Bridging Humanitarian Mapping and the Sustainable Development Goals

Abstract

The Sustainable Development Goals (SDGs) have become the global framework for evaluating the effectiveness of humanitarian projects. Humanitarian mapping is considered a popular voluntary geographic information technique that provides data for disaster response. Although humanitarian mapping has contributed significantly to the SDGs, there is a lack of in-depth studies on the state of this relationship. This paper aims to assess the potential relationship between the SDGs and humanitarian mapping by (1) analyzing SDG indicators to determine their potential contribution to humanitarian mapping, and (2) identifying the actual contribution of humanitarian mapping projects to the SDGs. To achieve this, the study uses a structured methodology that combines SDG indicator analysis with project-level data filtering and text mining. Three major humanitarian mapping platforms—HOT-TM, MapSwipe, and Ushahidi—are examined in order to capture their potential and actual contributions to the SDG framework. Ultimately, the study highlights the strong alignment between humanitarian mapping activities and the need to monitor the SDGs, particularly in water, urban infrastructure, and land use, emphasizing the potential of volunteer-driven geospatial data to address critical data gaps.

1. Introduction

1.1. From Agenda 2030 to SDGs

In 1987, the Brundtland Commission introduced the concept of sustainable development (SD), defining it as the “paths of human progress that meet the needs and aspirations of the present generation without compromising the ability of future generations to meet their needs” [1,2]. Since then, SD has become the main topic of numerous theoretical and practical studies [3,4]. The United Nations (UN), as a worldwide intergovernmental organization (IGO) with members from 193 sovereign states [5], plays its role by developing frameworks for the implementation of SD, particularly the Millennium Development Goals (MDGs), from 2000 to 2015, and the more recent Sustainable Development Goals (SDGs) [6].

In September 2015, the adoption of “Transforming our World: The 2030 Agenda for Sustainable Development” established a new universal development agenda that acts as the blueprint for the action of the majority of nations [6,7,8]. The framework has become the main tool for UN Member States to report on SD progress in their countries across 17 different key areas. According to Geng et al. (2018), in the 15 years from 2016 to 2030, each nation agreeing with the SDGs should keep up with the progress in implementing the goals and targets, at all levels [9]. The SDGs comprise 17 goals that have been agreed to be key achievements for global sustainability. To ensure the success of the goals, 169 targets and 247 indicators have been developed. On the one hand, the targets keep the global ambition of the goals, on the other hand, they need to adapt to local policy and needs in each country. They specify the method of realization, stabilizing political opinions, and provide data for the common achievements. This system has been emphasized as comprehensible and valuable [6].

Data availability (DA), a system for assessing data contribution for the SDGs, is reviewed annually and decided by the Inter-agency and Expert Group on SDG Indicators (IAEG-SDG). The chosen DA system in this paper is from 10 April 2025 [10]. In this system, each indicator is categorized in two tiers corresponding to the quality of data entry.

• Tier I: Indicator is conceptually clear, has an internationally established methodology and standards are available, and data are regularly produced by countries for at least 50 per cent of countries and of the population in every region where the indicator is relevant.
• Tier II: Indicator is conceptually clear, has an internationally established methodology, and standards are available, but data are not regularly produced by countries. [10].

The data monitoring gap for each SDG indicator can be identified by checking its tier. For example, an indicator ranked in tier II tends to lack data, a framework, and a standard for data acquisition and integration compared to an indicator in tier I and vice versa. In some minor cases, indicators can have multiple tiers of DA depending on the mixture of indices [10].

1.2. Geospatial Information for the Quality of Each Indicator

For SDG monitoring, geospatial location is one of the important keys for measuring indices and disaggregation. This has been proven to be the central motivation of indicator monitoring systems, answering the where questions [6,11,12,13]. State-of-the-art technology, such as open geographic data, mobile devices, web mapping, and crowd-sourcing platforms, are crucial for the process of collecting geolocation data [12,14]. To manage this aspect, the Working Group on Geospatial Information by IAEG-SDGs (IAEG-SDGs-WG-GI) was formed. In August 2017, they met and proposed a list of 24 indicators in the SDGs that benefit from geospatial information (GI) technology. In this list, there are 15 indicators that can be contributed directly by GI and 9 indicators that can be well supported. The Group on Earth Observations (GEO) has also proposed an additional 29 indicators for the list of indicators that can benefit from GI [11,13,15] (

Table A1. The analysis of the SDG indicators under the criteria of HM contributions.

Goal	Indicator	Indicator Description	Data Availability	IAEG- SDGs- WG- GI	GEO	HM-I	HM-E	Mapping Types	Notes
1	1.1.1	1.1.1 Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)	Tier I	X			X	Digitisation	Mapping buildings to support demographic distribution data
1	1.4.1	1.4.1 Proportion of population living in households with access to basic services	Tier I			X		Digitisation	Mapping buildings and public facilities; support data disaggregation by geographic location
	1.4.2	1.4.2 Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenure	Tier II	X			X	Classification	Mapping landuse; support data disaggregation by geographic location
	1.5.1	1.5.1 Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population	Tier I			X		Digitisation	Mapping damaged buildings; support data disaggregation by geographic location
2	2.4.1	2.4.1 Proportion of agricultural area under productive and sustainable agriculture	Tier II	X			X	Classification, Digitisation	Mapping landcover (agriculture area); support data disaggregation by geographic location
3	3.8.1	3.8.1 Coverage of essential health services	Tier I			X		Digitisation	Mapping distribution of essential health services; support data disaggregation by geographic location
	3.9.1	3.9.1 Mortality rate attributed to household and ambient air pollution	Tier I		X			Digitisation, Conflation	Building mapping (building and ambient air quality mapping)
4	4.5.1	4.5.1 Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregated	Tier I/II	X			X	Conflation	Support calculating location dimension of Parity Index
5	5.2.2	5.2.2 Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence	Tier II	X			X	Digitisation	Mapping place of occurence;support data disaggregation by geographic location
	5.4.1	5.4.1 Proportion of time spent on unpaid domestic and care work, by sex, age and location	Tier II	X			X	Digitisation	Support data disaggregation by geographic location
	5.a.1	5.a.1 (a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure	Tier II	X			X	Classification, Digitisation	Mapping landcover (agriculture area); support data disaggregation by geographic location
	5.a.2	5.a.2 Proportion of countries where the legal framework (including customary law) guarantees women’s equal rights to land ownership and/or control	Tier II	X
6	6.1.1	6.1.1 Proportion of population using safely managed drinking water services	Tier I			X		Digitisation	Mapping location of drinking water sources; support data disaggregation by geographic location
	6.2.1	6.2.1 Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water	Tier I/II			X		Digitisation	Support data disaggregation by geographic location
	6.3.1	6.3.1 Proportion of domestic and industrial wastewater flows safely treated	Tier I/II		X		X	Digitisation, Conflation	Mapping wastewater sources;
	6.3.2	6.3.2 Proportion of bodies of water with good ambient water quality	Tier I	X			X	Classification, Digitisation, Conflation	Mapping water (including basin, stream, water surface, pollution points); support data disaggregation by geographic location
	6.4.1	6.4.1 Change in water-use efficiency over time	Tier I			X		Digitisation	support data disaggregation by geographic location
	6.4.2	6.4.2 Level of water stress: freshwater withdrawal as a proportion of available freshwater resources	Tier I		X		X	Digitisation	Support data disaggregation by geographic location
	6.5.1	6.5.1 Degree of integrated water resources management	Tier I		X		X	Digitisation	Support data disaggregation by geographic location
	6.5.2	6.5.2 Proportion of transboundary basin area with an operational arrangement for water cooperation	Tier I	X			X	Digitisation, Classification, Conflation	Mapping water (including basin, stream, water surface, pollution points); support data disaggregation by geographic location
	6.6.1	6.6.1 Change in the extent of water-related ecosystems over time	Tier I	X			X	Classification, Digitisation, Conflation	Mapping water (including basin, stream, water surface, pollution points); support data disaggregation by geographic location
7	7.1.1	7.1.1 Proportion of population with access to electricity	Tier I		X		X	Digitisation	Support data disaggregation by geographic location
9	9.1.1	9.1.1 Proportion of the rural population who live within 2 km of an all-season road	Tier II	X			X	Digitisation, Conflation	Mapping buildings and all-season roands; support data disaggregation by geographic location
	9.3.1	9.3.1 Proportion of small-scale industries in total industry value added	Tier II			X		Digitisation	Mapping small-scale industries distribution
	9.4.1	9.4.1 CO2 emission per unit of value added	Tier I		X
	9.c.1	9.c.1 Proportion of population covered by a mobile network, by technology	Tier I	X			X	Digitisation	Mapping living area of the population who is covered by a mobile network, broken down by technology
10	10.7.4	10.7.4 Proportion of the population who are refugees, by country of origin	Tier I			X		Digitisation	Support data disaggregation by geographic location
11	11.1.1	11.1.1 Proportion of urban population living in slums, informal settlements or inadequate housing	Tier I		X		X	Digitisation	Mapping buildings; support data disaggregation by geographic location
	11.2.1	11.2.1 Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities	Tier I	X			X	Digitisation	Mapping public transport accessibility (Point of interest); support data disaggregation by geographic location
	11.3.1	11.3.1 Ratio of land consumption rate to population growth rate	Tier I	X			X	Digitisation, Conflation	Mapping urbnized area, urbanized area change, built-up area; support data disaggregation by geographic location
	11.5.1	11.5.1 Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population	Tier I			X		Digitisation	Mapping damaged buildings; support data disaggregation by geographic location
	11.5.2	11.5.2 Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)	Tier I			X		Digitisation	Mapping damaged buildings; support data disaggregation by geographic location
	11.5.3	11.5.3 (a) Damage to critical infrastructure and (b) number of disruptions to basic services, attributed to disasters	Tier I			X		Digitisation	Mapping damaged buildings and critical infrastructure; support data disaggregation by geographic location
	11.6.1	11.6.1 Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated, by cities	Tier I			X		Digitisation	Mapping municipal solid waste collected and managed in controlled facilities mapping; support data disaggregation by geographic location
	11.6.2	11.6.2 Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)	Tier I		X			Digitisation, Classification, Conflation	Air quality mapping
	11.7.1	11.7.1 Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities	Tier I	X			X	Digitisation	Mapping buildings, roads, public spaces; support data disaggregation by geographic location
	11.7.2	11.7.2 Proportion of persons victim of non-sexual or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months	Tier II	X			X	Digitisation	Mapping place of occurence; support data disaggregation by geographic location
12	12.a.1	12.a.1 Installed renewable energy-generating capacity in developing and developed countries (in watts per capita)	Tier I		X
13	13.1.1	13.1.1 Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population	Tier I		X		X	Digitisation	Mapping damaged buildings; support data disaggregation by geographic location
14	14.1.1	14.1.1 (a) Index of coastal eutrophication; and (b) plastic debris density	Tier II			X		Digitisation, Classification	Support geospatial analysis
	14.2.1	14.2.1 Number of countries using ecosystem-based approaches to managing marine areas	Tier II	X
	14.3.1	14.3.1 Average marine acidity (pH) measured at agreed suite of representative sampling stations	Tier II		X
	14.4.1	14.4.1 Proportion of fish stocks within biologically sustainable levels	Tier I		X
	14.5.1	14.5.1 Coverage of protected areas in relation to marine areas	Tier I	X			X	Digitisation, Classification	Mapping protected area
15	15.1.1	15.1.1 Forest area as a proportion of total land area	Tier I	X			X	Digitisation, Classification	Mapping Forest Area
	15.1.2	15.1.2 Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type	Tier I	X			X	Digitisation, Classification	Mapping protected area
	15.3.1	15.3.1 Proportion of land that is degraded over total land area	Tier I	X			X	Classification, Digitisation	Mapping degraded land area
	15.4.1	15.4.1 Coverage by protected areas of important sites for mountain biodiversity	Tier I	X			X	Digitisation, Classification	Mapping protected area
	15.4.2	15.4.2 (a) Mountain Green Cover Index and (b) proportion of degraded mountain land	Tier I	X			X	Digitisation, Classification	Mapping Mountain Area, Landcover and degraded land area
16	16.1.2	16.1.2 Conflict-related deaths per 100,000 population, by sex, age and cause	Tier II			X		Digitisation	Mapping place of death
	16.1.4	16.1.4 Proportion of population that feel safe walking alone around the area they live after dark	Tier II			X		Classification, Digitisation, Conflation	Mapping neightborhood
17	17.6.1	17.6.1 Fixed broadband subscriptions per 100 inhabitants, by speed	Tier I		X
	17.18.1	17.18.1 Statistical capacity indicators	Tier I		X

, Appendix A).

The 64th UN Conference of European Statisticians (Paris, 27–29 April 2016) highlighted the importance of geospatial data and analysis in the SDG monitoring framework, recommending three levels of contribution:

• Contributing directly to the proposed framework, not only in terms of sources of information to increase data availability and spatial disaggregation but also in terms of methods and analysis to produce indicators resulting from the integration of geospatial and statistical information;
• Promoting common accepted standards and frameworks to guarantee comparability;
• Encouraging innovation and modernization [15].

This study focuses on the first method of contribution; therefore, the direct improvement of data availability is considered.

1.3. From Citizen Science to Humanitarian Mapping

Citizen science (CS) has an important role in contributing to the SDGs [16,17,18,19]. CS is defined by the Oxford English Dictionary as “scientific work undertaken by members of the general public, often in collaboration with or under the direction of professional scientists and scientific institutions” [20]. In a systematic review of definitions, CS can be briefly understood as cross-disciplinary, “community-based” activities of “data monitoring and collection”, toward participatory, inclusive, and deep engagements [21]. Therefore, crowd-sourced data is the main object of CS. A branch of CS dealing with geospatial information, especially for disaster risk response (DRR), is volunteered geographic information (VGI) [22,23]. VGI (volunteered geographic information) refers to geospatial data obtained by citizens from different backgrounds who contribute to appropriate web platforms, thanks to the development of Web 2.0 and the Global Navigation Satellite System (GNSS) [22,24,25,26]. Based on content generation, VGI is classified as either active/conscious or passive/unconscious [26]. The term ’crowd-sourced geographic information’ (CGI) was later introduced as an alternative to VGI to encompass the two content generation methods [27]. It has also been noted that in numerous cases the UN has replaced its traditional mapping method with VGI/CGI, as it allows for saving costs on expensive closed-source map production and increasing data maintainability [28].

Humanitarian mapping (HM) is a separate field focused on providing maps and geospatial data for humanitarian purposes [29]. Mapping is central to humanitarian efforts, because in most cases, these activities face the problem of geospatial information inequality [30]. The earliest records of HM can be traced back to the 19th century, almost at the same time as the beginning of humanitarianism. Over time, it is observable that HM has changed from analog to digital mapping, from fine detail to large-data acquisition, from individual work to networked collaboration [29].

In the last fifteen years, these two fields have merged, sharing multiple characteristics. In 2010, the catastrophic earthquake that occurred in Port-au-Prince, Haiti, has marked a significant evolution of HM through the use of VGI/CGI platforms such as OpenStreetMap (OSM) and Ushahidi [16,31,32,33]. The historical process of HM in the OSM community has been described by Herfor et al.; they indicated that HM “refers to collaborative mapping in OSM […] for both humanitarian relief responses and humanitarian purposes in general” and “remote mapping or digitization and consists of the generation of geographic data based on satellite imagery” [16]. Others consider HM as a branch of CS and VGI that efficiently provides data for DRR and the SDGs [14,31,34,35]. In the extent of this paper, the scope of HM is narrowed to digital HM contributed by VGI/CGI.

1.4. Humanitarian Mapping as One of the Pillars in the SDG Data Framework

Other sources of data include crowd-sourced data, which is one of the four main pillars of reliable data contribution in the National Spatial Data Infrastructure system for the SDGs, in addition to Earth Observations and Monitoring; National Spatial Data Infrastructure; and National Statistical Systems, Accounts, Administrative, Registers, Demographics [7,36] (Figure 1). As mentioned in Section 1.3, crowd-sourced data can be found throughout the HM process. In particular, OpenStreetMap (OSM), the global HM data source and community, has played a significant role in covering the gaps in missing and outdated data for the SDGs in certain countries [37,38]. It is also stated that, in certain cases, DRR community-based innovation is strongly encouraged [38,39]. HM applications for the SDGs are already well promoted in universities [35,40,41,42]. Meeting the requirement for three levels of contribution to the SDGs (direct contribution, promotion, and innovation engagement) mentioned in Section 1.2, HM is identified as a robust, global, and highly available source of data acquisition for the monitoring of the SDGs.

Generally, there are three types of HM activities, categorized by the use and generation of data.

• Classification: The process of assigning predefined attributes (values/categories) to existing geographical information.
• Digitization: The process of creating new digital geographic objects based on existing geographic information.
• Conflation: The process of integrating existing geographic information representing the same real-world object into a consistent digital representation [43].

Although there are multiple potential applications of HM as an effective source for bridging SDG data gaps, there have been few recent studies reviewing the state of the art in the role of HM in SDG contributions. This paper explores two main questions: Which indicators can potentially benefit from HM activities? What is the current condition of HM activities’ contributions to the SDGs?

This paper examines the potential contribution of three platforms—HOT-TM (digitization), MapSwipe (classification), and Ushahidi (conflation)—to the SDG monitoring framework. A detailed comparison has been carried out and is presented in

Table 1. A comparison of the three selected platforms in terms of accessibility, data input, project type, and contribution methods.

	HOT-TM	MapSwipe	Ushahidi
Accessibility	Web browser	Web browser, mobile application	Web browser
Data sources	Satellite	Satellite	Mixed (email, news, other sources)
Project type	Digitization	Classification	Conflation
Project geographic border	Predefined	Predefined	Non-defined
Project management	Project	Project	Deployment
Task unit	Task	Task	Post
Task number	Limited	Limited	Unlimited
Data contribution	OSM	Proper server	Proper server

.

• HOT-TM is a platform develop by the Humanitarian OpenStreetMap Team (HOT) for OSM digitization, dedicated mostly to HM purposes [44]. HOT-TM splits the mapping area into a grid of smaller squared areas, called tasks. Each mapper is assigned a task and collaborates with the other members of the team without worrying about conflicts.
• MapSwipe is a web and mobile application in which a user classifies a certain area by looking at satellite images [45]. Similarly to HOT-TM, MapSwipe assigns tasks to the mapper, represented as 3 × 3 squares, in both the cases of mobile and web browsers. Users are instructed to examine the basemap and report the presence of mapping objects by tapping each square on the screen.
• Ushahidi, founded in 2008, is a powerful toolbox for gathering monitoring data at a fast pace in the web environment [46]. The organizers create deployments on specific themes, then users can contribute by reporting emails, text, and multimedia through posts. Unlike HOT-TM and MapSwipe, each deployment is open-ended, organizers can expand the project without creating its replica, and there is no need to eliminate duplication theoretically.

2. Materials and Methods

Two aspects are analyzed in this paper: (i) the potential contribution of HM to the structure of the SDG indicators, and (ii) the actual contribution of HM projects. As shown in Figure 2, in this study, we consider 2 main aspects: (A) SDG indicator analysis, and (B) HM project analysis. The process has 3 steps: (i) pre-processing, (ii) analysis, and (iii) synthesis. These develop into 7 components: (1) SDG indicator investigation, (2) GI-benefited indicators, (3) HM’s potential to contribute to SDGs, (4) HM definition and development, (5) HM project extraction, (6) HM-filtered projects, (7) state of the art of HM’s contribution to SDGs.

The first aspect of this study investigates the potential of HM to contribute to the SDGs (components 1–4). With the insights into HM mentioned in the previous sections, 247 indicators have been individually reviewed by thoroughly checking the metadata and comparing with the historical dataset of geospatial information-supported indicators listed by IAEG-SDGs and GEO. The selected indicators must be able to obtain and aggregate HM data registered in their metadata. Two criteria are used to categorize them: the potential contribution of HM and the potential mapping technique type. For the first categorization, the indicators are separated into three groups:

• Introduced indicators (HM-I) are indicators that were not included in the IAEG-SDG-WG-GI and GEO lists, but are included in this study.
• Enhanced indicators (HM-E) are indicators that are mentioned in the IAEG-SDG-WG-GI and GEO lists, and have the potential to be improved by HM.
• Non-affected indicators are unrelated indicators.

Then, the indicators are labeled with three types of mapping techniques, as defined in Section 1.4. Each indicator can be tagged by one or more mapping techniques. At the end of this step, we propose a list of indicators to which HM activities have the potential to contribute.

The second aspect analyzes HM projects with regard to their actual contribution to the SDGs (components 5–6). As shown in Figure 3, HM projects were extracted from the sources then filtered using three criteria: recentness, contribution, and uniqueness. Firstly, the projects were sorted according to when they started, with 50% of the most recent ones being considered. The second step of the filtering process evaluated the contribution. The projects were sorted according to the percentage of completed tasks for HOT-TM and MapSwipe, while those in Ushahidi were sorted according to the number of posts. Fifty percent of the most valuable projects were kept. Finally, TF-IDF (term frequency–inverse document frequency) and cosine similarity were used to detect project replication [47,48].

TF-IDF is a technique that measures the importance of each term in a document d relative to the set of all documents, called the corpus, D.

$$TF\text{-}IDF(t,d)=f_{t,d}·log\left({\displaystyle \frac{N}{1+d_t}}\right)$$

where $f_{t,d}$ is the relative frequency of term t in the document d, N is the number of all documents in corpus D, and $d_t$ is all documents containing term t in corpus D.

In this study, the document is the concatenation of the projects’ selected features and the corpus is all the projects in one HM platform. Based on empirical insights, the selected features from HOT-TM projects are name, organization, author, and country; from MapSwipe projects are name, project_details, look_for and organization_name; and from Ushahidi are title, country, category, and excerpt. The TF-IDF vectorizer then represents each document as a multidimensional vector.

$\overrightarrow{d}=\left(\begin{array}{c}TF\text{-}IDF\left(t_0\right)\\ TF\text{-}IDF\left(t_1\right)\\ .\\ .\\ .\\ TF\text{-}IDF\left(t_n\right)\end{array}\right)$ where $t_0,t_1,...t_n$ are the different terms in document d

The cosine similarity measures the cosine of the angle between any two documents in the corpus. This results in numbers ranging from −1 to 1, which show the level of similarity, from low to totally identical. Project pairs with a cosine similarity score higher than 0.5 are marked as duplicates and one member is removed.

At the end of the screening process, 614 HOT-TM projects, 71 MapSwipe projects, and 42 Ushahidi projects were considered for the analysis.

The current state of HM and its relation to the SDGs was studied by comparing it to the list of indicators with potential contributions in order to form the dataset of actual contributions by HM to the SDGs. Then, we manually compared the scope of each selected project with the requirement of each indicator pointed out in the metadata. Based on the descriptions, all projects that were vague, irrelevant, general, or training-oriented were excluded. Subsequently, these statistical data were then used to visualize the coverage of HM projects in the SDG indicators (component 7).

For the final analysis, three new indicators are used: (1) introduced indicators refers to the number of indicators that were not included in GEO and IAEG-SDG-WG-GI’s lists but introduced in this study; (2) exclusive indicators refers to the number of indicators that are contributed to by only one HM platform; (3) low-data-availability indicators are indicators that are classified into tier II and tier I/II.

Finally, trends, gaps, limitations, and the future scope of the study are shown based on the findings.

3. Results

This section presents the main findings of the study, focusing on the potential and actual contributions of humanitarian mapping (HM) to the Sustainable Development Goals (SDGs). The analysis is structured into three parts: the assessment of SDG indicators influenced by HM (Section 3.1); the distribution of data sources across HM projects (Section 3.2); and the evaluation of the actual contributions of HM to specific SDG indicators (Section 3.3).

3.1. SDG Indicator Analysis

The results indicate that HM has the potential to support nearly all the SDGs. As shown in Figure 4, there is a strong alignment between several indicators (particularly Goal 6: Clean water and sanitation; Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable; and Goal 15: Make cities and human settlements inclusive, safe, resilient and sustainable) and HM activities. In total, there are 15 introduced indicators (HM-I) and 30 enhanced indicators (HM-E). The potential introduced indicators are highly concentrated in Goal 11 (four indicators) and Goal 6 (three indicators); while enhanced indicators are scattered between Goal 6 (six indicators), Goal 11 (six indicators), Goal 15 (five indicators), and Goal 5 (three indicators). HM was found to have no potential for contribution to Goal 8, Goal 12, or Goal 17. The tier I data indicators remain mainly in Goal 11 (nine indicators), Goal 6 (six indicators), and Goal 15 (four indicators). The tier II data indicators are mainly distributed in Goal 5 (four indicators), Goal 14 (three indicators), and Goal 6 (three indicators).

The number of indicators in each goal categorized by the three HM techniques is shown in Figure 5. Digitization is the leading HM approach in terms of the number of indicators (43 indicators), followed by classification (15 indicators) and conflation (10 indicators). Moreover, digitization is shown to be more contributive to Goal 6, Goal 11, and Goal 15 because mapping water, buildings, and land cover can contribute greatly to these goals. In other cases, classification is the most effective for Goal 15 because land cover mapping requires classification technology, while the conflation technique is more significant for Goal 6 and Goal 11 because mapping water and settlement requires an interdisciplinary approach.

It is also observed that one indicator can be involved in multiple HM approaches. For example, for indicator 16.1.4: Proportion of population that feel safe walking alone around the area they live after dark, HM can help classify imagery, digitize urban physical elements such as buildings and open spaces, and overlay various data, particularly population or local criminality. In other cases, as in indicator 11.6.1: Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated, by cities, waste-collection-point mapping is considered as purely related to digitization.

These findings show that the concentration of potential contributions in Goals 6, 11, and 15 suggests that HM is particularly well suited to address challenges relating to water, urban infrastructure, and land use.

3.2. Humanitarian Mapping Project Data Resource Distribution

HM is involved in the provision and the use of geospatial data for humanitarian aid. An HM project can collect data from multiple sources. VGI/CGI is currently the main source of HM data, but not all VGI/CGI projects are considered HM. In order to map HM activities in one holistic map, further efforts are needed. This study analyzes three popular VGI/CGI platforms in order to reveal the potential data resources for HM projects, without indicating precisely the geospatial distribution of HM projects.

The number of recent non-duplicated projects in the three investigated platforms aggregated by nation is shown in Figure 6. The three platforms work globally, but the geographic ranges are different. HOT-TM and MapSwipe are built within an OSM environment, focusing on disaster response in the Americas, most of Africa, South Asia, and the Asia–Pacific area. Meanwhile, Ushahidi is an independent database, and its projects are frequently found in the Americas and Europe, with others scattered across Africa, South Asia, and Oceania.

In detail, the number of projects in HOT-TM ranges from 1 to 70, with the highest number in Uganda (70 projects), and an average of 2.5 projects per country. A total of 71 projects are recorded in MapSwipe. The number of projects in each country ranges from 1 to 5, with a mean of 1.775 projects per country. Chad, India, and South Sudan have the highest number of projects on MapSwipe. Ushahidi has the fewest projects, with a total of 42 and a mean of 1.355 projects per country. The maximum number of projects per country is four, which can be observed in Colombia.

Distinct geographic patterns among the three platforms have been revealed by the analysis, heightening the global reach and regional focus of HM data contributions.

3.3. HM Contribution to SDG

HM projects contribute to 15 goals and to 30 of the 53 potential indicators (56.49%) (see Figure 7). HM is mainly present in Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable (eight indicators), Goal 6: Clean water and sanitation (five indicators), and Goal 15: Life on land (four indicators). There are 11 indicators that are introduced indicators and 14 exclusive indicators. With only nine tier II and tier I/II indicators contributed, HM projects do not focus on improving missing data indicators.

As summarized in

Table 2. Number of indicators contributed by HM platforms. The total number is computed considering the number of non-duplicated indicators.

Platform	Introduced Indicators (a)	Exclusive Indicators (b)	Low-Data-Availability Indicators (c)	Total Number of Unique Indicators
HOT-TM	11	11	8	27
MapSwipe	7	2	1	12
Ushahidi	7	1	1	15

^(a) Indicators that were not in GEO and IAEG-SDG-WG-GI but introduced in this study. ^(b) Indicators that are contributed by only one HM platform. ^(c) Indicators classified in tier II and tier I/II.

, HOT-TM ranks first in the total number of contributions, with 27 indicators, because HOT-TM is the largest platform in terms of the number of recent non-duplicated projects. Following this, Ushahidi has 15 indicators and MapSwipe has 12 indicators. Regarding the introduced indicators, we do not observe a significant difference: 11 indicators for HOT-TM and 7 indicators each for Ushahidi and MapSwipe. With 11 indicators, HOT-TM’s exclusive indicators outweigh MapSwipe (2 indicators) and Ushahidi (1 indicator). For low-data-availability indicators, HOT-TM projects have contributed to eight indicators, with only one for both MapSwipe and Ushahidi.

The analysis of HM projects’ geo-distribution reveals a predominant distribution of projects in the Global South and North America (see Figure 8). A total of 296 HM projects have used introduced indicators, with a high concentration in developing countries such as Brazil (12 projects), Nigeria (12 projects), Malawi (10 projects), India (9 projects), and the Philippines (9 projects). For the distribution of exclusive and low-data-availability indicators, there are a total of 21 and 23 projects, respectively. The highest numbers of projects were recorded in Uganda, Colombia, Mexico, and Somalia. We can observe that the three distinct platforms, each with a different scope, have covered a variety of potential indicators. However, there is still a lack of interest in improving the diversity and quality of contributions to the SDGs.

4. Discussion and Conclusions

4.1. Trends and Gaps

HM has made a great deal of data available for SDG contributions by using geospatial data for disaster relief. There is a focus on post-disaster damage mapping activities; for example, indicators 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population; 11.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP); and 11.5.3 (a): Damage to critical infrastructure, and (b): number of disruptions to basic services, attributed to disasters; with a particular emphasis on the affected population and damage to infrastructure. One reason is that the DRR framework can effectively integrate VGI/CGI and HM, especially in the response and recovery phases [30,34].

Environmental mapping is one important phenomenon, as observed in the contributions to Goal 6: Clean water and sanitation, and Goal 15: Clean water and sanitation. Water is a key focus in HM projects, for example, for mapping water access during the Malawi cholera outbreak [49]; mapping water security during TerresEauVie by USAID, Sustainable Water Partnership, and Winrock [50]; and waterway mapping for topographic maps for peacekeeping activities by UN Mappers [40,51]. Forest-area mapping is also a critical movement in HM, as observed in El Carmen forest monitoring (Mexico), and in the Amazon forest and buildings project by the Open Mapping Hub—Latin America and the Caribbean [52].

Urban areas are also common targets for HM, because urbanization is a global phenomenon. Informal settlement (11.1.1: Proportion of urban population living in slums, informal settlements or inadequate housing) and facility accessibility (11.2.1: Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities; 11.7.1: Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities; 9.c.1: Proportion of population covered by a mobile network, by technology; 9.1.1: Proportion of the rural population who live within 2 km of an all-season road) are the two primary types of this mapping. This can be explained by the fact that, in most cases, VGI/CGI data concerning settlements and infrastructures are visible on satellite imagery and therefore practically available via the HOT-TM and MapSwipe projects [53]. Moreover, open data on urban construction is complementary and highly integrative with other urban development projects [54].

However, energy and ocean mapping is found to be under-represented across the three HM platforms, as in Goal 14: Life below water and Goal 7: Affordable and clean energy. In most cases, submarine elements and electrical infrastructure are not readily identifiable in satellite imagery.

4.2. Limitations

This study has the following limitations: (A) the number of HM projects is updated rapidly, but the study could only capture the situation during research implementation; (B) the elimination of incomplete or duplicate projects may have led to information loss; (C) the review process was performed manually, therefore human errors and biases are inevitable; (D) the three platforms were chosen based on popularity, so the study may have missed contributions from lesser-known platforms.

4.3. Contribution

This study described the current state of HM projects’ potential and actual contributions to the SDGs. The finding that HM projects pay insufficient attention to diversifying data themes and working areas, and improving indicators with low-availability data, could be explored further in other studies and enhanced in future HM projects. The assessment pipeline presented in this paper could be reused or integrated into future reports to support SDG assessment over the next five years (2025–2030).

4.4. Future Directions

Crowd-sourced data and humanitarian mapping have undeniable advantages for the SDGs. The question that should be raised is how to monitor HM projects in order to respond to global needs. Two development tracks could be proposed: extraction of the SDG indicators and project orientation.

For the first of these, using up-to-date artificial intelligence (AI) technology, e.g., large language models, one could analyze HM projects’ description in depth in terms of mining data in connection with one or a set of the SDG indicators (a primitive prototype of the application has been developed during this study; see Figure 9). In the future, the review process of the projects and of the SDG indicators should be automatic and enhanced in terms of the rapid detection of the SDG indicators embedded in HM projects and the quantification of large datasets of HM projects. The data from this study could be reused to train deep learning models, thereby fostering automatic progress. Reducing the time spent reviewing the completeness of indicators could enhance the quality of SDG data flow monitoring.

The second track would focus on how HM projects are formed. AI could help suggest relevant SDG indicators during the creation of HM projects. It is recommended that this application be integrated into HM platforms. In addition, the application should also guide projects in providing the necessary data for humanitarian projects and contributing data to the urgent SDG indicators. An application could be developed to suggest to users in the area the platforms that would be most efficient, the requirements of the HM project, and the description and instructions for future projects.