CAPACITY BUILDING FOR GIS-BASED SDG INDICATORS ANALYSIS WITH GLOBAL HIGH-RESOLUTION LAND COVER DATASETS

: The support of geospatial data and technologies for the United Nations Sustainable Development Goals (SDG) framework is critical for assessing and monitoring key indicators, revealing the planet’s trajectory towards sustainability. The availability of global open geospatial datasets, especially high-resolution land


INSTRODUCTION
The support of geospatial data and technologies to the United Nations Sustainable Development Goals (UN SDGs) framework has turned out to be critical for both the assessment and the monitoring of key indicators, revealing the trajectory of our planet and society towards sustainability (Chen et al., 2020).The increasing availability of global coverage and openly licensed geospatial datasets unlocks noteworthy opportunities for the computation and comparison of these indicators across different geographical regions, as well as multiple spatial and temporal scales (UN-GGIM, 2021).
To that end, a notable class of global geospatial products is represented by High-Resolution Land Cover (HRLC) datasets which have a critical role in many scientific and policymaking applications, including climate modelling, natural resource management, landscape and biodiversity preservation, and urbanization monitoring (Bratic et al., 2021).Global HRLC data, coupled with complementary global open geospatial layers (such as spatial demographic datasets, satellite-derived soil and vegetation products, etc.) provide plentiful capabilities to investigate SDG indicators (Avtar et al., 2020).
Generally, national or local geospatial data provide (where available) a higher spatial and temporal resolution than coarser global products and -in turn -better capabilities for accurate computation of SDG indicators.However, the uneven availability of such data often prevents consistent assessment of SDG progress across countries worldwide.In this context, the use of open global geospatial datasets represents a quick, efficient, and cost-effective solution for providing a first comprehensive assessment before local data is produced and analysed (Carter, 2019).The tangible benefits of these datasets are particularly evident in developing countries, where the availability of such information from local authorities is often limited.Nevertheless, there are still several barriers to their proficient use due to the lack of data awareness, management and processing capacities using Geographic Information Systems (GIS) software tools (Brovelli et al., 2018).The presence of sufficient human expertise in GIS technology is recognized as a crucial foundation for effectively incorporating geospatial information into SDG implementation.Particularly in developing countries, this capacity is still in its early stages, and there is a strong need for initiatives that prioritize capacity building in GIS for SDGs (Sarvajayakesavalu, 2015).
Given the above, we present here the Capacity Building

GLOBAL GEOSPATIAL DATASETS FOR SDG INDICATORS
Despite the development of the SDG indicators framework was originally based on traditional statistical data, many statistical offices and governmental agencies have recognised the need for geospatial data to augment non-spatial information and provide new and consistent data sources, which are critical to SDGs monitoring (UN-GGIM, 2021).In fact, It has been estimated that approximately 20% of the SDG indicators can be interpreted and measured either through the direct use of geospatial data itself or through integration with complementing statistical data (Allen et al., 2021).GIS, Earth observations and global-coverage geospatial data provide an important link to enable consistent comparison among countries, provide granularity and disaggregation of the indicators and communicate their geographic dimensions (Avtar et al., 2020).
Global geospatial data products that cover major thematic areas of the biosphere and society (e.g.land cover, vegetation productivity, forests, wetlands, surface water, human settlements, etc.) are strongly supporting the methodological development and measurement of several SDG indicators (European Space Agency, 2020).National or local geospatial data, when accessible, offer improved resolution for accurate measurement of SDG indicators compared to global data.However, the uneven availability of such data hinders consistent assessment of SDG progress worldwide.Open global geospatial datasets provide a quick, efficient, and cost-effective solution for conducting an initial comprehensive assessment before national data becomes available (Carter, 2019;Allen et al., 2021).

Global high-resolution global land cover datasets
A key class of global geospatial products, which plays a major role in many SDGs, is the class of high-resolution global land cover (HRLC) maps (Bratic et al., 2021).The land cover stands for all the geospatial information describing the physical and biological cover of the Earth's surface.Land cover data products at high spatial resolution have a critical role in many scientific and policy-making applications, including climate modelling, natural resources management, landscape and biodiversity preservation, urbanization monitoring, and spatial demography (Radwan et al., 2021).In the last two decades, the advancement of satellite Earth observation as well as the availability of global coverage satellite imagery with high temporal resolution -available as free and openly-licensed datahave significantly promoted international coordination in SDGs monitoring and favoured production, access and usability of global and multi-temporal HRLC data products (Avtar et al., 2020;Guo et al., 2023).
Accordingly According to Carter (2019), fit-for-purpose global HRLC data should be characterized by a minimum spatial resolution of 1 km and a minimum temporal resolution of 3 to 5 years.The thematic detail is instead indicator-specific.Finally, the accuracy of global HRLC data generally varies across the globe and local validation is strongly suggested depending on study areas and the user-expected accuracy performances (Brovelli et al., 2018).Some of the most popular and mature, openly available, and fit-for-purpose global HRLC datasets are reported and detailed in Table 1.

Complementary global geospatial datasets
Global HRLC data were also used in combination with complementary global geospatial information demonstrating their capabilities of directly supporting the computation of indicators e.g. from SDG 2 (Radwan et al., 2021), 6 (Mulligan et al., 2020), and 9 (Ilie et al., 2019).Actually, land cover is only one of the Global Fundamental Geospatial Data Themes to SDGs identified by the UN (UN-GGIM, 2019).These fundamental themes include a variety of geospatial datasets which are necessary to spatially represent key phenomena and objects for the realisation of economic, social, and environmental targets identified by the SDGs.These datasets include -among othersdemography, soil/vegetation parameters, administrative boundaries, infrastructures, transport networks, 2D/3D topographic maps, water bodies, etc. (UN-GGIM, 2021).The suitability of complementary geospatial datasets is challenging to assess globally for each specific SDG indicator or country.Spatial and temporal resolution, thematic details and accuracy should be evaluated case-by-case, using recommendations valid also for the global HRLC data, and their use should be functional to the provision of baseline data in support of national SDGs reporting (Arnold et al., 2016).HRLC data for the computation of land cover-connected SDG indicators.

TRAINING REQUIREMENTS
The identification of training requirements and the primary recipients of the training material played a crucial role in the project's development.discussions with experts and potential users who were involved in all phases of the project.
To outline and assess the training requirements, the project team conducted an open survey.The survey was distributed among the project investigators and extended contacts, including the media channels of the ISPRS Student Consortium, in alignment with the project's educational objectives.The survey results helped identify the primary user group and their specific training needs.
Additionally, the survey collected valuable feedback on the initiative and generated an initial list of individuals interested in participating in the training.Survey respondents (as of May 2022) were 102 whose demographics are reported in Figure 1.
Respondents' familiarity with SGDs, GIS and global geospatial data was also investigated, together with their interest in using the training material.
The respondents' group was composed of mainly graduate students and both young researchers and professionals who, accordingly, are considered the primary users' group for the project.Respondents declared a good knowledge of GIS while a generally lower familiarity with SDGs (see Figure 2) and with the use of global maps to compute SDG indicators.More than 70% of the respondents provided a personal email address to receive updates from the project and declared interest in open online training material as well as workshops on the project topics (see Section 5).

TRAINING MATERIAL DEVELOPMENT
The training material was designed as a web book and includes an introductory practical course on the combined use of open GIS and global HRLC and complementary datasets to compute SDG indicators.The web book sections are enriched with a theoretical/informative preamble to the SDGs and their connection to geospatial data and GIS, descriptive lists of relevant datasets, and hands-on exercises (see Figure 3a).Currently, the practices section of the web book (see Figure 3b) proposes computer exercises on the computation of indicators 15.1.1 (Forest area as a proportion of total land area), 11.3.1 (Ratio of land consumption rate to population growth rate), 9.1.1(Proportion of the rural population who live within 2 km of an all-season

DISSEMINATION AND OUTREACH
Workshops connected to the training material were envisaged by the project proposal and further justified by the survey responses, as discussed in Section 3.
Three public workshops were organised during which both project development works and outputs were presented.Workshop 1 focused on the preliminary project presentation, includ- Finally, the web book was listed on the UN Global Geospatial Information Management (UN-GGIM) Academic Network's website, within the educational material section (http://un ggim.academicnetwork.org/educational-material)with the goal of promoting its diffusion.

CONCLUSIONS
The ultimate goal of the presented project is to enhance awareness of the connection between GIS and SDGs and engage, especially, GIS students and practitioners due to their potential key contribution to achieving Agenda 2030 in the next few years.To that end, advocating open data and software in this path is critical for maximising access to the required knowledge for students worldwide (without any economic barrier).The transformative potential of geospatial technologies in capacitybuilding for SDGs emphasizes the importance of collaboration, knowledge sharing, and inclusive approaches to tackle complex global challenges.The open availability of training material, analysis methods and tools promoted by the project represents a small but significant step forward in this direction.
Future activities of this work will include a continuous revision of the developed material.Integration with additional case studies on different SDG indicators is also envisaged.

Figure 2 .
Figure 2. Familiarity of the survey respondents with SGDs and GIS.
Figure 3. (a) landing page and (b) sample of the practices section of the web book.ing survey outcomes, by seeking feedback from both potential users and domain experts on the course syllabus.This activity was carried out through the participation of the project team at the Geospatial Information-enabled SDG Monitoring (GI4SDG) Forum Track of the XXIV ISPRS Congress held in Nice, France on June 7, 2022.Workshop 2 was dedicated to pilot testing of the draft web book content by collecting feedback which was used useful to adjust and complete the final version of the material (recording available at: https://tinyurl.com/52xw95jd).Workshop 3 presents the final version of the web book and it was provided as an open blended -in person and online -lecture (recording available at: https://tinyurl.com/ms4b9e2m).
for GIS-based SDG Indicators Analysis with Global Highresolution Land Cover Datasets project, funded by the ISPRS Education and Capacity Building Initiatives 2022 (https: //www.isprs.org/society/ecbi).The project addressed the creation of open training material covering the complete learning process of discovering, accessing, and manipulating global open geospatial datasets for computing SDG indicators, with a focus on those directly connected to water and terrestrial ecosystems, urban environment, and climate.

Table 1 .
Selection of most popular and mature open global HRLC datasets suitable for SDG indicators computation.

Table 2 .
The training requirements and the training material syllabus were determined through surveys and active Selection of popular global open geospatial datasets, complementary to global HRLC data, relevant to SDG indicators computation.