibia or the Arabian Peninsula) although this line of research remains largely experimental.

In addition to these technological-geographical linkages, the geographic distribution of the studies reveals certain patterns. A significant proportion of research efforts are concentrated in countries with ambitious hydrogen strategies and strong geospatial data infrastructures, notably Germany, Spain, China, Australia, and the United States. These countries lead not only to technological development but also to methodological advances for spatial modeling and decision support. Conversely, regions with high renewable potential but less-developed geospatial capabilities, such as parts of Latin America or sub-Saharan Africa, are underrepresented in the literature, suggesting both a gap and an opportunity for future international collaboration.

These insights are summarized in

Table 5. Summary geospatial techniques in the production stage of the renewable hydrogen value chain.

Application	Ref.	Main Geospatial Techniques	Primary Uses of the Geospatial Techniques	Challenges
Site selection	[53,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77]	GIS + MCDM	Identification of suitable locations based on renewables, land use, water, grid access	Fragmented datasets; varying resolution; regulatory constraints
Resource mapping	[54,56,78]	GIS + Remote Sensing	Spatial assessment of solar irradiance, wind potential, terrain suitability	Cloud cover in imagery; inconsistent data granularity
IoT monitoring	[55,79,80,81,82]	GIS + IoT Sensors	Real-time performance tracking, system automation, predictive maintenance	Sensor calibration and failure; infrastructure cost in remote areas
Clustering	[55,73,83]	GIS + Clustering Algorithms	Aggregation and mapping of high-potential production zones using big data	Need for harmonized cross-sectoral datasets
Techno-economic modeling	[84,85,86,87,88,89,90,91,92,93]	GIS + Economic Simulation Models	Spatialized cost analysis of hydrogen supply configurations	Lack of integration between spatial and financial models

, which categorizes the main applications of geospatial techniques in renewable hydrogen production, the predominant methods employed, their specific uses, and the primary challenges identified across the literature.

Despite these advances, several gaps remain in the literature. The integration of GIS with techno-economic models capable of simulating levelized costs of hydrogen (LCOH) under spatial constraints is still limited. Nevertheless, De Almeida et al. [94] demonstrated an effort by modeling the techno-economic potential of offshore wind-powered hydrogen production in Brazil using integrated GIS. The spatial optimization of emerging technologies such as photocatalytic hydrogen production or offshore wind-powered is only beginning to be explored. Additionally, the hydrological dimension, especially in arid or water-stressed regions, remains insufficiently addressed, even though water availability is a key limiting factor for electrolysis-based hydrogen production.

Overall, while GIS remains the cornerstone of geospatial analysis in renewable hydrogen production, the convergence with other technologies such as remote sensing, IoT, and machine learning offers substantial opportunities for improving precision, responsiveness, and scalability in planning and operation.

3.2.2. Renewable Hydrogen Storage

Within the HVC, storage represents a key research focus, with 25 of the 177 reviewed papers dedicated to this stage. As a versatile energy vector, renewable hydrogen requires intermediate storage between production and end use to ensure availability during periods without renewable energy input.

Two primary storage methods are identified, depending on capacity and temporal requirements. Short-term storage, typically lasting days or weeks (e.g., 10-day supply), is achieved using pressurized tanks designed to handle frequent charge/discharge cycles [95]. For long-term or inter-seasonal storage, underground geological formations—such as salt domes and depleted gas reservoirs—offer significantly greater capacity, often in the multi-megawatt range [96].

Due to their spatial efficiency and large-scale capacity, underground storage options have attracted greater research interest, accounting for 10 of the 25 papers in the storage cluster. GIS emerges as the predominant geospatial tool in this context, often integrated with advanced methods like Multi-Criteria Decision Making (MCDM).

Interest in subsurface hydrogen storage has also led to studies on natural hydrogen reservoirs. A total of 11 papers focus on this area, including topics such as the formation mechanisms (4 papers), characterization of underground cavities (3 papers), and detection via surface leakages (4 papers). While GIS remains widely used, additional geospatial techniques—such as geophysics for subsurface imaging and IoT for monitoring thermo-physical properties—have also been applied.

Spatial analysis of the literature reveals geographic concentrations: Spain and France lead in studies on natural hydrogen, particularly in the Pyrenees region (3 papers). Poland features prominently due to the presence of salt domes suitable for underground storage (3 papers). Other studies are located in high solar potential regions (including Iran, Algeria, Australia, Portugal, and Italy) highlighting the prevalent view of solar energy surplus as a key driver for hydrogen production.

A summary of the papers found regarding hydrogen storage is shown in

Table 6. Summary geospatial techniques applied to renewable (and natural) hydrogen storage.

Application	Ref.	Main Geospatial Techniques	Primary Uses of the Geospatial Techniques	Challenges
Determination of potential locations	[97,98,99,100,101,102,103,104,105]	GIS	Multi-criteria decision-making, advanced decision methods, artificial intelligence	Validation of AI techniques. Further testing to achieve increased accuracy.
Understanding natural H2	[106,107,108,109]	GIS, IoT and geophysics	Simulations, multi-sensor and historical data for low-concentration H2 detection	Integration of multi-source data for a deeper understanding
Characterization of underground cavities	[110,111,112]	IoT and geophysics	Non-destructive data acquisition to feed physical and AI models	Simulations and machine learning for accurate predictions
Detection of natural H2 through leakages	[96,113,114,115]	CV, IoT and geophysics	Application of machine learning, multi-sensor (IoT, imagery) data for low-concentration H2 detection	Technology refinement and multi-source data integration for the detection of very low-concentration H2 detection
Calculation of storage capacity and needs	[116]	GIS	Integration of storage in the hydrogen value chain and national energy mix	Examination of grid-related factors to improve geographic feasibility analyses
Storage design	[95,117,118]	GIS and IRT	Determination of optimum distance between storage containers, and their charge/discharge cycles	Robust optimization to ensure that solutions are resilient to uncertainties

.

Table 6 provides a structured overview of the main geospatial techniques applied in various aspects of renewable hydrogen storage research and development. GIS, IoT, and advanced computational approaches (e.g., ML, CV) play a central role across different application domains. GIS-based multi-criteria decision-making is critical for identifying optimal storage locations by integrating diverse environmental, technical, and socio-economic factors. As an example, Derakhshani, R. et al. [99] apply a convolutional neural network (CNN) to generate suitability maps for hydrogen storage in salt deposits. Twelve criteria were determined for the MCDM, classified as evaluation and exclusion criteria. Evaluation criteria include maps of roads, gas pipelines, water, and borehole locations, as well as maps like storage capacity, land use, and energy consumption; while exclusion criteria showed protected areas around the rock salt deposits, including national forests, special protection areas, protected areas, conservation areas, and ecological sites. Results showed a good performance of the CNN compared with another advanced method such as AHP. In this line, Gao, J. et al. [100] applied a two-stage decision framework for optimal site selection, based on GIS as the first step, and on the Large-scale Group Decision-making method (LSGDM) as the second step. The weights of the criteria are established based on experts’ opinions with the probabilistic linguistic terms. In this way, optimal sites are determined in China for wind-photovoltaic-hybrid energy storage projects including hydrogen energy storage and electric thermal energy storage.

In parallel, the use of IoT and geophysical methods facilitates the detection and characterization of natural H₂, particularly in low concentrations, while overcoming challenges related to subsurface uncertainty and non-invasive assessment. Aimar, L. et al. [107] have developed a methodology to detect natural hydrogen seepages from remote sensing data, provided that their visual imprint can be easily confused with that of salt lakes. With this aim, a combination of IoT measurements (salinity, soil gas sampling) with geophysics (X-Ray diffraction) has been applied, finding that surface geology has a more recent mark in the case of hydrogen than for salt lakes. Additionally, it has been concluded that the determination of the origin of hydrogen would require the monitoring of surface features for a longer period of weeks to better understand the evolution of the gas mix seeping above a structure. In Lefeuvre, N. et al. [106] the focus was on multi-gas monitoring, with which the authors detected that hydrogen was commonly accompanied by CO₂ and Rn. This led to the understanding that the generation of hydrogen took place due to mantle rocks serpentinization, and that it traveled to the surface along major thrusting faults that are well-imaged with geophysical methods. The detection of hydrogen leakage is also key for the management of the storage and in order to ensure its safety. For this reason, Gao, K. et al. [119] have combined ML with a geophysical technique such as seismic data to detect and characterize hydrogen leakage. Their advance lies in the possibility of detecting hydrogen from sparse data, avoiding the requirement of continuous measurements. On the contrary, Zou X. et al. [113] have developed a methodology based on Raman spectroscopy for the real-time and minute-analysis of hydrogen leakage. Their system is validated in simulation tests, especially focusing on the avoidance of interference due to the detection of other gases such as CO. Yang, B. et al. [114] have also developed a real-time monitoring system based on geophysics, consisting of a network of piezoelectric sensors. Their system focuses on hydrogen vessels instead of underground storage, in such a way that the implementation of laboratory and real-life tests was possible.

In addition to hydrogen detection, the integration of simulations and artificial intelligence further enhances the modeling of underground storage behavior and design, offering predictive capabilities essential for optimizing system configuration and storage performance. An example of this is the work by Samsatly, S. et al. [95], consisting of the development of a spatio-temporal Mix-Integer Linear Programming (MILP) model coupled with GIS modeling that can simultaneously optimize the design, planning and operation of integrated energy value chains, identifying candidate sites for energy production and optimizing scenarios for the production of renewable hydrogen. Another difference in this work with respect to the others found is that the energy source considered is wind, including both onshore and offshore wind turbines. In the same line, the work by Battaglia, V. et al. [116] applies MILP to optimize the design of the energy network in Italy, considering both production and storage, from an economical point of view. The results show that, although hydrogen is a technically optimal option, the inefficiencies in the conversion processes make it less cost-effective compared to other technologies such as pumped-hydro storage.

3.2.3. Renewable Hydrogen Transport and Distribution

Hydrogen transport is an essential component in the hydrogen value chain, especially in contexts where production and consumption are in different areas.

Out of the total of 177 articles, 14 articles have been identified that refer to hydrogen transport, which represents 8% of the total number of articles.

As can be seen in

Table 7. Summary geospatial techniques applied to the transport of hydrogen.

Application	Ref.	Main Geospatial Techniques	Primary Uses of the Geospatial Technique	Challenges
Economic analysis	[61,120,121,122]	GIS	Production and transportation cost analysis using different existing tube	Difficult obstacles to model; Lack of underground data; Lack of climatic data; Limited GIS detail
Safety	[123]	GIS	Hydrogen via risk assessment	Complex modeling; Few documented accidents
Production/Transport	[124,125,126,127,128,129]	GIS	Optimization of the transport and/or storage system for supply	Non-harmonized data between countries; complex multisectoral modeling; Public data scarcity
Adaptation of existing Pipelines	[130,131,132]	GIS	Use of existing pipes to optimize transportation	Access to limited data; Different resolution by regions; Heterogeneity of pipes

, 42.8% of the articles are based on the application of GIS techniques for the detection of the production location and the optimization of the transport route. Some 28.5% base their studies on the techno-economic analysis of the feasibility of hydrogen production and transport. On the other hand, another 21.5% examine the adaptation of existing pipelines for use in hydrogen transport. And finally, 7.3% base their studies on the safety of hydrogen transport.

According to the mention of most of the articles of GIS techniques, there are methodological limitations due to the low spatial or temporal resolution of the available data, the heterogeneous data formats across regions, and the complexity of modeling multi-sectoral infrastructure systems.

The studies reviewed highlight the role of GIS in addressing the logistical, technical, and economic challenges involved in the transportation of hydrogen through various means, such as pipelines, trucks, and maritime shipping. GIS technologies are used to analyze and optimize transport routes, assess infrastructure needs, evaluate terrain and environmental constraints, and support decision-making on profitability and risk management. By integrating spatial data with engineering and economic models, GIS enables a more comprehensive and informed planning of hydrogen supply chains.

Some studies carry out an economic analysis as Ouchani, F. et al. [61]. They integrate indicators such as Levelized Cost of Energy (LCOE) and LCOH to identify optimal production sites and routes for transporting and exporting hydrogen from Morocco to Europe via pipelines and ships including MCDM analysis. A map is generated to show the restricted and unrestricted areas for installing production plants. To generate this map, various GIS tools are needed to integrate all the data.

For the design of pipelines Hammond, J. et al. [120] use a GIS model to integrate physical obstacles and technical restrictions and propose an innovative analysis based on genetic obstacle algorithms, which allows the calculation of optimal routes with variable costs considering hydraulic, geographical, and economic constraints. It is especially useful for scenarios where ground topography influences the feasibility of routes. Hanto, J. et al. [121] use GIS to locate the productions and consumptions regions this integration is not detailed, which limits optimization and investigate the effects of hydrogen mixing on existing gas pipelines reducing the need for new transport investments. However, the technical and regulatory limitations associated with different mixing percentages are also analyzed, and Gunawan, T.A. et al. [122] the study optimizes the system size of hydrogen production prioritizing the wind energy production system, compress the hydrogen before the storage- GIS is used to pair each wind-hydrogen system with its nearest gas pipeline and identify the shortest road route between them. Other studies on transport optimization focus on evaluating on-site hydrogen production, thereby reducing the need for transport, by using GIS models and satellite imagery to measure solar and wind capacity as a source of hydrogen. Li, Y. et al. [125] use geospatial techniques to locate hydrogen stations and assess the availability of excess renewable energy focus on using this excess renewable of energy for the Hydrogen Refuelling Stations (HRS) through electrolysis. Furthermore, Shahzad, S. et al. [126] propose to integrate renewable hydrogen into smart grids highlighting its potential to transform energy systems towards more sustainable and decarbonized models. Although the article does not focus specifically on geospatial tools, integration with existing electricity networks involves spatial analysis. and, Gunawan, T. A. et al. [127] make a techno-economic study to power Irlanda’s buses with hydrogen in order to decarbonize the bus and coach networks. A location-allocation algorithm in a GIS environment optimizes the distributed hydrogen supply chain from each wind farm to a hypothetical HRS in the nearest city, identifying efficient transport routes and optimal locations for HRS. Herwartz, S. et al. [129] focus their efforts on assessing the feasibility of using hydrogen in the fuel cell trains operating between Berlin and Brandenburg. Geospatial techniques are used to detect optimal locations for hydrogen production and its efficient distribution along regional railway routes, and the integration of wind power production with regional transport by fuel cell trains is evaluated. In Brandenburg, around 10.1 million train-kilometers per year could be converted to fuel cell electric train operations. The global study conducted by Linsel, O. et al. [124] models transcontinental supply chains including ship and pipeline transport. Its approach enables integration with the electricity and gas sectors, use GIS to create, transform, analyze and interpret geographic data. The main difficulty found in this study is the lack of standardized data and variability in existing infrastructure.

While other studies look at the costs of retrofitting and transporting existing pipelines Nielsen, S. et al. [128] present a model for the investment evaluation of Power-to-Gas (PtG) plants to generate hydrogen from electricity. GIS is used to evaluate and display data such as renewable resources, existing infrastructure, and energy demand. Zhao, Z. et al. [130] make a techno-economic study evaluating the economic viability of integrating renewable hydrogen into existing pipe infrastructure in Wyoming, identifying optimal locations to mix hydrogen with gas- Meanwhile, Hampp, J. et al. [131] uses geospatial techniques to determine the potential of renewable energy sources in different regions. It faces the added difficulty of using hourly data from different geographical areas for renewable energy production. A comparative analysis of import routes to Germany is performed, evaluating options such as hydrogen, methane, methanol and ammonia, also studying other importation options from seven countries compared to local productions options. Cerniauskas, S. et al. [132] study the cost of reallocating gas pipelines for use in hydrogen transport, GIS was used to assess the availability and suitability of the gas pipeline network in Germany. This approach identified that more than 80% of the gas pipeline network analyzed is technically feasible for repurposing to transport hydrogen.

Another study found focuses on urban or regional contexts and studies a very important factor, such as safety, such as Noguchi, H. et al. [123] assess the risks associated with the road transport of hydrogen in Yokohama, taking into account accidents and road infrastructure and identify specific risks in densely populated urban areas. The effects of road structure, traffic volumes, and population information were reflected in the estimation of the risk for each road segment by using GIS.

3.2.4. Renewable Hydrogen End-Uses

According to the Global Hydrogen Review 2024 [133], the end-uses of renewable hydrogen can be classified into industry and buildings, mobility, and power generation. As shown in the report, the level of development in each category is different. The categories closest to the market with a higher Technology Readiness Level (TRL) are those corresponding to mobility (Hydrogen Refueling Stations (HRS) and Fuel Cell Vehicles (FCVEH)), buildings (Proton Exchange Membrane Fuel Cell (PEMFC) and Solid Oxide Fuel Cell (SOFC) cogeneration) and power generation (High-Temperature Fuel Cell (FC)). One of the barriers detected in the Global Hydrogen Review 2024 is the level of technological development of each component of the FCs, which is why a separate category has been included in which geospatial techniques can be included.

Table 8. Summary geospatial techniques applied to renewable hydrogen end-uses.

Application	Ref.	Main Geospatial Techniques	Primary Uses of the Geospatial Technique	Challenges
Mobility	[134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157]	GIS and IoT	Multi-source database integration Visualization results Real-time data acquisition Environmental analysis	Data reliability; lack of geolocalized data
Industry and Buildings	[158,159,160]	GIS	Multi-source database integration Visualization results Optimization and cluster algorithm	Lack of open database; Computational time
Power generation	[55,161,162]	GIS and DT (IoT)	Monitorization and precise control PEMFC Techno-economic analysis	Low resolution
Component	[163,164,165]	CV and IRT	Detection of membrane manufacturing defects Thermal analysis PEMFC	Integration in the manufacturing process Automatic detection deep learning algorithms

summarizes the main applications and main challenges of geospatial techniques in the different categories into which hydrogen end uses can be classified according to the systematic literature review performed.

The application where geospatial techniques stand out is mobility. It is considered that this is because it is the application with the closest to market [133], since there are different car brands that have FCVEH models in their catalogs [166]. Geospatial techniques stand out in the search for and opening of new markets, through the optimization of HRS site selection. Chrysochoidis-Antsos, N. et al. [142] perform a GIS-MCDM to analyze the suitability of incorporating wind-generated renewable hydrogen into existing conventional refueling stations in the Netherlands. One of the sources used is OpenStreetMap (OSM), which introduces uncertainty in the reliability of the data as OSM data are entered and validated by individual users and are not managed by relevant institutions. GIS techniques can also be employed for the comparison of different HRS implementation scenarios. Löfving, J. et al. [135] use GIS to implement geospatial models including dynamic data for the calculation of energy estimation on long-distance truck routes in Europe. The research analyzes only major roads, as lower ranked roads are not included in the national strategic reports where future scenarios to 2050 are evaluated. In the same line of research, Zulfhazli, A. R. et al. [137] use GIS to visualize the results of their research, which is focused on the analysis of hydrogen demand in three different scenarios for road transport in fourteen G20 countries. The integration of the data into GIS has faced the challenge of low data standardization across countries, making it difficult to find robust and compatible databases among the different states analyzed.

GIS techniques are often complemented with other geospatial techniques to increase the accuracy of the studies. Oksuztepe, E. and Yildirim, M. [139] combine GIS with GPS to analyze the effect on emissions reduction of incorporating supercapacitors into a PEMFC-based propulsion system. The results show a 46.2% improvement in emissions due to the incorporation of data such as slope or traffic. However, with this combination it is not possible to include eventualities such as a crosswalk, so they propose to include IoT sensors to monitor vehicle behavior in real time. The combined use of GPS and GIS enables comparative studies of sustainable mobility solutions based on real operational data. Stein, A. et al. [136] used a combination of GIS and satellite imagery to analyze the relationship between occupied area and HRS capacity. In this case they found that of the 773 HRS they initially identified in the United States (US), they were only able to analyze 134 due to low image quality or the absence of geolocated data. The low image quality prevented them from identifying which infrastructures belonged to the HRS and which did not, especially in those HRSs included in other refueling stations. Caraveo Mena, C. et al. [134] installed a geolocated IoT system in the exhaust system of a vehicle using different blends of biofuels and hydrogen, with the objective of analyzing the impact of emissions. The incorporation of such an IoT system required modifying the exhaust system to include sensors.

GIS is also the predominant technique in stationary applications such as industry and the residential sector. In this case, the general use of GIS is the integration of different data from different sources and morphologies, such as meteorological data, renewable resources, or consumption profiles. Jin, L. et al. [158] propose a GIS-based framework with the ability to compare different energy systems and consumer clusters. The advantage provided by GIS is the automation of the selection of optimal locations using clustering algorithms. The availability of open data and computational time are the main barriers encountered.

In the case of power generation, the combination of GIS and real-time monitoring stands out. Zou, G. et al. [161] applied such a combination of technologies in an extreme environment such as Antarctica. Real-time monitoring allows for increasing the accuracy of the control system of the power plant based on PEM technology. In this case, the GIS allows to integrate climate data and to improve the reference parameters of the control system, as well as to provide a solid basis for extrapolating the results to other regions.

Finally, geospatial techniques based on CV and thermographic imaging have a significant impact during the manufacturing process. The performance and lifetime of components such as FCs depend on highly accurate manufacturing process that minimizes defects. The most critical component is the polymer membrane. Phillips, A. et al. [165] used a thermographic camera to detect hot spots during the operation of an FC. A hot spot can identify a manufacturing defect and can even be classified according to its shape and intensity. In the same line of research, Yan, A. et al. [163] apply CV together with deep learning algorithms to automatically identify and classify defects that may occur during the manufacturing process. In both investigations, the main limitation found is the absence of image datasets with which to train the algorithms. However, the results obtained can be extrapolated to other components used in HVC if sufficient images of classified defects are available.

3.2.5. Renewable Hydrogen Cross-Cutting Issues

Based on a systematic classification of the literature, four main cross-cutting application categories have been identified in the use of geospatial techniques within the hydrogen sector: hydrogen valleys, safety, leak detection, and training.

Table 9. Summary geospatial techniques applied to cross-cutting issues.

Application	Ref.	Main Geospatial Techniques	Primary Uses of the Geospatial Technique	Challenges
Hydrogen Valleys	[167,168,169,170,171,172,173,174,175,176,177]	GIS	Optimize the location of valleys and supply chains, group regions, analyze distribution	Complexity of finding clusters, heterogeneous data, balancing computational load resolution
Safety	[82,178,179]	DT, CV, IRT	Improve security	Heterogeneous data sources and formats, variability in environmental conditions
Leak Detection	[180,181,182,183,184,185]	DT, VR, IoT, LIDAR	Leak detection	Data quality, variability in environmental conditions, reception signal
Training	[30,186,187]	VR, Augmented Reality (AR)	Educational materials	Adaptation to national contexts

summarizes the main applications and main key challenges associated with geospatial techniques within each of these categories, as classified according to the systematic literature review conducted. As shown, the hydrogen valleys category accounts for 47.8% of the total papers reviewed, 13% focus on improving safety, 26.2% are classified under leak detection applications, and the remaining 13% address cross-cutting issues from a training perspective.

Some studies focus on Hydrogen Valleys. The predominant geospatial technique used is GIS, which enhances the accuracy of the location of Hydrogen Valleys, compared to higher-level frameworks, and aids in the design and optimization HVC in the most suitable locations. In this line of research, the study by Mendler, F. et al. [169] stands out, proposing a framework that combines GIS with clustering techniques to identify potential locations of Hydrogen Valleys with greater precision than higher-level discretizations such as administrative regions. They compared five different algorithms, with the best results being achieved by the Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (REDCAP) method [188]. Moustapha Mai, T. et al. [172] develop a framework that combines the functionalities offered by a GIS environment such as QGIS and the functionalities of the GAMS optimization environment for designing and optimizing HVC in island territories, minimizing costs, GHG, and risk index. The results demonstrate the potential of GIS as a tool for managing, collecting, and processing geospatial data from different sources, adjusting the data to the needs of optimization. Using the developed framework, the research performs a sensitivity analysis between different optimization objectives. The strategy of minimizing GHG is the one that reports the highest LCOH in all hydrogen production scenarios evaluated. Dong, H. et al. [177] propose a decision-making model for selecting optimal configurations in hydrogen storage systems linked to wind energy. This model considers the uncertainty inherent in wind power generation, and the different interests and levels of risk aversion of the stakeholder involved.

Research classified in the safety category applies geospatial techniques such as CV, DT, and IRT to improve traditional flame propagation characterization techniques. Hydrogen safety standards establish minimum safety requirements in the event of a hydrogen flame. However, the propagation of a hydrogen flame depends on environmental conditions (e.g., temperature and wind) as well as on the hydrogen itself (e.g., pressure and state of aggregation); therefore, further research is needed to characterize the flame under different operating conditions. Noteworthy in this line of research is the work carried out by [179], who propose a system based on a high-speed VR camera and IRT to characterize the morphology and temperature of the hydrogen flame. The research proposes a test of the venting of a spherical hydrogen tank and compares it with standards such as NFPA 68. The comparison shows that the standard is perfectly applicable in an industrial environment and is aligned with the observations resulting from the tests.

Leak detection is another key cross-cutting issue in the hydrogen sector. Because the hydrogen molecule is very small and operating conditions are demanding, the likelihood of gas leaks at joints is increased. Additionally, due to these characteristics, automating hydrogen detection is particularly challenging. The studies analyzed propose a combination of geospatial tools such as DT, VR, IoT sensors, and LiDAR in different components of the HVC. Kondratyev, S. I. et al. [181] propose adaptations of existing leak detection and monitoring technologies for tanks specifically used in mining environments, using LiDAR detection in geostationary orbit with a laser radiation wavelength of 532 nm. On the other hand, Yang et al., D. [182] propose a system of IoT sensors distributed at different coordinates along a hydrogen pipeline to detect possible leaks based on changes in operating parameters such as pressure or temperature.

The shortage of professionals trained in hydrogen technologies poses a significant barrier for companies undertaking projects in this field. Training in hydrogen technologies must follow a broad strategy, focused on all levels of education. In this sense, geospatial techniques such as VR or AR can be used to create highly visual and robust educational material, suitable for different educational levels. Whitlock, M. et al. [186] use AR with physical elements and virtual animations to provide an interactive experience that allows users to explore and better understand the technical and operational challenges of these physical and virtual systems. Finally, Sendari, S. et al. [187] use AR to learn how to work in the laboratory with a fuel cell-based educational system. The aim is to provide a visual interface that allows different students to perform different experiments in a virtual environment as if they were in the laboratory. It is considered to increase the scope and hours of training and reduce costs, but without compromising educational quality.

4. Discussion

This section provides a broader discussion on the role, implications, and challenges of geospatial techniques in the HVC. The section is divided into two main parts: Section 4.1 focuses on the overall use of geospatial techniques across the HVC and Section 4.2 addresses the main challenges and limitations associated with the application of geospatial techniques, including technical and data-related limitations.

4.1. Use of Geospatial Techniques in HVC

Location plays a key role in all HVC stages, and geospatial techniques, such as GIS, provide a differential value in the optimal selection of the most suitable locations. In this case, GIS offers an incomparable framework with a multitude of tools and functionalities that allow the integration of a large amount of geolocalized information from different sources and in different formats. In addition, emerging energy technologies cannot be evaluated solely from an economic perspective, but must also consider environmental, social, and regulatory aspects. The innovative combination of GIS with other enabling technologies such as MCDMs or AI can help in the process of automating and reducing the subjectivity of decision-making processes. It should be kept in mind that traditional decision-making processes require expert personnel with extensive experience and knowledge in the field to validate and define the multi-criteria problem adequately. Renewable hydrogen, being an emerging technology, does not have a multitude of experts capable of dealing with complex decision-making processes. In addition, implementation of GIS-MCDM, GIS-IA, or GIS-MCDM-IA frameworks, not only allows automating a large part of the decision-making process, but also allows processing more information in less time, increasing the number of criteria that define the problem and improving the quality and accuracy of the final decision.

Despite the dominance of GIS, emerging hybrid workflows are steadily gaining ground. Multi-criteria decision-making, remote sensing, clustering algorithms, techno-economic models, IoT-enabled monitoring, machine-learning spatial analytics and digital twins prove their value in enhancing precision and speeding up planning. Despite the growing potential of AI and hybrid geospatial frameworks, several critical challenges must still be addressed. One major limitation is the lack of high-quality, publicly available datasets needed to train AI algorithms and validate results. In addition, available datasets, whether provided by public institutions or acquired through commercial sources, often lack standardization, which hampers the transferability and applicability of these frameworks across different geographical contexts. Lastly, although not exclusive to geospatial applications, all AI-based developments must undergo rigorous validation to ensure public trust and social acceptance of the outcomes. In this regard, the training of new professionals, discussed in the cross-cutting challenges section, is essential to ensure the robustness and reliability of future geospatial-AI applications.

Geophysical techniques have shown to be highly applicable in the prospecting and analysis of subway hydrogen storage. The characterization of subway caverns with hydrogen storage potential is key to be able to analyze the viability of the facility, considering the capacity of the cavity and its stratigraphic characteristics, which determine the equipment needed to ensure the supply of hydrogen in adequate quantity and quality. Also in the storage stage, the combination of GIS and IoT makes it possible to monitor and determine in real time cavity alterations, such as hydrogen leaks, and to issue alarm signals or act automatically to improve the safety of the facilities. Nevertheless, progress is constrained by fragmented, low-resolution datasets and a lack of standardized protocols for integrating spatial data across sectors, leading to heavy computational loads. A particularly under-studied area is the thermal and materials impact of hydrogen cycling in underground storage boreholes, which highlights the need for improved sensing approaches.

Practical opportunities lie in extending geophysical surveys, such as seismic or electromagnetic methods, to characterize subsurface cavern capacity and stratigraphy, and in leveraging multispectral and infrared satellite imagery to detect pipeline leaks and thermal anomalies, with alerts seamlessly integrated into GIS platforms. Incorporating social-acceptance factors (visibility, land use, population density) into spatial LCOH models can further guide infrastructure sitting with minimal public resistance.

Safety is one of the key barriers to holding back the development of hydrogen technologies. Remote sensing has also been recently applied to detecting leaks in pipelines. One of the characteristics of hydrogen is that the flame is invisible, which makes it difficult to detect visually, especially in large installations or in remote applications such as the hydrogen pipeline network. For this reason, provided that multispectral and infrared satellite imagery can identify thermal variations on the Earth’s surface, they can be applied in this context to detect thermal losses or temperature changes caused by leaks in surface pipelines and identify abnormal heating zones that could indicate uncontrolled evaporation. They can also be used to monitor remote areas to improve maintenance efficiency. On the other hand, hydrogen pipelines are often underground making leak detection by traditional methods (such as anomalous pressure differentials) challenging. In this sense, the use of machine learning-based models trained with GIS layer variables can estimate the probability of failure or leakage in different segments of the network considering variables such as material age, pressure, geological environment and slopes, among others. Maintenance and failure history can also be used to train supervised models that detect risk patterns and help implement predictive maintenance systems when an increased likelihood of structural deterioration is indicated. The integration of Satellite Based Augmentation System (SBAS) with GIS can generate dynamic ground deformation maps in areas where critical infrastructure is located, allowing georeferenced alerts to be generated in areas with shifting and deformed soils. Pressure, temperature, and vibration sensors can be added to create an integrated geospatial monitoring system.

Finally, the end-use stage within HVC is considered to have a significant impact on the social acceptance of the technology. It is the stage closest to the end-user, and it is vital that those first renewable hydrogen applications are successful for the perception and penetration of the technology to improve. In this sense, a GIS-IoT application with a web interface or mobile app can help improve the transparency of a facility’s operation. For example, in an energy community that proposes hydrogen as a seasonal storage system, users’ real-time availability of the energy flows of the facility and the hydrogen system increases the transparency of the energy system and significantly improves the end-user’s perception. Along the same lines, the use of a geolocalized blockchain could improve the traceability of renewable hydrogen, guaranteeing that it was originally produced by renewable energies.

The use of geospatial techniques in the hydrogen field is not only limited to applications with absolute coordinates, but it has also been detected in the review that they can have small scale applications using relative reference systems. This is the case of manufacturing defect detection using geo-visualization or IRT and AI. The quality of the manufacturing process of the components is key to ensure a high service life and efficiency. Advances in AR make it possible to generate didactic material with which to train new professionals in a simulated and safe environment, before undertaking an action in the installation that could become dangerous.

4.2. Challenges and Limitations in the Use of Geospatial Techniques

The study performed has allowed the recognition of main challenges and limitations in the application of geospatial techniques in the HVC.

Table 10. Summary of data limitations and technical challenges.

Limitation/Challenge	Impact on Results	Implication for Future Research	Stages of the HVC
Lack of high-resolution or standardized spatial data	Reduces accuracy in site selection and comparability across regions	Development of global, standardized geospatial datasets	Production; Storage; Transport and Distribution; End-uses
Fragmented and inconsistent datasets	Causes unreliable or incomplete analyses	Improvement of data harmonization and interoperability	Production; Transport and Distribution; Cross-cutting
Cloud cover in satellite images Lack of climatic data	Limits solar resource assessment	Use of alternative sensors or correction methods	Production; Transport and Distribution
High computational complexity Long computation times for complex models	Restricts large-scale or real-time modeling Delays simulations and scenario analysis	Optimization of algorithm, use high-performance computing parallel computing, or cloud solutions	Production; Transport and Distribution; End-uses; Cross-cutting
Limited deployment of sensors in remote areas Sensor calibration and failure	Inhibits real-time monitoring in key locations	Development of low-cost, resilient IoT technologies	Production; Storage; End-uses
Poor integration of spatial and economic models	Incomplete cost analyses of hydrogen systems	Combination of GIS with techno-economic frameworks	Production; Transport and Distribution
Inaccurate or sparse underground/geological data	Affects storage site suitability and safety	Enhancement of geophysical mapping and data collection	Storage; Transport and Distribution
Non-harmonized data across countries	Blocks cross-border infrastructure planning	Promotion of international standards and open data	Transport and Distribution; Cross-cutting
Unreliable or non-geolocated mobility data	Affects planning of hydrogen refueling stations and demand analysis	Standardization of data collection and ensuring spatial tagging	End-uses
Lack of training datasets for AI/image-based defect detection	Limits automation in component manufacturing	Creation of labeled image databases for machine learning	End-uses; Cross-cutting
Complex integration of multi-source geospatial data	Hinders holistic system analysis	Building unified platforms for GIS, IoT, and remote sensing	All
Lack of open-access or updated databases	Limits reproducibility and collaboration	Promotion of public repositories and government-supported open data	All

shows the different challenges and the impact they may have on the specific application of various techniques in the HVC. It also provides a series of future research directions to address these challenges. Additionally, a final column has been included indicating the stage of the HVC to which each challenge is assigned.

Although this section focuses on technical aspects, governance-related factors such as access to geospatial data, regulatory frameworks for data use, and institutional incentives can influence the adoption of these tools across the HVC. In summary, future research should focus on generating more comprehensive and harmonized data, improving spatial modeling algorithms, and integrating artificial intelligence and IoT sensors to achieve more accurate and highly granular geospatial frameworks.

5. Conclusions

Geospatial techniques will play a key role in CET as enablers of emerging technologies. Because of their characteristics and applications, their impact on technologies such as renewable hydrogen can make all the difference. Renewable hydrogen is a key pillar for achieving the decarbonization targets set for 2050. Its versatility allows it to be used in a wide range of applications, such as electric or thermal power generation, seasonal storage, or mobility. It is therefore postulated as a key agent in the decarbonization of society as a substitute for fossil fuels and as a complement to renewable energies. A system based on renewable hydrogen allows for a more efficient use of renewable resources at the local level and more precise adjustment of equipment sizing and improved profitability of the installation.

This systematic review of 177 peer-reviewed studies published between 2019 and 2025 offers an integrated view of how geospatial techniques support every stage of the renewable HVC. By combining quantitative bibliometric analysis with qualitative mapping of methods, it becomes clear that nearly half of all research focuses on hydrogen production, with a further sixth addressing end-uses and just over a tenth on storage, while transport and cross-cutting topics complete the landscape. The research carried out demonstrates the role of geospatial techniques for the development of a hydrogen economy. In this regard, in the short term, it is considered that they should focus on overcoming the main barriers identified in relation to optimal site selection, monitoring and safety, and social acceptance. Although GIS, employed in roughly 80% of the papers for site selection and local resource assessment, has been identified as the predominant geospatial technique, the study has shown that other geospatial techniques such as photogrammetry and CV offer promising applications, including component inspection, infrastructure monitoring, and leak detection, but their use in the hydrogen sector remains limited. This is likely due to a combination of factors: technical difficulties in applying them in outdoor or industrial environments, high equipment and processing costs, and a physical infrastructure (production facilities, pipelines, applications) that is still limited. As the sector matures and the hydrogen industry grows, these techniques are expected to be adopted more widely, especially in process monitoring and automation. Based on the analysis of recent trends in each of the stages of HVC, the following three priority lines of research are proposed, which are undertaken in the next five years:

• GIS, IoT, and satellite information-based computational geo-intelligence framework. The development of tools capable of integrating real-time information on renewable energy resources from different sources of information is recommended. One of the key features of this type of tool is that human–machine interaction must be reduced, integrating predictive and machine learning models to automate the decision-making process at the local level. A framework of this kind would be very useful for administrations responsible for energy planning or for those responsible for the sustainability of critical facilities such as airports or ports.
• Participatory co-creation tools in early hydrogen application projects. The aim is to involve the different stakeholders in the development and evaluation of the functioning of a hydrogen-based energy community or hydrogen valley. It is considered that to increase social acceptance, it is necessary to have traceable (geolocated) tools where aspects for improvement and opinions from real users can be integrated. In this case, a combination of GIS and blockchain could be useful to promote end users’ confidence in this technology, as well as being a useful source of information for professionals in the sector.
• Geospatial DT framework with real-time monitoring of facilities. Having DTs of facilities in operation is key to efficient management, improved safety, and the training of new professionals. In the process of creating DT, technologies such as LiDAR or photogrammetry will be useful for creating the 3D model, while techniques such as IoT or IRT will be useful in the operational phase for managing and monitoring the safety of the facility.

Finally, we acknowledge that this review is limited to English-language literature indexed in Scopus and Web of Science through 2025; rapidly evolving practices and non-indexed studies may offer additional insights beyond this scope.