Natural Hazards and Spatial Data Infrastructures (SDIs) for Disaster Risk Reduction ^†

Abstract

When there is an absence of disaster prevention measures, natural hazards can lead to disasters. An essential part of disaster risk management is the geospatial modeling of devastating hazards, where data sharing is of paramount importance in the context of early-warning systems. This research points out the usefulness of Spatial Data Infrastructures (SDIs) for disaster risk reduction through a literature review, focusing on the necessity of data unification and disposal. Initially, the principles of SDIs are presented, given the fact that this framework contributes significantly to the fulfilment of specific targets and priorities of the Sendai Framework for Disaster Risk Reduction 2015–2030. Thereafter, the challenges of SDIs are investigated in order to underline the main drawbacks stakeholders in emergency management have to come up against, namely the semantic misalignment that impedes efficient data retrieval, malfunctions in the interoperability of datasets and web services, the non-availability of the data in spite of their existence, and a lack of quality data, while also highlighting the obstacles of real case studies on national NSDIs. Thus, diachronic observations on disasters will not be made, despite these comprising a meaningful dataset in disaster mitigation. Consequently, the harmonization of national SDIs with international schemes, such as the Group on Earth Observations (GEO) and European Union’s space program Copernicus, and the usefulness of Artificial Intelligence (AI) and Machine Learning (ML) for disaster mitigation through the prediction of natural hazards are demonstrated. In this paper, for the purpose of disaster preparedness, real-world implementation barriers that preclude SDIs to be completed or deter their functionality are presented, culminating in the proposed future research directions and topics for the SDIs that need further investigation. SDIs constitute an ongoing collaborative effort intending to offer valuable operational tools for decision-making under the threat of a devastating event. Despite the operational potential of SDIs, the complexity of data standardization and coordination remains a core challenge.

1. Introduction

Geospatial data represent physical and social entities of the Earth, and their analysis aims to support society in efficiently addressing complex challenges that require immediate decision-making. Natural hazards pose the main threat to critical infrastructures whose impact can cause severe implications to the functionality of sectors such as power energy, transportation and telecommunication networks, water and food supply, as well as health-care services, thus making it imperative to enhance urban resilience policies so as to diminish any potential disruption to the aforementioned urban services [1]. Undoubtedly, natural hazards can turn into disasters accompanied by tremendous implications when adversely affecting the local population (namely, people considered as injured, homeless, and otherwise affected), increasing fatalities and inducing economic losses [2] that reflect the disaster’s impact on development, as seen in

Table 1. Percentage of events and fatalities that affect the people and economic losses in 10 highly impacted countries by virtue of geophysical hazards (i.e., earthquakes, dry mass movement, or volcanic activity) that occurred in the period 1900–2020. Adapted from [3].

Country	Events (%)	Country	Fatalities (%)	Country	Affected (%)	Country	Economic Loss (%)
China	20.40	China	32.50	China	44.96	Japan	46.37
Indonesia	19.40	Haiti	11.08	India	16.49	China	13.31
Iran	13.03	Indonesia	10.79	Indonesia	6.96	Italy	10.91
Turkey	11.04	Japan	9.39	Chile	5.78	USA	8.88
Japan	8.46	Iran	7.68	Philippines	5.34	Chile	6.54
Philippines	7.16	Russia	7.28	Guatemala	4.73	Turkey	3.47
Peru	5.77	Pakistan	7.18	Pakistan	4.29	Russia	3.42
Mexico	5.17	Italy	5.79	Turkey	4.14	New Zealand	2.91
Italy	4.88	Turkey	4.69	Nepal	3.68	Iran	2.12
USA	4.68	Peru	3.62	Peru	3.65	Taiwan	2.08
Total	1005		2009 (thousand)		173 (million)		1140 (USD billion)

,

Table 2. Percentage of events and fatalities that affect the people and economic losses in 10 highly impacted countries by virtue of hydrological hazards (i.e., floods, landslide, wave action, and riverine flooding) that occurred in the period 1900–2020. Adapted from [3].

Country	Events (%)	Country	Fatalities (%)	Country	Affected (%)	Country	Economic Loss (%)
China	18.14	China	96.02	China	58.22	China	41.93
India	17.05	India	1.17	India	24.83	USA	17.97
Indonesia	14.58	Bangladesh	0.77	Bangladesh	9.53	India	11.44
USA	9.54	Guatemala	0.60	Pakistan	2.23	Italy	6.05
Philippines	8.78	Venezuela	0.44	Thailand	1.69	Thailand	5.77
Brazil	8.50	Pakistan	0.27	Philippines	0.94	Germany	3.90
Colombia	6.46	Japan	0.21	Vietnam	0.92	Japan	3.88
Pakistan	6.17	Russia	0.20	Brazil	0.69	Pakistan	3.16
Afghanistan	5.94	Peru	0.16	Sri Lanka	0.47	North Korea	2.97
Bangladesh	4.84	Indonesia	0.15	Colombia	0.46	UK	2.92
Total	2106		6902 (thousand)		3624 (million)		986 (USD billion)

and

Table 3. Percentage of events and fatalities that affect the people and economic losses in 10 highly impacted countries by virtue of meteorological hazards (only the effects of storms) that occurred in the period 1900–2020. Adapted from [3].

Country	Events (%)	Country	Fatalities (%)	Country	Affected (%)	Country	Economic Loss (%)
USA	28.41	Bangladesh	48.63	China	43.86	USA	65.34
Philippines	15.69	China	13.41	Philippines	16.89	Japan	8.42
China	13.34	India	12.79	India	13.13	China	7.73
India	8.60	Myanmar	11.08	USA	8.92	Puerto Rico	4.08
Japan	7.85	Philippines	3.82	Bangladesh	7.92	India	3.02
Bangladesh	7.64	Japan	2.68	Vietnam	4.90	Germany	2.91
Vietnam	5.25	USA	2.38	Cuba	1.95	Mexico	2.33
Mexico	4.95	Honduras	1.90	Madagascar	0.92	Australia	2.19
Australia	4.66	Hong Kong	1.82	Mexico	0.76	France	2.18
Taiwan	3.61	Vietnam	1.51	Japan	0.76	Philippines	1.80
Total	2383		1306 (thousand)		1138 (million)		1892 (USD billion)

, which present the percentages of the aforesaid variables using data derived from the Emergency Events Database (EM-DAT) for the 10 countries greatly impacted by events originating from geophysical, hydrological, and meteorological hazards in a timeframe of 120 years (1900–2020), according to Chaudhary and Piracha [3]. China is among the first three countries with the highest percentages for every investigated variable for every group of hazards. In general terms, the three groups of hazards mostly impacted the countries belonging to the Asia–Pacific region compared to the total, thus affecting the population and financial costs [3].

On the other hand, there are specific measures targeting disaster prevention and mitigation that are classified into five categories, from planning to engineering, as depicted in

Table 4. Actions needed to be implemented for disaster prevention and mitigation. Adapted from [4].

Physical Planning Measures	Economic Measures	Societal Measures	Management and Institutional Measures	Engineering and Construction measures
Design of services and roads	Diversification of economic activity	Public information campaigns	Education and training	Stronger individual structures
Control of population density	Economic incentives	Education	Research	Hazard control structures
Design of services and roads	Insurance	De-sensationalize hazards	Technical expertise
Land use regulation		Community involvement	Strengthening the capability of local authorities

.

The geospatial data or spatially referenced data of disasters consist of the stable and trigger factors of a natural hazard [5], as well as the data representing the result of a natural disaster and, more specifically, the type of hazard, the date, and the location of occurrence. Basic data sources of natural disasters are government departments, such as ministries, administrative divisions—such as regions and municipalities—international and European agencies, research institutes, universities, and business corporations. Taking into consideration the high distribution of data among the aforementioned stakeholders, it is indispensable that data providers consent to data accessibility and share a framework of regulations and collaborations, which benefit the management of the data for natural disasters in the most efficient way. This framework is entitled “Spatial Data Infrastructures (SDIs)” and has been put into practice in various domains, such as in the case of marine spatial planning [6]. Although the necessity to utilize geospatial information has been pointed out in order to reinforce Disaster Risk Reduction (DRR) strategies and policies, for which SDIs form an essential component across all the phases of DRR [7]—that is prevention, preparedness, response, and recovery, where trust is essential [8]—there is an abundance of data, both geospatial and spatially referenced, which exists but not shared; thus, they are unavailable in practice, compelling the user to look for different data sources. The problem of this study is that when one focuses on disaster mitigation, instead of using an open-to-the-public geodatabase of natural hazards, the research community has to advance through a time-consuming procedure in order to gather various datasets so that it has spatio-temporal continuity. Thus, the research gap lies in the fact that, even though SDIs are widespread, their significance as a framework that supports open data, which remarkably benefit disaster management, is yet to be further explored. One should bear in mind that there has been an enormous increase in the frequency of natural hazards since 1980 [2,9], together with the prediction that an intense and unpreventable increase in wildfires is expected, even under the lowest-emissions scenario [10], where it has to be noted that of crucial importance is their cascading effects, that is the network of antecedent (i.e., drought events and heatwaves) and subsequent (i.e., floods and landslides) hazards that accompany wildfire events [11]. Therefore, this study gives prominence to the crucial role of SDIs, with the support of national and local governmental agencies, to manage and mitigate hazards’ devastating implications, which nowadays comprise one of the basic concerns of humanity. What is missing in the current literature are the following points:

• A comprehensive overview of national SDIs as case examples, which emphasize the challenges that impair their functionality;
• The alignment of national SDIs with international initiatives—such as the Group on Earth Observations (GEO) oriented toward the building of the Global Earth Observation System of Systems (GEOSS) and the European Union’s Space program Copernicus that provides satellite Earth Observations (EOs) and in situ (non-space) data, and, additionally, a vast number of datasets derived from the six thematic streams of Copernicus services;
• The integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in the framework of SDIs aiming to provide timely and valid predictions within the context of Disaster Risk Reductions (DRRs); and
• The implementation of real-world barriers for SDIs, like the funding mechanisms, bureaucratic issues, and legacy systems.

The purpose of this research is to respond to the above-mentioned research topics, while presenting the principles of SDIs, the levels of the SDI hierarchy within the context of natural disasters, and the challenges that must be confronted in order for the venture of SDIs to be successful and appropriate to deal with natural disasters.

2. Methods

2.1. Principles of Spatial Data Infrastructures

According to the INSPIRE Directive [12], “infrastructure for spatial information” means metadata, spatial data-sets, and spatial data services; network services and technologies; agreements on sharing, access, and use; and coordination and monitoring mechanisms, processes, and procedures, established, operated, or made available in accordance with this Directive. According to a plethora of studies focusing on Spatial Data Infrastructures, their framework consists of the following components:

• Geospatial data, more specifically [13]:

  ➢

  Raster datasets, such as a high-resolution digital elevation model (DEM) or updated land cover data;

  ➢

  Vector datasets, such as flood and wildfire events as polygon features containing the extent, time period, and cause of those disasters;

  ➢

  Maps;

  ➢

  Earth observation satellite images for constructing remote sensing indices;

  ➢

  Aerial photographs;

  ➢

  Spatially referenced data, such as wind characteristics, temperature, and precipitation, as well as socio-economic data, like demographics.

• Spatial data services, which refer to network services, such as the standard protocols, were developed within the context of the Open Geospatial Consortium (OGC), i.e., Web Map Service (WMS), Web Feature Service (WFS), Web Coverage Service (WCS), Catalog Service for Web (CSW), etc. [14]. In order to explain the aforementioned terms, the following are utilized: (1) the Web Map Service (WMS) is applied to georeferenced map images (like JPEG or PNG files, etc.) that derive from one or more distributed geospatial databases [15,16]; (2) the Web Feature Service (WFS) aims to provide geospatial data (like the vector datasets) to the web [17,18]; and (3) the Web Coverage Service (WCS) is utilized to retrieve geospatial datasets, like the orthophotos [17]. To be more precise, geospatial data are utilized as “coverages” for spatio-temporal regular and irregular grids [19]. In addition to the aforementioned services, there is a battery of several other services provided by the Open Geospatial Consortium [20,21], like (1) the Web Map Tile Service (WMTS), which completes the existing Web Map Service standard of the OGC and gathers images from a server [22], that is to say, raster tiles [23]; (2) the Web Coverage Processing Service (WCPS), which aims for the retrieval, processing, and investigation of the multi-dimensional coverage of satellite and aerial imageries or statistics data [24] that contribute significantly to the processing of raster data cubes [25]; and (3) the Web Processing Service (WPS), which is oriented toward web-based geoprocessing [26], according to rules that standardize requests and responses [27].
• Metadata, which is a fundamental principle for the operation of SDIs since it provides descriptions for the spatial datasets and spatial data services to be used, and, hence, international standards have been developed so as to achieve this target [28];
• Standards that refer to the technical specifications the geodata have to comply with [29], such as the ISO/TC 211 Geographic information/Geomatics [30] and the Open Geospatial Consortium (OGC) in the geospatial domain [31];
• Data infrastructures of geographic data [32], namely “digital means for storing, sharing, linking together, and consuming data holdings and archives across the internet” [33], thus incorporating spatial databases;
• Technologies, such as blockchain, application programming interfaces (APIs), geospatial data on the web and the semantic web, big data analytics, Machine Learning and Artificial Intelligence (AI), etc. [31];
• Policies that refer to the access and sharing of data [29];
• Human resources that refer to end-users and data providers [34], as well as qualified researchers and specialists [32,34];
• Institutional arrangements that refer to the administrative structure that will determine the roles and the responsibilities of the parties involved with the operation of the SDI, as well as to the legislation that will regulate the way spatial data will be handled and used both by public administration authorities and by citizens [29]. Moreover, the institutional framework incorporates political decisions regarding the national, regional, and global Spatial Data Infrastructures [34].

2.2. SDI Hierarchy Within the Context of Disasters

The management of disasters demands a national strategy, where there is a plan for how to deal with wildfires, floods, earthquakes, etc., which occur locally in various places. It is of vital importance for stakeholders to be aware of as much information as possible for natural disastrous events, such the start and the end date of the occurrence, the place and the extent of the disaster, as well as the reasons for the disaster, for example, a devastating flood was created due to the absence of vegetation, which was destroyed by a detrimental wildfire that occurred one year before. The hazard interrelation between wildfires and floods has been investigated by Gill and Malamud [11], where they determined that wildfires increase the probability of floods. A real case study of this hazard interaction is the 2019 post-fire flood in Kinetain Central Greece [35], where an extreme storm called “Girionis” occurred from 24 November to 26 November 2019, which followed serious wildfire events that happened on 14 May 2017 and on 23 July 2018, which eventually resulted in a devastating flash flood event in 2019. Having inspected the area in the aftermath, the findings revealed that the influence of the aforementioned wildfire in 2018 determined the magnitude of the flood’s damage to a considerable extent [35]. This information can foster the development of early warning systems, which can significantly help a country prevent natural hazards from becoming disasters.

Since the forecasting of disasters requires geospatial data, it is important for the aforementioned information to be accessible to everyone who is involved in disaster mitigation. When one considers that the target of SDIs is to provide spatial datasets, SDIs are divided into six different levels, for which more detailed data can be derived from corporate SDIs, as illustrated in the SDI hierarchy model in Figure 1, namely, different SDIs at the corporate level, which supply the different levels of spatial datasets and, more specifically, local SDIs [36]. Hence, each level of the SDI hierarchy is developed by the incorporation of spatial datasets produced at lower levels according to the administrative division.

2.3. Challenges of Spatial Data Infrastructures

The most important level of the SDI hierarchy is undoubtedly the National Spatial Data Infrastructure (NSDI), which accesses the geospatial data and services of each scientific field for all citizens. If one considers the key variables regarding the evaluation of the NSDI in developing countries as provided by Eelderink, et al. [38], which have been used in case studies [13], the SDIs that are geared toward addressing natural disasters encounter the following severe issues that should be addressed.

2.3.1. Data Availability

It is necessary for the datasets to be characterized by a wide spatial and temporal range for as many hazard types as possible [39], both for their location and their stable and trigger factors [5], thus resulting in comprehensive recordings of all events and not providing limited datasets of a specific date, place, and format.

Should the data for natural hazards exist, either in terms of their outcome (position, extent, and date of the event) or their parameters (i.e., precipitation data for flood simulations), which are mostly distributed among various stakeholders, three major issues have to be addressed in order to be useful, specifically data accessibility, data sharing, and data quality.

◦

Pertaining to the accessibility of the data, it has to be pointed out that, even if the datasets exist, it does not mean that they are accessible, due to the reluctance of public organizations to provide them [13]. Government agencies are hesitant to openly share government data and render them publicly available [40], a practice that is due both to the absence of data sharing policies and protocols on account of the scientific community to openly share research data [41], and to the inefficacy of a metadata governance framework [42].

◦

In relation to data sharing, the lack of relevant legislations on data security causes a lack of collaborations between stakeholders [13] for competitive reasons, without recognizing the benefits of data sharing, such as the decrease in duplications and the integration of data from separate sources [43];

◦

With respect to data quality, datasets have to meet the specifications provided prior to their acquisition in order to be accurate, complete, and up to date, for example, the proper coordinate system or the cloud cover of satellite images within the context of metadata, given the fact that this can be refined by applying cloud-masking methods that aim to create cloud-free satellite mosaics through cloud removal [44].

2.3.2. Interoperability

Interoperability represents, namely, the possibility for spatial datasets to be combined and for services to interact, without repetitive manual intervention, in such a way that the result is coherent and the added value of the datasets and services is enhanced, as specified in the INSPIRE Directive [12]. It must me noted that semantics comprise an integral part of interoperability, given the fact that semantic interoperability is related to the ability of at least two information systems to construe the content and the meaning of the exchanged information essentially and precisely, assisted by syntactic interoperability, which is the exact format of the information [45]. Of utmost importance is the comprehensiveness of the metadata, which must contain as much information as possible, when one bears in mind that their registration follows standardized formats [13] since the efficacy of interoperability is contingent largely upon a metadata catalog [32].

2.3.3. Legal/Institutional Frameworks

The establishment of legislative processes toward achieving NSDI coordination must be performed, through which the leading service that has been designated for NSDI coordination has to put in place the legal framework regarding the sharing and gathering of geodata. It should be pointed out that the leading agency is responsible for managing all the NSDI activities, fostering the collaboration of a diversity of stakeholders, possessing specialists with technical skills who will supervise the progress of the integration of geospatial data [13], and undertaking initiatives to promote NSDIs, like the understanding of NSDI concepts and benefits and the connection of stakeholders to data sharing, workshops, etc. [13,38]. In the case of a limited awareness of NSDI endeavors, the aftereffect will be the lack of dissemination to the authorities, enterprises, scientific community, and institutions regarding the benefits of NSDIs [13] and why their contribution matters. Hence, the involved stakeholders may object to complying with the provisions and requirements. Another critical point to consider is the vision to retain NSDI endeavors concerned with the standardization of geodata in order to address data fragmentation and ensure the interoperability of the available data sources and the implementation of the afore-mentioned challenges, such as data sharing, data quality, etc., coupled with the monitoring and assessment of NSDIs [13].

2.3.4. Technical Capacity

Capacity strengthening refers to the enhancement of the skills of the specialists involved that manage activities pertaining to NSDIs [13]. At this stage, the human capital has to be characterized by interdisciplinarity, which refers to a variety of experts that should be engaged with NSDI development [13]. An additional essential matter is the functionality of geoportals, where the availability of data is characterized by open, registered, and limited access [13], thereby affecting the research objectives, in conjunction with the dual function a geoportal has to perform, since it is divided into two categories, that is the catalog geoportals that are in charge of data accessibility and the application geoportals that offer web services [28].

2.3.5. Financial and Political Constraints

Financial resources finance tasks related to NSDI development, such as gathering, preserving, and managing geospatial data and spatially referenced data, providing capacity development and ongoing education, investing in infrastructure, dissemination activities, etc. [13], for which it is imperative to establish a context that aims to exclusively fund NSDI initiatives. During this phase, political stability is vital in view of the fact that any sociopolitical turbulence will bring about the reallocation of investments [13] and, therefore, the funding of the NSDI initiative will be adversely affected.

3. Results

SDIs are a set of different sections where the ineffectual functionality of at least one of them can undermine the reliability of disaster-related geospatial data services, namely, the provision of complete datasets, for instance, the malfunction of spatial data services, such as WMS, WFS, etc.; deficient metadata, lack of policies for data sharing; and, certainly, the absence of semantics, which can hamper interoperability, which is a key component of the SDI’s functionality for the reason that it impedes the needless duplication of data sources [46] and indubitably plays a fundamental role in the management of large volumes of data [47]. What is more, inaccurate data quality can intensely modify the research objectives of a project involving a pilot of geospatial modeling, given the fact that the imprecision of input data will drastically affect the legitimacy of the geospatial analysis [48] and, indisputably, the assessment of its reliability will be a significant challenge for users [49]. A typical example is the case of groundwater quality estimation, which is simulated using parameters that require comprehensive, reliable, and updated datasets from a series of different sources, and the potential errors that usually accompany the data will critically affect the precision of the final result [50].

On the other hand, the challenges of implementing SDIs on a national scale are numerous, given that it is a collaborative effort toward the management of crucial issues, such as natural disasters [51]. Various case studies of NSDI implementations have demonstrated a diversity of challenges that have hampered this endeavor. With regards to the NSDIs in the Republic of Armenia, the main obstacle is the legal component, where there is a dearth of a legal documents that determines data availability at different levels [52], despite the fact that the framework of the NSDI has to be consistent with legal acts (regulations and laws). On the condition that this compliance exists, data management (access, use, and sharing) can be accomplished under a legitimate and authorized context, and therefore the challenges concerning intellectual property rights can be anticipated [52]. For this reason, Efendyan et al. [52] proposed the elucidation of concepts and definitions in the legislation currently in effect in order to rectify the discrepancies, the delineation of the principles related to the management of Armenian NSDI, as well as the incorporation of implementation guidelines that specify technical approaches for achieving the interoperability of spatial datasets and services.

When it comes to the NSDI of Iraq, Al-Bakri and Fairbairn [53] took into consideration the technical, institutional, social, legal, exchange, and sharing issues pertaining to SDIs, where the highly significant areas to be considered are the quality of data, inter-departmental cooperation, and efficient data accessibility. It is worth mentioning that data-related issues are deeply influenced by exchange and sharing issues, that is, as mentioned by Al-Bakri and Fairbairn [53], the absence of trust, the search for necessary data, data privacy protection, network communication infrastructure, a single source of verified data, improved decision-making, as well as the reduced duplication of resources.

In the case of NSDIs in Brazil (named in Portuguese as “Infraestrutura Nacional de Dados Espaciais” (INDE)), their effectiveness was evaluated through their capability to support the objectives of the 14 global fundamental geospatial data themes [54], which are authorized by the United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) in order to enhance the national geospatial information infrastructures through the Integrated Geospatial Information Framework (UN-IGIF). Among the main challenges of the Brazilian NSDI is the integration of precise local-level data. In the context of the Brazilian NSDI’s response to meet the 14 geodata themes of the UN-GGIM, which are presented in

Table 5. Examples of keywords used to gather the geospatial data layers of the Brazilian NSDI to match the 14 themes of the UN-GGIM. Adapted from [54].

Theme	Typical Keywords Employed
Global Geodetic Reference Frame (GCRF)	GNSS, reference network, gravimetric network, control points
Addresses	Address, house number, urban address, rural address
Buildings and Settlements	Building, locality, village, city, settlement
Elevation and Depth	Bathymetry, contour lines, altimetry, terrain, numerical model
Functional Areas	Zones, territory, agriculture, boundaries, municipality
Geographical Names	Geographic name, toponymy, local name
Geology and Soils	Soils, rock, geological map, lithology, mineral resources
Land Cover and Land Use	Vegetation, forest, deforestation, land use and occupation, biome
Land Parcels	Properties, parcels, districts, quarters, indigenous land
Orthoimagery	Orthophoto, orthomosaic, Sentinel, Landsat
Physical Infrastructure	Airport, schools, power plant, industry, utilities
Population Distribution	Population, population distribution, population density, migration, residential units
Transport Networks	Roads, routes, railway, waterways, subway, aerodromes
Water	Rivers, water balance, water quality, reservoir, pH

, even though there are open data that correspond to the total of the data themes, the datasets are not characterized in detail since their mapping is restricted to small cartographic scales [54] that deters a comprehensive spatial analysis. Another issue that matters for the functionality of the Brazilian NSDI is the incongruity of metadata, which, as a component of an SDI, is of paramount importance owing to the fact that the users are unable to harness the shared geospatial datasets. With the objective of upgrading the NSDI as a tool to achieve the 17 Sustainable Development Goals (SDGs) due to the significance of geospatial data to achieve them, it is obligatory for the data producers to provide high-quality, standardized, and sufficient metadata through which data discoverability, reliability, and interoperability will be indisputably strengthened [54]. Furthermore, another challenge to be mentioned is the usability impairment of the system’s interface, for which Nunes and Camboim [54] suggest simplified interfaces characterized by a user-center design so that data retrieval and processing are fruitful. During this phase, the complex interface of the Brazilian NSDI complicates the user’s capability to find relevant datasets due to a plethora of differential and superfluous themes and sub-themes included in the categorization scheme of the Brazilian NSDI that are opposed to the 14 themes of the UN-GGIM, which should be used as the main categorization scheme according to Nunes and Camboim [54]. Last but not least are the issues related to interoperability and the urgent need to use new data sources, such as from local governments, academic institutions, and Volunteered Geographic Information (VGI) sources, so the Brazilian NSDI is reinforced with more thorough and revised geospatial data to augment the NSDI’s capacity to meet the SDGs. In the group of prompt and prolonged actions needed to face the challenges of the Brazilian NSDI, as mentioned before, Nunes and Camboim [54] emphasize the utilization of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to amplify the functionality of the NSDI, for instance, the automation of metadata creation or the strengthening of real-time data processing, as well as the implementation of a collaborative governance framework that assists the assembling of datasets originating from varying data providers, such as private, community, and non-governmental stakeholders, given that the continuous maintenance of the datasets is guaranteed and the interaction with the interested parties is boosted.

Pertaining to the NSDI of Pakistan, Ali et al. [55] identified, through a literature review, the parameters that hinder the implementation of SDIs, which were interconnected in the aftermath with the strategic pathways provided by the United Nations’ Integrated Geospatial Information Framework (UN-IGIF), as shown in

Table 6. Presentation of 13 barriers that hinder the implementation of the SDI framework, linked with the strategic pathways of the UN-IGIS. Adapted from [55].

Barriers for the Implementation of SDI Framework	IGIS Strategic Pathways
Inter-organizational coordination and communication (IC)	Pathway 1: Governance and Institutions
National data policy (NP)	Pathway 2: Policy and Legal
Specified roles of stakeholders (SRSs)	Pathway 9: Communication and Engagement
Data sharing policy and legal framework (DSP)	Pathway 2: Policy and Legal
Incentives for data sharing (IS)	Pathway 2: Policy and Legal
Organizational partnerships (OPs)	Pathway 7: Partnerships
Information sharing culture (ISC)	Pathway 8: Capacity and Education
Access network (AN)	Pathway 4: Data; Pathway 5: Innovation; Pathway 6: Standards
Data security and privacy (DS)	Pathway 2: Policy and Legal Pathway 4: Data
Financial commitments and constraints (BCs)	Pathway 3: Financial
Data management (DM)	Pathway 6: Standards
Data costs (DCs)	Pathways 3: Financial; Pathway 4: Data; Pathway 6: Standards
Capacity development (OC)	Pathway 8: Capacity and Education

. UN-IGIF is an initiative adopted by the UN-GGIM, which is liable for the reinforcement of national geospatial information management arrangements, like in the case of SDIs, through the provision of guidelines internally and between the Member States, having as an ultimate goal the fulfillment of the 17 Sustainable Development Goals.

The basic barriers impeding the NSDI framework were highlighted after having applied partial least squares structural equation modeling (PLS-SEM) to 520 questionnaire surveys delivered to 280 local stakeholders from the public and private sectors that had expertise in spatial data production. Through the utilization of PLS-SEM, the most profound obstacles to hamper the NSDI of Pakistan were highlighted, beginning with the shortage of a national data policy, implying a policy recommended or put into practice by the government geared toward the public stakeholders involved in geospatial information management (i.e., data availability, accessibility, exchange, and application). In addition to the aforesaid obstacle is the lack of specified roles by stakeholders who can be either data producers, such as surveying engineers through their offices; mapping departments in companies or research centers and space organizations or value-adding agencies, like the municipalities collaborating with the academic community; business users, like real estate; or political and decision-makers, such as relative ministries and departments [55]. Absent data sharing policies and legal frameworks is another important aspect that hinders the NSDI of Pakistan, whose implications concern the reluctance of organizations to share data due vague legal matters (especially the public sector being overprotective as an authority to distribute public data), which is associated with legal and non-legal bindings to gather, preserve, and distribute geospatial data. Legal bindings concern enacted laws and the legal framework in general, as well as contractual commitments, to which the interested parties are bound so as to succumb to data sharing. Non-legal bindings denote a mutually agreed common framework of standards (for instance, specifications for metadata, web mapping, etc.) that the associated agencies or corporations have to abide by through signed agreements with the purpose of effective interoperability to data sharing between them or across other institutions. Furthermore, it is worth pointing out that a critical obstacle for the NSDI of Pakistan is the insufficient inter-organizational coordination that addresses the collaboration of distinct SDI stakeholders, highlighting the outstanding importance of such a network and the ongoing communication during the phases of an SDI’s establishment compared to an individual endeavor by reason of the reciprocity when exchanging products and materials, processes, and technical knowledge. The final point, yet crucial, is the weakened organizational partnerships among stakeholders from the local, regional, to global levels in the context of data sharing. Local partnerships refer to collaborations of unaffiliated participants, for instance, companies from the private sector, to distribute their data using web-based GIS. Regional partnerships concern country-level collaborations toward achieving the enhancement of geospatial information management on behalf of mutual benefits and global partnerships, implying issues like cooperation geared toward the establishment and implementation of standards for geospatial data or partnerships toward international initiatives, such as the 2030 Agenda for Sustainable Development [55,56]. Ali et al. [55] highlighted the need for collaborations between the government and other organizations to be carried out so as to include technical compatibility in data management, together with collaborations among the public and private sectors aiming to decrease transaction costs.

At this point, it has to be mentioned that the Sendai Framework for Disaster Risk Reduction 2015–2030 has defined four priorities and seven targets in order to counteract the aggravation of natural hazard events due to the effects of climate change (i.e., the increased frequency of megafires in wildland–urban interface (WUI) areas). More specifically, priority 1, entitled “Understanding disaster risk”, reflects the necessity of data availability [56], given the fact that disaster risk management requires an integrated approach in the context of the geospatial modeling of natural hazard characteristics, vulnerability, capacity, and the exposure of population and infrastructures. Moreover, it is of crucial importance to note that the seventh target of the Sendai Framework highlights the necessity for the availability of and access to multi-hazard early warning systems and disaster risk information and assessments. A multi-hazard early warning system (MHEWS) consists of four pillars [57]:

• Disaster risk knowledge and management;
• Detection, Observations, Monitoring, Analysis, and Forecasting;
• Warning dissemination and communication;
• Preparedness to respond.

The first two pillars underscore the significance of the availability and semantic alignment of geospatial datasets, as well as the interoperability of web-GIS services in light of the fact that the first one emphasizes the urgency for hazards and vulnerabilities to be understood by inhabitants, and accessibility to risk maps and data, while the latter underlines the accuracy of warnings, which is based on interoperability.

The model of SDI hierarchy is important in the context of natural disasters, given the fact that the availability of spatial data and services and the vertical interaction [36] of different SDIs will stipulate the effectiveness of national SDIs to deal with disasters without any losses.

4. Discussion

SDIs aim to provide a framework where the spatial data of a specific domain can be leveraged efficaciously through the interoperability of all SDIs pertaining to the topic under investigation. It is worth mentioning that SDI can be applied across disciplines requiring spatial analysis when it has to be analyzed from a spatial approach. The elements that comprise the Disaster Risk Management (DRM) framework [58] require reliable, up-to-date, and readily available data [51] in order for the disaster response time to be optimal. Below we present three fundamental topics that stipulate the efficiency of SDIs.

4.1. Alignment of National SDIs with the GEO and Copernicus Programs

SDIs matter because of their versatility. The rationale that has enabled the venture into SDIs is the interconnection of a considerable amount of datasets from distant locations using the internet, aiming to improve the exploitation of geospatial data effortlessly. It merits attention that NSDIs have the potential to align with global initiatives, such as the Group on Earth Observations (GEO) and the Copernicus space program, by means of offering a framework for data gathering, management, and distribution, as well as through the provision of standardized geospatial data. More explicitly, GEO is an intergovernmental body that brings together a plethora of stakeholders, from national governments and the academic community to private companies, aiming to formulate comprehensive solutions reflected in the vision and mission of GEO through the Earth Intelligence for All, by utilizing state-of-the art methods, like Artificial Intelligence and data analytics, to harness a vast amount of data so as to provide pioneering solutions for data-driven and knowledge-based decision-making in various domains, that is, as mentioned on the website of GEO [59].

• Global Ecosystem Atlas, like the evaluation of biodiversity and carbon storage investigations;
• Global Heat Resilience Service, such as the detection of heat risks and the provision of insights toward heat resilience;
• Agriculture and Food Security, pertains to sustainable farming and food system resilience;
• Water and Land Sustainability, aiming at the monitoring of terrestrial and aquatic resources;
• Ecosystems, Biodiversity, and Carbon Management, specializes in the monitoring of habitat changes and the evaluation of biodiversity health;
• Weather, Hazard, and Disaster Resilience, embracing all phases of the disaster cycle (prevention, preparedness, response, and recovery) through the fostering of early warning systems, comprehension of disaster risks, and post-disaster analyses;
• Climate, Energy and Urbanization, targets the monitoring of climate trends and enhancing urban resilience in general;
• One Health, refers to human, animal, and environmental health, and the domino/triggering effect of epidemics, environmental pollution, and ecosystem degradation on public health on a global scale;
• Community Impact, considers the most vulnerable and marginalized communities that need the benefits of geo-information systems;
• Open Data, Open Knowledge, and Infrastructure, for instance, the principles of data sharing or the GEO Knowledge Hub;
• Policy Coordination, provides as an example the endeavor of the Disaster Risk Reduction and Adaptation Working Group (DRRA-WG) to stimulate participants to adopt Earth Intelligence in order to challenge climate change effects.

GEO is involved in the United Nations 2030 Agenda for Sustainable Development, the Paris Agreement, and the Sendai Framework for Disaster Risk Reduction, while it has developed the Global Earth Observation System of Systems (GEOSS), a global network of data and providers that facilitates the collection and sharing of EO data collected from a wide range of sources to all end-users, where its functionality as a system relies on the implementation of international standards of interoperability [60]. The SDIs are consistent with solutions offered by GEO, given that its purpose as a framework is to provide a seamless interaction between the data and services it uses and the end-users that take advantage of them, in other words, unencumbered interoperability by the contribution of the NSDIs to the GEOSS aiming for a more effective employment of local, standardized, geospatial data. It is imperative to highlight that the scope of NSDIs relates vividly to the corresponding scope of GEO for the reason that the data of NSDIs cover a wide spectrum of data so as to benefit a country in as many aspects as possible, which corresponds to the GEO solutions. Another critical point to point out is that the accuracy of geospatial data, the core of NSDIs, plays a fundamental role in the achievement of SDGs [54], which GEO intends to perform through its solutions. Therefore, the demand for geo-information (targeting SDGs, GEO solutions) entails the provision of a large volume of earth-related data (from the NSDIs) according to Georgiadou and Reckien [61].

The Copernicus space program is composed of Sentinel satellites offering valuable EO data and in situ systems, like ground stations using sensors on the ground, at sea or in the air. An additional factor to take into account is the available geospatial data and spatially referenced data being distributed in six thematic streams of Copernicus services, as mentioned on the website of Copernicus [62]:

• Atmosphere, the Copernicus Atmosphere Monitoring Service (CAMS) providing datasets through the Atmosphere Data Store monitoring air quality and atmospheric composition, ozone layer and ultra-violet radiation, emissions and surface fluxes, solar radiation, and climate forcing.
• Marine, the Copernicus Marine Environment Monitoring Service, offering data through the Copernicus Marine Data Store related to the state of the ocean (i.e., salinity, plankton, sea surface height, sea ice, temperature, organic carbon, etc.) at both international and regional levels.
• Land, to be specific, the Copernicus Land Monitoring Service (CLMS) delivering datasets concerning:
  • Bio-Geophysical Parameters, such as soil moisture (i.e., surface soil moisture, soil water index), snow (i.e., snow cover extent, snow state classification), temperature and reflectance (i.e., land surface temperature, land surface water temperature), vegetation (i.e., burnt area and vegetation indices, like the Normalized Vegetation Index (NDVI), the Plant Phenology Index, etc.), and water bodies (i.e., river and lake ice extent, lake water quality, etc.);
  • Land Cover and Land Use Mapping (i.e., dominant leaf type, forest type, tree cover density, grassland, etc.);
  • Priority Area Monitoring (i.e., urban atlas, coastal zones, riparian zones, etc.);
  • Ground Motion Data through the European Ground Motion Service;
  • Land Satellite Mosaics (i.e., Sentinel-2 Global Image Mosaic);
  • Reference and Validation Data (i.e., Ground-based Observations for Validation).
• Climate Change, in particular the Copernicus Climate Change Service (C3S), provides data on the past, present, and future climate status on the European and global scale through the Climate Data Store. The product types are associated with climate indices (i.e., bioclimatic indicators, precipitation risk indicators, climate extreme indices, and heat stress indicators); climate projections (i.e., climate and energy indicators or ocean surface wave indicators); derived analysis (i.e., near surface meteorological variables); in situ observations, such as observations of meteorological and soil variables, in situ temperature, relative humidity, and wind profiles; reanalysis, like ERA5-Land hourly data, ERA5 hourly data on pressure levels, etc.; satellite observations, such as soil moisture gridded data, sea ice edge and type daily gridded data, etc.; as well as seasonal forecasts, for instance, multi-model seasonal forecasts of river discharge, seasonal forecast monthly statistics on pressure levels, etc. C3S is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission.
• Security responds to security challenges by optimizing crisis prevention, preparedness, and responses in four basic fields:
  • Border surveillance;
  • Maritime surveillance;
  • Support to EU External and Security Actions (SESA);
  • Research for Earth Observation Security applications;
• Emergency, through the Copernicus Emergency Management Service (Copernicus EMS) specializing in the sectors of:
  • On-demand mapping, utilizing satellite imagery and other geospatial data with the aim to respond to incidents created by natural hazards, human-made emergency situations, and humanitarian crises through the provision of free-of-charge mapping globally;
  • Wildfires, through the European Forest Fire Information System (EFFIS);
  • Floods, through the European Flood Awareness System (EFAS) and the Global Flood Awareness System (GloFAS);
  • Droughts, through the European Drought Observatory (EDO) and the Global Drought Observatory (GDO);
  • Exposure mapping, through the Global Human Settlement Layer (GHSL).

It is crucial to elucidate the previously mentioned six areas of focus of Copernicus because at least one sector could be the national or regional project of a country, requiring detailed data covering the areas of interest. Taking into consideration the vast variety of critical and useful data provided by the Copernicus program for an abundance of sectors, national SDIs align with the aforesaid thematic Copernicus services in order to take advantage of them through their integration in national projects. Among the components of national SDIs is the legislation regarding data availability and data sharing, and hence, apart from efficient interoperability, it is of paramount importance for a country to establish a proper legal framework through which the external data, derived from international organizations, can be embedded into national policies and practices and be utilized by abiding to the regulations. In the case of emergency cases, for instance, severe disasters resulting from natural hazards, take as an example a great wildfire. The mapping service of the ministries addressing environmental issues or civil protection can be benefited substantially to integrate past wildfire events from Copernicus EFFIS and land cover changes from the Copernicus Land Monitoring Service when making a map of the impacted area using data from the national SDI. Another reason why the alignment of national SDI with the Copernicus services matters is the case of a national early warning systems, where the high-resolution data of Sentinel satellites could be utilized to geovisualize past major events of natural hazards and thereafter to be incorporated into the national SDIs. Another significant aspect to note about the importance of Copernicus data is that they are utilized in the daily schedule of national mapping agencies, such as in the case of land monitoring by the Federal Agency for Cartography and Geodesy (Bundesamt für Kartographie und Geodäsie, BKG) of Germany [63], implying the alignment of NSDIs with Copernicus. In general, the adherence to interoperability standards is crucially important to incorporate Copernicus data into national SDIs.

4.2. Artificial Intelligence and Machine Learning Integration in SDIs for DRR

Artificial Intelligence (AI) and Machine Learning (ML) comprise the state-of-the-art technology to be integrated in the framework of Spatial Data Infrastructures (SDIs) by extracting knowledge from big geospatial data, giving rise to the advantageous scientific field of geospatial artificial intelligence (GeoAI), which reinforces spatial analytics [64]. GeoAI has been noted by Nugroho and Supangkat [65] for its ability to eliminate data duplication and to enhance the analysis of big geospatial data through the extraction of information from vast volumes of geospatial data using ML algorithms, such as the Support Vector Machine (SVM), Support Vector Regression (SVR), and Random Forest (RF), thus fostering the integration of GeoAI with the SDI of Indonesia. The historical disaster data available in a SDI provide a valuable input that can be analyzed using ML algorithms, along with other parameters depending on the hazard under research, such as in the case of forest fire risks [66], for making accurate hazard predictions and modeling disaster scenarios that can assist the emergency response teams to make crisis management strategies and plans as efficient as possible, like in the case of evacuation routes [64]. Additionally, the significance of Machine Learning in the evolution of Geographic Information Systems (GIS) is pointed out by Roy and Das [32], given the fact that the analytical processes can be automated in various fields, like land cover classification or environmental monitoring, while the predictive modeling needed for disaster responses can be reinforced and be more efficient in the case of natural hazards. Thereby, ML algorithms improve the framework of SDIs when considering the plethora of data-mining techniques [67]. ML algorithms have turned out to be beneficial approaches in the case of urban spatial planning problems [68], which in turn play a pivotal role in the efforts targeting Disaster Risk Reduction and urban resilience. Moreover, it has to be noted that the utilization of data science and Machine Learning can be advantageous for SDIs when concerning the automatic production of high-quality metadata [69].

4.3. Real-World Implementation Barriers

Implementation barriers toward achieving NSDI goals have been observed in the case of the South African SDI, where among the identified challenges are the lack of funding to facilitate cooperation, ambiguity regarding the gathering of thematic data due to conflicting legislation, deficiency of formal agreements or mechanisms that concern data sharing, issues related to personal data and disclosure risks, as well as uncertainties surrounding intellectual property rights [70]. It is noteworthy that the termination of financial support for the project related to “WebGIS Moçambique” and the SDI targeting the Zambezi valley were the reasons why these services ceased their operation [71]. With regard to the SDI of Mozambique, although the enhancement of geospatial data is of paramount importance for SDIs to be implemented, Mozambique lacks any legal or regulatory framework related to spatial data and data sharing is achieved without established sharing policies, in a context where there is a dearth of common standards in data production and an absence of metadata regulations [71]. Maphale and Smit [72] highlight that the legal framework and funding models are among the main constraints that exist in the domain of management and, as a result, those barriers are imprinted in the case of SDI by preventing participation when considering that various stakeholders must take part in this multi-organizational initiative in order for them to be implemented. Moreover, according to Adeleye et al. [73], insufficient funding, along with deficient collaboration across federal and state levels, has been noted as a hindrance to the NSDI in the USA, while inadequate funding also comprises a barrier in SDI development and implementation for low- and middle-income countries (LMICs). Limited funding brings about the imperfection of geospatial data systems, which are vital for public health improvement by reinforcing epidemiological monitoring [73]. Thus, Adeleye et al. [73] emphasize the necessity of SDIs in public health decision-making by mentioning that any change in governmental leadership could profoundly reallocate the funding resources, namely the discontinuation of long-term SDI initiatives in support of public health. An additional real-word obstacle toward SDI development is bureaucratic obstruction, which is evident in the gathering and sharing of spatial data geared toward public health issues across different agencies [73], while the necessity for bureaucracy to be reduced has also been highlighted by Nugroho and Supangkat [65] in favor of SDI development.

5. Conclusions

SDIs are as important as critical infrastructures that interconnect isolated and distant areas. SDIs gather a vast amount of geospatial data and spatially referenced data derived from multiple databases that can be utilized, as long as interoperability is efficient. Given that any damage to transportation, energy, and telecommunications infrastructures results in detrimental consequences to the determinant functions of society [74], in this respect, the ineffectiveness of a NSDI targeting disaster management, due to the challenges highlighted in this study, will adversely affect the efforts of stakeholders to prevent the devastating repercussions of natural hazards.

NSDI is a skillful tool to fulfill the 17 SDGs in the context of the 2030 Agenda for Sustainable Development, where these goals require an international collaboration by all developed and developing countries, hence reflecting the needed cross-organizational coordination on a national scale between the different levels of the SDI hierarchy [37], which in the aftermath will bring about the required partnership across NSDIs. NSDIs can benefit considerably from GEO and Copernicus for the reason that the provided data and services, which are oriented toward the study of international issues about the Earth, can be useful resources for national environmental challenges so as to reinforce decision-making processes. AI makes up the research orientation of SDIs, given the fact that there are large Earth observation and geospatial data that require ML algorithms, big data processes, and high-performance computing to derive meaningful insights from them. Among the challenges confronted by NSDI, those presented in theory are confirmed in practice by the case studies addressed above, and according to the latter are the legal aspects, data quality, cross-departmental collaboration, limited inter-organizational coordination, data accessibility, and the incorporation of accurate and detailed localized data. Moreover, additional barriers are the inconsistency of the metadata, the system interface limitations affecting usability, the interoperability impairment issues, and the necessity of new data sources to be considered. Additionally, further constraints are the non-exploitation of AI and ML to amplify the effectiveness of the NSDI, the deficiency of a national data policy, the lack of clarity of stakeholder responsibilities, and the non-existence of a data sharing framework. However, bureaucratic issues play a central role in the effort to implement SDIs, while funding can determine both their maintenance and interruption.

Taking into account the necessity of analyzing geospatial data for Disaster Risk Reductions, a limitation regarding the development SDIs in the domain of natural hazards is related to data accessibility. It must be mentioned that disaster data are derived mostly from public organizations. Hence, due to the non-existence of a data sharing legal framework leading to bureaucratic issues, the end-users have to explore alternative data sources, which in the aftermath, result in a time-consuming search and further data processing.

A future research direction of SDIs pertains to the development of SDIs tailored to planetary science, giving rise to Planetary Spatial Data Infrastructures (PSDIs). A case study has been performed by Laura et al. [75], particularly the case of the PSDI of Europa, a moon orbiting Jupiter, where data such as the photomosaic of Sidon Flexus (a geological feature on Europa) and digital terrain models (DTMs) by various sources can be explored and accessed. Another aspect worth investigating further is the usability assessment of the SDIs depending on the user’s opinion to determine if the infrastructure aligns with usability heuristics, as was implemented in the Brazilian SDIs by deploying the HEUA-SDI method [76], where pilot questionnaires consisting of 40 tasks, referring to the visibility of system status, consistencies and standards, flexibility and efficiency in use, etc., were administered to 16 Brazilian SDIs (i.e., SDI of the state of Minas Gerais, SDI of the state of Espírito Santo, etc.). A further consideration for the ongoing research is the reinforcement of NSDIs with three-dimensional (3D) models of real estate for property valuations within the framework of smart city applications, as explored in the case of the NSDI of Türkiye in the city of Amasya by employing the ISO 19152-4 Land Administration Domain Model [77] Part 4: Valuation information and value affecting parameters [78]. Lastly, yet importantly, it must be highlighted that the countries have to encounter in their entirety an abundance of different natural hazards along with their cascading effects, like in the case of Greece [79]. Thus, considering the gains of AI and geospatial modeling in disaster management, like in the case of disaster detection and post-disaster mapping requiring real-time or near real-time precise spatial data [80], it is critically important for governments, which are the leading entities in the establishment of SDIs in Europe, to adopt more open data policies so SDIs are open to non-government interested parties, like businesses, citizens, and other stakeholders [81]. A pilot case study was performed in four European countries, in particular, the Netherlands, the United Kingdom, Denmark, and Finland, who allowed stakeholders from the private sector, namely non-governmental participants (i.e., business community), for the development of their NSDIs, resulting in a greater accessibility of spatial data and spatially enabled services [81]. Although the aforementioned countries made the majority of their geospatial datasets publicly accessible through national geoportals, which in this context are open data portals, and permitted their reuse across diverse applications via the adoption of common licensing terms, the public authorities are still the driving forces behind the deployment and decision-making process of SDIs in Europe. Consequently, the aforesaid pilot study highlights that it is imperative to move from opening the data to opening the infrastructure itself, meaning that companies and research organizations should be included in the management and coordination of these infrastructures so that SDIs are open to all concerned participants [81].