# A database of disaster impacts in the Global South using Red Cross reports and Large Language Models

Laura Hasbini, Luca Severino, Mariana Madruga de Brito, Gabriela Gesualdo, Ana Maria Rotaru, David Bresch, Evelyn Mühlhofer, Jingxian Wang, Taís Maria Nunes Carvalho

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Laura Hasbini, Luca Severino, Mariana Madruga de Brito, Gabriela Gesualdo, Ana Maria Rotaru, et al.. A database of disaster impacts in the Global South using Red Cross reports and Large Language Models. 2026. hal-05503877

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
1 Title

2 A database of disaster impacts in the

3 Global South using Red Cross reports

4 and Large Language Models

5

6 Authors & Affiliations

7 Laura Hasbini ¹,²* - laura.hasbini@lsce.ipsl.fr, https://orcid.org/0009-0000-6875-1386

8 Luca G. Severino³,⁴* - luca.severino@usys.ethz.ch, https://orcid.org/0009-0008-4002-6535

9 Mariana Madruga de Brito⁵, mariana.brito@ufz.de, https://orcid.org/0000-0003-4191-1647

10 Gabriela C. Gesualdo⁶, gabriela.gesualdo@psu.edu, https://orcid.org/0000-0001-6589-3397

11 Ana Maria Rotaru⁷, anamaria.rotaru@polimi.it, https://orcid.org/0009-0008-8374-6348

12 David N. Bresch ³,⁴ - dbresch@ethz.ch, https://orcid.org/0000-0002-8431-4263

13 Evelyn Mühlhofer ⁴, Evelyn.Muehlhofer@meteoswiss.ch, https://orcid.org/0000-0002-5587-

14 9070

15 Jingxian Wang⁸,⁹, jingxian.wang@polimi.it, https://orcid.org/0000-0001-7650-9987

16 Taís Maria Nunes Carvalho⁵,¹⁰, tais.carvalho@informatik.uni-leipzig.de,

17 https://orcid.org/0000-0001-8658-9781

18

19 ¹Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212CEA-CNRS-UVSQ,

20 Université Paris-Saclay, Gif-sur-Yvette, France.

21 ²Generali France SAS, 93210, Saint Denis, France
22
23 Institute for Environmental Decisions, ETH Zurich, Universitätstr. 22, 8092 Zurich, Switzerland

24
25 Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich-Airport, Switzerland

26
27 Helmholtz-Centre for Environmental Research, Department of Urban and Environmental Sociology, Leipzig, Germany

28
29 Department of Geosciences, The Pennsylvania State University, State College, PA, United States

30 Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy

31 University School for Advanced Studies IUSS Pavia, Pavia, Italy

32 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

33
34 Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Universität Leipzig, Leipzig, Germany

35

36 *These authors contributed equally

37 Correspondence authors: Laura Hasbini and Luca Severino

38

39 Main Text: ~ 7000 words

40 Abstract: 170 words

41 3 Tables

42 6 Figures

43

44 Abstract

45 Damage from natural hazards exacts a heavy toll on society and is expected to increase under climate change. Yet, existing impact datasets remain limited and often biased toward

46
Northern countries and monetary losses. To help address these gaps, we present ROUGE; a new socio-economic impact database obtained using textual operational reports from the International Federation of Red Cross and Red Crescent Societies (IFRC). These reports are systematically collected and provide broad coverage of regions that are commonly underrepresented in existing sources. Using large language models, we extract qualitative and quantitative information on a wide range of non-monetary impacts at national and sub-national scales. The resulting dataset documents socio-economic impacts of natural hazards on the population and the built environment with a spatial detail reaching the subregional level, capturing impacts that are rarely included in conventional databases. This resource is designed to support research and applications that require geographically explicit information on socio-economic impacts of disasters, enabling more precise and inclusive analyses of socio-economic consequences of natural hazards across the world.

## Background & Summary

Natural hazards exact a heavy toll on society, and have caused over 2 million fatalities and 3.6 trillion Euros in damages worldwide in the past 50 years ¹. This trend is expected to continue in the future, as many disasters become more frequent and severe due to climate change and exposure shifts ².

Within this context, understanding how natural hazards lead to impacts is crucial for improving risk management and reducing damage. Gaining such understanding demands a comprehensive view of where and when disasters occur, as well as the magnitude of their impacts. While several databases recording the impacts of natural hazards at the global ¹ or regional scale³ do exist, they suffer from geographical biases ⁴, which can lead to under-reporting for the Global South ⁵ and result in overlooking certain risks in the most vulnerable regions. A second gap is that most impact databases focus on monetary losses, disregarding human, social, and environmental losses ⁶. This focus undermines the importance of non-monetary, intangible losses and further amplifies the bias towards impacts in the Global North, where more financial and insured assets are concentrated. Finally, only a few databases report impacts at the subnational scale, while information at fine spatial resolution would improve impact modelling precision, especially because hazard information does exist at much higher resolution than the national scale.

With the advent of big data and recent advances in computer science and artificial intelligence, a larger volume of data has become exploitable. Artificial intelligence and
80 machine learning tools have emerged as powerful tools for improving the understanding and
81 characterisation of natural hazards⁷ and their impacts⁸⁻¹⁰. In particular, natural language
82 processing has demonstrated strong potential for extracting information from scattered and
83 unstructured data sources, where manual extraction is too time-demanding. For instance,
84 recent work has shown how natural language processing can be used to automatically
85 identify drought impacts in large collections of news articles, extracting details about the
86 affected sectors, locations, and reported consequences through rule-based and machine
87 learning classifiers⁸. However, the performance of keyword search and rule-based
88 approaches is limited, requiring highly detailed and context-specific dictionaries¹¹. Moreover,
89 traditional classification models require large volumes of annotated training data, which are
90 time-intensive to produce, especially for rare impact types⁸. In such a context, large
91 language models (LLM) represent a more flexible and extensive tool for data extraction.
92 Latest studies demonstrate the effectiveness of LLM-based approaches in systematically
93 retrieving and analysing impact information¹⁰,¹².

94 Additionally, the use of such approaches in the field of disaster risk management is still at an
95 early stage, and challenges remain in developing methodologies and evaluating the
96 applicability of these methods. In particular, validating the extracted information is crucial,
97 given LLMs' propensity to hallucinate (i.e. produce inaccurate or unsupported information)¹³.
98 This validation task remains time-consuming, especially when reference data is scarce and
99 the data extracted is complex.

100 Against this background, the International Federation of the Red Cross' Societies (IFRC)
101 reports impacts from natural hazards since 1919¹⁴ in the form of written operation reports.
102 Part of the data that can be found in these reports is available in machine-readable format
103 through the IFRC GO platform¹⁴ and the IFRC's Montandon initiative¹⁵. These databases
104 only include figures on the number of fatalities, people affected, injured, displaced, and
105 missing at the national level. However, the reports provide information on a wider range of
106 impacts, such as health and infrastructure damage, and at a finer spatial resolution. Hence,
107 these written reports represent a valuable and largely underexplored resource for creating
108 impact datasets. Another advantage of this data is its explicit focus on disasters, which
109 reduces the biases that often affect other impact datasets derived from news⁸, or Wikipedia
110 articles¹⁰. Moreover, the IFRC provides systematic reporting on events affecting Global
111 South countries, which are frequently underrepresented in global impact datasets. As such,
112 the IFRC reports can provide a more inclusive and balanced perspective on disaster
113 impacts.
The combination of societal urgency, new artificial intelligence tools, and increasing volumes of text data provides a unique window of opportunity for studying the impacts of climate extremes. In this study, we leverage data from IFRC's textual reports and LLMs to produce a novel socio-economic impact database: ROUGE (Redcross Operations Unified Global Emergency database). Specifically, we focus on unconventional, non-monetary, qualitative, and quantitative impacts at the national and sub-national levels. Our database records the impacts of 716 disasters in 145 countries for 14 different hazards from 2016 to 2025. The database covers impacts for 20 subtypes of impacts from three main types of impacts, including *Human, Infrastructure and Service Access*, and *Economy and Culture*. We expect this database to help the modelling and investigation of disasters and their risks, by providing new data on natural hazards and their impacts in regions which suffer from under-reporting in other conventional databases.

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# Methods

The extraction and processing of text data from IFRC reports followed four main steps (Figure 1). First, we extract the reports and convert them to text format. Second, we extract and clean the text to prepare it for automatic extraction (Subsection Preprocessing). Third, we prompt an LLM to identify and extract impact data from the cleaned text (Subsection Data extraction). Finally, during post-processing (Subsection Post-processing), we standardise the extracted outputs, remove inconsistent elements, and geolocate the reported locations (Subsection Geocoding). Additionally, for technical validation (Subsection Technical Validation), we compare the extracted information with manually labelled data and other established impact databases.

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<figure><img src="image_2.png" /></figure>

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<caption>Figure 1: Flowchart of the impact extraction pipeline.</caption>
139 Data

140 We use operational reports from the Disaster Response Emergency Fund (DREF) and the
141 Emergency Appeal Fund of the IFRC as source data to extract the impacts of natural
142 hazards. The reports are issued by National Societies of the IFRC in cases of humanitarian
143 crises or disasters, covering small-scale crises (relatively narrow geographical scope and a
144 relatively low number of affected people) to large-scale disasters (a large area of a country
145 or countries is concerned and a very large number of people are affected)¹⁶. These reports
describe the extent of the crisis or disaster, including the area and populations affected, as
146 well as the drivers that lead to the emergency situation (including natural hazards,
147 epidemics, and conflicts). The reports also include detailed descriptions of the impacts on
148 the population, the infrastructure and the environment, as well as the planned response
149 measures for disaster relief. The IFRC identifies events using *appealCode* that uniquely
150 identify each crisis. For each *appealCode*, the IFRC can issue several reports that provide
151 updates on the operation's status, including updates and a mandatory final closing report
152 when the emergency appeal has been terminated¹⁷.
153

154 In order to avoid extracting duplicate impacts, we select a single report per event from the
155 multiple reports issued. Specifically, we keep the longest available report for each event to
156 minimise information loss, as later updates or final reports may contain more summarised
157 versions of the original details. Furthermore, we only consider reports from April 2016 to
158 ensure structural consistency throughout the reports.

159 Next, we select reports for which the associated disaster type in the IFRC metadata
160 corresponds to a natural hazard from the following list: Drought, Flood, Glacial lake outburst,
161 Cyclone, Hurricane, Typhoon, Storm, Tornado, Heatwave, Coldwave, Mass movement,
162 Earthquake, Volcano, Tidal Wave, Wildfire. As an additional check, we verify whether any of
163 these hazard terms are mentioned in the report title.

164 We scrape the reports from the IFRC website¹⁴ and download them as PDFs. The text is
165 extracted using the PyPDF2 Python package¹⁸. All images are discarded, and only text,
166 including tables, is retained. This results in 717 reports, describing unique disaster events in
167 141 countries from April 2016 to July 2025.

168

169 For technical validation, we used the EMDAT¹ and IFRC Go¹⁴,¹⁹ impact datasets. EM-DAT is
a widely used impact data provided by the Centre for Research on the Epidemiology of
170 Disasters in open access¹. It contains impact information from more than 27000 events
171 worldwide from 1900 to the present day. The dataset gathers information from various
172 sources such as UN agencies, non-governmental organisations, reinsurance companies,
173 research institutes, and press agencies. It targets only major events that have resulted in at
174
least 10 deaths and/or 100 affected people and/or were declared as an emergency by the state and/or were associated with an international assistance call. Precise information about the location impacted can be provided, but the impact is only given at the country level. To support systematic impact tracking, the IFRC has developed several initiatives aimed at collecting and structuring disaster impact information, such as the IFRC GO. This database compiles hazard events and associated impact data from IFRC DREF reports. It provides structured impact information for 1476 events recorded between 1997 and 2025. While this dataset offers valuable opportunities for impact comparison, its scope is limited to a narrow set of human and monetary impact categories.

## Preprocessing

In order to ensure machine-readability and facilitate the use of LLMs, we conduct several preprocessing steps on the raw text from the reports using the Spacy python library²⁰. First, we identify the language of each report using a language detection pipeline²¹. Results show that only four reports are not written in English. These reports are discarded to avoid needing to adapt our extraction pipeline to other languages. Next, we remove unwanted text and characters such as URLs, ligatured characters, line breaks and multiple spaces. Based on the observed repeated structure of the reports (see supplementary material), we extract only the sections of the report that contain information about the hazard and its impacts. This limits the text we process, from an average of 244 sentences per report before selection down to an average of 78 sentences per report after selection, making extraction faster and more focused.

## Impact data typology and database structure

We define a socio-economic impact typology comprising 3 impact types and 20 subtypes as detailed in Table 1. The typology was defined using a combination of inductive and deductive approaches, incorporating results from a data-driven topic analysis of the reports and partly adapting the impact types and subtypes of the EM-DAT and DESInventar frameworks. Besides the impacts, we also extract information such as the hazard type, location, and dates. The hazard typology has been adapted from EM-DAT hazard typology¹ and is described in Table 2.

<table><thead><tr><td>Impact Maintype</td><td>Impact Subtype</td><td>Description</td></tr></thead><tbody><tr><td>Human</td><td>Affected People</td><td>Total number of individuals impacted by the hazard event.</td></tr></tbody></table>
<table><tr><td></td><td>Injured People</td><td>Number of people injured, including those hospitalised or admitted.</td></tr><tr><td></td><td>Displaced People</td><td>Number of people forcefully displaced or evacuated before or following the event.</td></tr><tr><td></td><td>Homeless People</td><td>Number of people losing housing following the event.</td></tr><tr><td></td><td>Missing People</td><td>Number of people unaccounted for following the event.</td></tr><tr><td></td><td>Human Deaths</td><td>Number of fatalities caused by the hazard.</td></tr><tr><td></td><td>Infected and Ill People</td><td>Number of people contaminated (cases) by an infectious disease.</td></tr><tr><td></td><td>Human Health and Wellbeing</td><td>Generic impacts on human health (physical, mental) or wellbeing not necessarily associated with the spread of an infectious disease.</td></tr><tr><td rowspan="10">Infrastructure and Service access</td><td rowspan="2">Transportation</td><td>- Roads and transportation infrastructure (e.g. bridges, highways..) impacted by a hazard.</td></tr><tr><td>- People losing the capacity to move safely and efficiently between locations.</td></tr><tr><td rowspan="2">Water, Sanitation, and Hygiene</td><td>- Number of water, sanitation, and hygiene infrastructure such as sewage networks, drainage systems, wastewater treatment plants, etc. impacted by a hazard.</td></tr><tr><td>- People losing safe, clean, and consistent supply of water for drinking, sanitation, hygiene services and other essential uses.</td></tr><tr><td rowspan="2">Healthcare</td><td>- Number of healthcare infrastructure such as hospitals, healthcare centers, pharmacies, clinics, etc. impacted by a hazard.</td></tr><tr><td>- People losing the ability to obtain needed medical services, including preventative care, emergency services ...</td></tr><tr><td rowspan="2">IT and Communication</td><td>- Number of IT and communication infrastructure such as data centers, communication towers, and cables impacted by a hazard.</td></tr><tr><td>- People losing communication access</td></tr><tr><td>Residential Buildings</td><td>- Number of residential buildings (e.g. houses) impacted by a hazard.</td></tr><tr><td>Informal settlements</td><td>- Number of informal settlements such as refugee camps, slums, tents, etc. impacted by a hazard.</td></tr><tr><td>Education</td><td>- Number of education infrastructures such as schools, universities, etc. impacted by a hazard.</td></tr><tr><td>Power and Energy Production</td><td>- People losing access to electricity and services related to power production</td></tr><tr><td>Agriculture and Access to Food</td><td>- Number of agricultural infrastructures such as farms, warehouses, greenhouses, fisheries, etc. impacted by a hazard.</td></tr><tr><td></td><td></td><td>- Number of crops, agricultural production and forest impacted by a hazard</td></tr><tr><td></td><td></td><td>- Total number of animals, including terrestrial and aquatic species,</td></tr></table>
<table><caption>Table 1. Impact types and description</caption><tr><td></td><td></td><td>impacted by the hazard. (e.g. perished animals or fishes)<br/>- People losing access to a secure food supply</td></tr><tr><td></td><td>Undefined<br/>Infrastructure and<br/>Service Access</td><td>- Any identified impact on infrastructure where the type of infrastructure impacted is not clearly defined, e.g. critical infrastructure, public infrastructure, access to basic services</td></tr><tr><td rowspan="2">Economy & Culture</td><td>Recreation,<br/>Tourism, and<br/>Culture</td><td>- Tourist attractions and cultural sites impacted by a hazard.</td></tr><tr><td>Economic and<br/>Livelihood</td><td>- Any identified impact on the economy or living conditions resulting from a natural hazard.</td></tr></table>

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<table><caption>Table 2. Hazard types and description</caption><thead><tr><td>Hazard</td><td>Description</td></tr></thead><tbody><tr><td>Drought</td><td>Prolonged lack of precipitation</td></tr><tr><td>Wildfire</td><td>Uncontrolled natural fires</td></tr><tr><td>Earthquake</td><td>Sudden tectonic shifting. Includes as well tsunami</td></tr><tr><td>Mass movement</td><td>Any type of downslope movement of earth materials. Includes Landslides and rockfalls</td></tr><tr><td>Volcanic activity</td><td>Eruptions and related phenomena</td></tr><tr><td>Flood</td><td>River, coastal, flash and ice jam flooding</td></tr><tr><td>Wave action</td><td>Wind-generated surface waves (e.g. Rogue waves, seiche)</td></tr><tr><td>Extreme warm temperature</td><td>Prolonged, abnormally high heat</td></tr><tr><td>Extreme cold temperature</td><td>Prolonged, abnormally low cold</td></tr><tr><td>Tropical storm</td><td>Tropical cyclonic storms (e.g. tropical cyclone, tropical storm, hurricanes, typhoons)</td></tr><tr><td>Convective storm</td><td>Convective-related storms (e.g. severe convective storms, derechos, hail, tornado)</td></tr><tr><td>Other storm</td><td>Any type of storm which does not correspond to a tropical cyclone or convective storm (e.g. extra-tropical storm, snow storm...)</td></tr><tr><td>Epidemic</td><td>Sudden outbreak of disease</td></tr><tr><td>Conflicts</td><td>Disagreements or disputes between different groups, organizations, or states</td></tr></tbody></table>
208 Impact data extraction

209 We select the model meta-llama/llama-4-scout-17b-16e-instruct <sup>22</sup>, to identify and extract
210 natural hazard impacts, as it is open-source and offers the best performance-to-price ratio
211 compared to other models tested (in supplementary material). We use the GROQ cloud
212 platform<sup>23</sup> to run the extraction.

213

214 The extraction of the impact data is done using a sequence of prompts<sup>24</sup>. For each report,
215 we prompt the LLM with a series of queries. This allows us to better guide the extraction by
216 providing the LLM with more precise information at each extraction step, to have better
217 control over the outputs and to limit the length of the output context windows. The extraction
218 sequence is designed as follows:

219 Identify subtypes of impacts from the text, given a pre-defined list of impact subtypes and
220 their definitions. The output is the list of identified subtypes.

221 For each identified subtype of impacts, extract the values and units of those impacts.

222 Extracting all impacts at once prevents the reuse of the same values for different
223 identified subtypes. The output is a list of dictionaries.

224 For each identified impact (defined as a subtype, a value, and a unit), identify
225 the affected locations. We allow a single impact to have multiple affected
226 locations to account for aggregated reports of impacts. The output is a
227 dictionary.

228 For each identified impact (defined as a subtype, a value, a unit, and a
229 location), identify the starting and ending dates. The output is a
230 dictionary.

231 For each identified impact (defined as a subtype, a value, a
232 unit, a location, and a date), identify the hazards driving the
233 impacts. We allow for multiple hazards to account for
234 compound events.

235

236 At each step, we use the pydantic library<sup>25</sup> to validate the output. This validation allows us to
237 control that each desired field is (i) present in the extracted json, (ii) of the correct data type
238 e.g. string, integer and (iii) contains only allowed values when value restrictions apply.

239 The validation on the value constraints is only applied to the hazards field, to ensure that
240 only hazards matching the hazards lists are extracted. The validation of value constraints for
241 the impactSubtype field was disabled to avoid the LLM relabelling unwanted data. If
242 validation fails, we reprompt the LLM once with the validation error so it can correct the
243 invalid output. Furthermore, we purposely extract unwanted "control" subtypes, such as the
244 money raised by the appeal or the number of assisted people, as this prevents these figures
245 from being wrongly extracted and classified as other similar impact subtypes, such as
246 *Economy and Livelihood or Affected People*. These control subtypes are later discarded
247 from the database.

248 To ensure traceability and for validation purposes, we also query the LLM to obtain the exact
249 text excerpt it used for data extraction at each step. The extracted data is stored in a CSV
250 file with the structure defined in the Data Descriptor section.

251

252 Post-processing

253 We conduct the following post-processing steps. The *impactValue* is evaluated to ensure
254 that it falls within the defined bounds, such that
255 `0` ≤ `0` ≤ `0`. We reclassify
256 and standardise the impact subtypes and hazard types using regular expressions. We
257 format the *impactUnit* with the following four sub-steps; (i) we detect numbers from the unit
258 field and move them to the *impactValue* field (e.g. *10 thousand people* becomes *1'000
259 people*), (ii) we convert monetary units to EUR using the price parser²⁶ and currency
260 converter²⁷ packages, (iii) we then standardise metric units (i.e. distance, mass, surface,
261 volume) to SI units, and (iv) we standardise “measured” units using regular expressions and
262 pre-defined conversion factors (e.g. *4 households* becomes *12 people*, *2 maternities*
263 becomes *2 healthcare structures*).

264 Finally, we perform the following final cleaning steps. We (i) remove duplicated rows, (ii) filter
265 out unknown impact subtypes and the control subtypes introduced during the extraction, and
266 (iii) filter out unknown impact units that could not be standardised in the previous post-
267 processing steps.

268 Geocoding

269 We convert textual location descriptions into georeferenced polygons. This step is done not
270 only to check the correctness of the location identified by the LLM but also to enrich it with
271 geographic boundaries.

272 For administrative boundaries, we rely on the *geoBoundaries* dataset²⁸, which compiles
273 information from governmental and open-license sources. It provides Polygons or Multi-
274 Polygons geometries for all countries, with administrative levels available up to admin 5.
Since administrative levels beyond 2 (e.g. subdistricts, villages, neighbourhoods) are not consistently available across all countries, we restrict our analysis to level 2.

The textual locations are first converted to geographic information using the OpenStreetMap (OSM) Nominatim tool²⁹. This service returns an OSM address with a geometry in the form of a Point, Polygon, or MultiPolygon object. An OSM address may include multiple address details (also known as *address keys* ³⁰), such as city, suburb, hamlet, street, and district.

In order to convert textual location strings to polygons we first compute the Levenshtein similarity³¹ between the input location string and all OSM address keys, with and without administrative descriptors and across rotated word orders. If a perfect match (1.0) is found, the search stops; otherwise, we keep the address key with the highest similarity above a 0.2 threshold, which identifies the most likely location name and administrative level, correcting spelling variations introduced by the LLM. The corrected name is then matched to the corresponding *geoBoundaries* polygon at the identified administrative level.

If matching with a polygon fails, we apply several fallback strategies. First, the location name may differ from the official *geoBoundaries* designation, so we iterate over other OSM address keys at the same administrative level and attempt to match each to a polygon. If this fails, we translate the location name into the country's official languages and widely-spoken languages (French, Spanish, German) and retry the match. If no textual match is found, we use the geographic object returned by OSM and search for a corresponding *geoBoundaries* polygon by testing for intersections at the identified administrative level. If all attempts fail, we repeat the procedure at progressively coarser administrative levels until a match is found.

This process yields a polygon for each textual location. Because reported impacts may reference multiple locations at different administrative levels, the polygons of each resolution identified are kept in the final geometry.

For each post-processing step, we introduce quality flags to ensure traceability and data quality and allow users to flexibly select or filter out data according to their needs. The flags and their descriptions are detailed in the database structure table (Table 3).

<table><thead><tr><td>Variable</td><td>Format</td><td>Description</td></tr></thead><tbody><tr><td>impactType</td><td>str</td><td>Impact type from predefined list</td></tr><tr><td>impactSubtype</td><td>str</td><td>Impact subtype from predefined list</td></tr><tr><td>impactValue</td><td>float or None</td><td>Numerical value quantifying the impact</td></tr><tr><td>impactValueMin</td><td>float or None</td><td>Lower bound estimate of the impactValue</td></tr></tbody></table>
<table><tr><td>impactValueMax</td><td>float or None</td><td>Upper bound estimate of the impactValue</td></tr><tr><td>impactUnit</td><td>str</td><td>Unit of impactValue</td></tr><tr><td>startYear</td><td>int or None</td><td>Start year of impact</td></tr><tr><td>startMonth</td><td>int or None</td><td>Start month of impact</td></tr><tr><td>startDay</td><td>int or None</td><td>Start day of impact</td></tr><tr><td>endYear</td><td>int or None</td><td>End year of impact</td></tr><tr><td>endMonth</td><td>int or None</td><td>End month of impact</td></tr><tr><td>endDay</td><td>int or None</td><td>End day of impact</td></tr><tr><td>hazards</td><td>list</td><td>List of hazards associated with the described impact</td></tr><tr><td>location</td><td>list</td><td>List of locations associated with the described impact</td></tr><tr><td>locationLowestAdmin</td><td>str</td><td>Lowest administrative level identified with the geocoding</td></tr><tr><td>locationPolygon</td><td>str</td><td>Name of the location polygon identified with the geocoding</td></tr><tr><td>geometry</td><td>Polygon or MultiPolygon</td><td>MultiPolygon of the locations identified with the geocoding</td></tr><tr><td>country_iso3</td><td>list</td><td>List of iso3 codes of the country identified with the geocoding</td></tr><tr><td>continent</td><td>list</td><td>List of continents identified with the geocoding</td></tr><tr><td colspan="3"><strong>Flags :</strong></td></tr><tr><td>impactPrecision</td><td>str</td><td>Flag describing whether the impactValue is exact or approximate</td></tr><tr><td>valid_errors_impactValue</td><td>int</td><td>Validation error impactValue extraction. Count of validation errors during the extraction of the impactValue.</td></tr><tr><td>valid_errorsLoc</td><td>int</td><td>Validation error location extraction. Count of validation errors during the extraction of the location.</td></tr><tr><td>valid_errors_dates</td><td>int</td><td>Validation error dates extraction. Count of validation errors during the extraction of the dates.</td></tr><tr><td>valid_errors_haz</td><td>int</td><td>Validation error hazards extraction. Count of validation errors during the extraction of the hazards.</td></tr><tr><td>flag_value_not_in_text</td><td>bool</td><td>ImpactValue should be in the original text. True if impactValue cannot be found in original text</td></tr><tr><td>flag_pop_cntry</td><td>bool</td><td>ImpactValue with "people" unit should be smaller than country's population. True if impactValue with people unit is higher than country population</td></tr><tr><td>flag_impactSubtype_reclass</td><td>bool</td><td>Inferred impactSubtype must be in the allowed list. True if extracted impactSubtype was not in the allowed list and has been reclassified via regular expression</td></tr><tr><td>flag_hazards_reclass</td><td>bool</td><td>Inferred hazardType must be in the allowed list. True if extracted hazardType was not in allowed list and has been reclassified via regular expression</td></tr><tr><td>flag_unit_conversion</td><td>bool</td><td>Unit conversion. True if a non-metric unit has been converted. These include families -> 3*people, households -> 3*people, communities -> 100*people, villages -> 1000*people</td></tr><tr><td>flag_non-SI_unit_standardization</td><td>bool</td><td>Standardization of non-metric unit. True if a unit has been reclassified using regular expression, e.g. schools -></td></tr></table>
<table><tr><td></td><td></td><td>education structures, bridges -&gt; roads</td></tr><tr><td>flag_SI_unit_standardization</td><td>bool</td><td>Standardization of metric unit to SI. True if the extracted impactUnit has been standardized to a SI unit.</td></tr><tr><td>flag_remove_number_unit</td><td>bool</td><td>True if a number has been identified from the extracted impactUnit and has been moved to the impactValue from the impactUnit (e.g. impactValue=10, impactUnit="million people" -&gt; impactValue=1000000, impactUnit="people")</td></tr><tr><td>flag currenciesConversion</td><td>bool</td><td>True if a monetary unit has been identified but has been failed to standardized to the default currency</td></tr><tr><td>flag_unit_processing_error</td><td>bool</td><td>True if there has been an issue in any of the following stages of the postprocessing of the impact unit: remove_number_unit, unitConversion, SI_unit_standardization, non-SI_unit_standardization, currencyConversion.</td></tr><tr><td>flag_unit_nonstd</td><td>bool</td><td>True if the extracted impactUnit is not part of the "standard units"</td></tr><tr><td>flag_reclass_subtype_from_unit</td><td>bool</td><td>True if the extracted impactSubtype has been reclassified based on the impactUnit</td></tr><tr><td>flag_value_no_unit</td><td>bool</td><td>True if an impactValue has no unit</td></tr><tr><td>flag_partial_unit</td><td>bool</td><td>True if the impactUnit lacks the measured unit (e.g. "hectares" instead of "hectares of crops")</td></tr><tr><td>flag_geocoding_osm</td><td>int</td><td>Number of locations for which osm point/polygon has been used to retrieve the GAUL/NE polygon, with spatial matching</td></tr><tr><td>flag_geocoding_country</td><td>int</td><td>Number of locations downgraded to the country to find a polygon</td></tr><tr><td colspan="3"><strong>Metadata :</strong></td></tr><tr><td>appealCode</td><td>str</td><td>Appeal code of IFRC report</td></tr><tr><td>reportDate</td><td>str</td><td>Date of the report in YYYY/MM/DD format</td></tr><tr><td>reportLink</td><td>str</td><td>Url link of the pdf report</td></tr><tr><td>disasterType_IFRC</td><td>str</td><td>Disaster type identified by the IFRC</td></tr><tr><td>sourceExcerpts</td><td>str</td><td>Text excerpts used for the information extraction</td></tr></table>

303 Table 3. Structure of the impact database

304 Data Records

305 We extract a database with the structure described in Table 3. Each row of the database

306 represents one impact record, specified by an impact subtype, a value, a unit, a location
307 (geometry), a date, and a driver (associated hazards). Each impact record is also
308 complemented with metadata (Table 3), informing on the source (i.e. the appeal code
309 uniquely identifying an IFRC appeal, the report date, the sentences that were used for the
310 extraction of the impacts), the precision of the impact value, and several quality flags, which
311 track issues and possible inconsistencies. The data will be made accessible via Zenodo
312 under the Creative Commons Attribution 4.0 International license. The database is available
313 in two formats: as a global csv file without geometries and as Parquet files with geometries,
314 available globally and split by continents. A notebook detailing how to open and access the
315 data is available in the code archive, which can be downloaded anonymously by the
316 reviewers at https://figshare.com/s/99da5ff800fc56ae10c0. The data can be downloaded
317 anonymously by reviewers at the same link.

318

Data Overview

319 The LLM-based impact dataset is created from IFRC reports and primarily captures impact
320 in the Global South, as illustrated in Figure 2. The regions most affected are Africa and Asia,
321 with 5760 and 4525 impacts reports (Figure 2), respectively, covering 707 and 796 different
322 natural hazards (Figure 3). Figure 3 shows that floods, along with tropical and convective
323 storms, are the hazards most frequently associated with the extracted impacts. Additionally,
324 mass movements and epidemics also contribute substantially to the overall recorded
325 impacts. 54% of the impacts extracted are geocoded at the country level (ADMIN0), 36% at the
326 first-order administrative division (ADMIN1), and 10% at the second-order administrative division
327 (ADMIN2).
<figure><img src="image_3.png" /><figcaption>Figure 2: Upper panel (a) shows the spatial distribution of the number of reported impacts per country in colour. Circular bar plots show the share of *impactType* per continent, with the inner numbers indicating the total number of impacts. The Lower panel (b) is the histogram of the number of impacts in each *impactSubtype* category. Colours correspond to the *impactType*.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: Share of hazards (in %) to which impacts are associated, for each continent. Colours are ordered according to the type of hazard.</figcaption></figure>
We validate our database internally by comparing LLM-extracted impacts with manually extracted impacts from 17 randomly selected reports (listed in the supplementary). Manual extraction followed strict, pre-defined guidelines (see supplementary *Labelling Guidelines*), and was performed by the authors with cross-reviewing. The labelled data acts as a control and represents the reference standard to which the LLM-extracted data is expected to adhere. From the 17 reports selected, we manually labelled 361 quantitative and 169 qualitative impacts, spanning over 20 impact subtypes and 13 hazards. In comparison, the automatic extraction using the LLM extracted 243 quantitative and 131 qualitative impacts, spanning over 20 impact subtypes and 9 hazards.

Comparing the LLM-extracted with the manually-labelled impacts allows us to derive similarity scores for the different variables extracted, as well as the *precision* and *recall* of the extraction. The similarity scores are defined as a function of the type of the examined variable and range from 0 for perfect dissimilarity to 1 for perfect similarity. Similarity of categorical variables is expressed using cosine similarity³², similarity of numerical variables (*impactValue*) with a normalised relative difference (equation 2 in the supplementary *Matching algorithm for internal validation*), and geometries with the Intersection-over-Union (equation 1 in the supplementary *Matching algorithm for internal validation*). Precision and recall represent, respectively, the percentage of correctly extracted data over the total extracted and the percentage of correctly extracted data over the total manually-labelled.

In order to compute the similarity scores for each extracted impact, we use an algorithm that matches each LLM-extracted row to the most similar row in the manually-labelled data. The matching procedure is described in the supplementary *Matching algorithm for internal validation*. Figure 4 shows the average similarity per matching variables, separated by the quantitative and qualitative extraction. The *match similarity* is the weighted mean of the variables' similarities for the matched data. We explain the lower match similarity in the qualitative data by the lower similarity of the impact subtypes, with some qualitative impact subtypes being more abstract, which may cause the LLM to perform worse at classifying the data into the correct subtypes. The similarity scores for the remaining variables are generally high, with scores above 80% for the impact unit, the iso3 code of the countries, the start year, and the impact values. The similarity of the dates is lower (around 50%). We attribute
this lower performance to the complexity of clearly identifying event dates, which the human labellers encountered during manual labelling.

<figure><img src="image_5.png" /><figcaption>Figure 4: Similarity score per matching variable. The horizontal bars show the similarity score (%) for each of the variables considered for the matching of extracted and manually-labelled impacts. The bottom bar plot shows the average for all matches of the weighted mean of the similarity scores for all matching variables. Colours show results for the quantitative vs qualitative impacts.</figcaption></figure>

Next, we compute the *Precision* and *Recall* using the matched data to estimate the predictive performance of our extraction:

$$ \square \square \square \square \square \square \square = \frac{\square \square}{\square \square + \square \square} \quad (1) $$

$$ \square \square \square \square \square = \frac{\square \square}{\square \square + \square \square} \quad (2) $$

where *TP* represents the True Positives, i.e. the number of LLM-extracted impacts for which we find a corresponding manually-extracted impacts, *FP* the False Positives, i.e. the number of LLM-extracted impacts for which we do not find a corresponding manually-extracted impacts, and *FN* the False Negatives, i.e. the number of manually-extracted impacts for which we did not find a corresponding LLM-extracted impacts. A perfect association should result in a *precision* and a *recall* score of 1. Lower *precision* corresponds to a higher
388 tendency of the LLM to hallucinate, while lower *recall* indicates that the LLM is missing a
389 larger portion of the true impacts.

390 To quantify the sampling uncertainty, we approximate the 95% confidence intervals of the
391 precision and recall metrics using a bootstrapping approach. For each sample from which
392 the metrics are estimated, we sample with replacement 5000 random samples of true
393 positives, false positives, and false negatives. We then compute the metrics from each
394 random sample to obtain the bootstrapped distribution of the metrics. Finally, we use the
395 2.5th and 97.5th quantiles of the bootstrapped distribution as the bounds of the confidence
396 interval.

397 Figure 5a shows *precision* and *recall*, and their estimated 95% confidence intervals for the
398 total number of quantitative and qualitative impacts extracted. We find a precision of 0.71
399 and 0.62 for the quantitative and qualitative data, respectively. These results suggest that
400 quantitative data can be extracted more precisely than qualitative data. This could indicate
401 that the model is better at extracting numbers than at identifying more abstract entities, such
402 as qualitative descriptions of impacts. Regarding recall, we obtain 0.37 for the quantitative
403 data and 0.52 for the qualitative data. This suggests that only one third of the quantitative
404 impacts that are described in the reports can be extracted with our approach. The extraction
405 of qualitative impacts performs slightly better. We explain this by the higher number of
406 quantitative (18 on average per report) than qualitative (10 on average per report) impacts
407 present in the reports.

408 Figure 5b shows the precision and recall metrics per impact subtype. The 95% confidence
409 intervals indicate that sampling uncertainty is high due to the smaller sample sizes when
grouping by impact subtype. For 7 of the 20 impact subtypes (*Infected and Ill People*,
410 *Homeless People*, *Missing People*, *Power and Energy Production*, *Recreation*, *Tourism*, and
411 *Culture*, *IT and Communication*, and *Undefined Infrastructure and Service Access*), the
412 sampling uncertainty is too large to exclude the precision and recall to be 0. For the
413 remaining subtypes, their performance does not differ significantly from the average
414 precision and recall.
415
<figure><img src="image_6.png" /><figcaption>Figure 5: Precision and recall per quantitative vs. qualitative extraction (a), and per impact subtypes (b). Colorbars represent the value of the metrics estimated from the samples directly. Red error bars represent the 95% confidence interval estimated via bootstrapping.</figcaption></figure>

In addition, we ran robustness checks to assess whether our approach is sensitive to the choice of the LLM used and if it is deterministic (see supplementary Sensitivity analyses).

## Comparison with external data (external validation)

We perform external validation by comparing quantitative impacts extracted by the LLM at the national level to EMDAT and IFRC-Go datasets. From our *impactSubtypes* (Table 1) we can compare the *Affected People*, *Human Death* and *Homeless People* categories with EMDAT and the *Affected people*, *Injured people*, *Displaced People*, *Human Deaths* and *Missing People* with IFRC-Go. As EM-DAT uses the IFRC to source their records of the number of people affected in 65% of cases ¹, we expect this category to match well across the three data sources.
From the LLM-extracted data, impacts are aggregated at the ADMIN0 level by retaining, for each record, the highest impact value reported at the coarsest administrative level. Square symbols in Figure 6 illustrate the percentage of events for which a quantitative impact was identified across the previously mentioned common impact subtypes. The three databases show differing proportions of events with recorded quantitative impacts. Counts of *Affected People* and *Human Deaths* are the most frequently reported, with EM-DAT consistently exhibiting the highest percentage of reports containing quantitative impact values. For the *Injured People*, *Displaced People*, and *Missing People* subtypes, IFRC Go reports quantitative impacts for approximately 40% of events on average. In contrast, the corresponding proportions derived from LLM-based extraction are lower, dropping to 24% for *Injured People* and 18% for *Missing People*. Overall, this indicates that the LLM captures quantitative impacts more effectively for widely reported subtypes such as *Affected People* and *Human Deaths*, while it appears to struggle with less consistently documented subtypes, which are also under-documented in EM-DAT and IFRC-Go, including *Injured People*, *Displaced People*, and *Missing People*.

We then match the reports containing quantitative information to the external datasets in order to compare the values of the reported impacts. Because EM-DAT and IFRC do not share a common unique event identifier, we match events using a stepwise procedure. We first link each IFRC report to EM-DAT events occurring in the same year and country. When multiple hazards are listed in an IFRC report, we select the hazard associated with the greatest number of reported impacts and retain only the EM-DAT events whose *DisasterType* corresponds to this dominant hazard. If multiple EM-DAT events still remain after this filtering, we select the event whose entry date is closest to the IFRC report date. This systematic procedure associates an event from EMDAT to each of the 594 events for which we extract quantitative information with the LLM. For IFRC-Go, matching can be done directly using the appeal code. We extract 566 events from the IFRC-Go data API over the period 2016-2025, which are associated with natural disasters. We successfully link 383 of these events with events which we extracted.

Bar plots from Figure 6 show the percentage of events from our database that we can match to the two external databases. The colours of the bars represent the percentage of matching against one of the two external databases. The brightness of the colours represent the percentage of matches for which we find (i) no impact value, (ii) an impact value differing from the LLM-extracted value by more than 10% and (iii) an impact value differing from the LLM-extracted value by less than 10% (see section *Comparison with manually labelled data*). We do not find a match within the IFRC-Go database for approximately 40% of the
466 LLM-extracted events. This implies that our database retrieved impacts for appealCode
467 which are not available within the IFRC-Go data API. Over the events matched, we do not
468 find an impact record for the different impact subtypes considered in about 30% of the cases
469 for both the IFRC-Go and EM-DAT. This suggests that, for the events considered, the LLM-
470 based extraction is more robust in automatically detecting impacts for the considered impact
471 subtypes. However, the degree of agreement between the impact values we extract and the
472 external databases remains variable across the different impact subtypes. Overall, we
473 observe a higher percentage of matched events with quantitative impact figures in EM-DAT
474 compared to IFRC-Go, which is inline with the higher percentage of total events with impacts
475 present in EM-DAT (square symbols). Interestingly, exact matches between LLM-extracted
476 values and EM-DAT occur more often than between LLM-extracted values and IFRC-Go. On
477 average across the different subtypes, we find exact value matches in less than 10% of the
478 cases with IFRC-Go, and less than 30% of the cases with EM-DAT. Possible explanations
479 include differences in the specific report versions used in the case of IFRC-Go, limitation of
480 the matching procedure for EM-DAT or inaccuracies in the extraction process in either the
481 LLM, IFRC-Go or EM-DAT databases. Nonetheless, these discrepancies across sources
482 and impact categories further highlight the inherent challenges in estimating quantitative
483 impact information from natural disasters.

<figure><img src="image_7.png" /><figcaption>Figure 6: Percentage of events from IFRC Go (grey) and EM-DAT (blue) that could be matched to an LLM-extracted event for the impact subtypes "Affected People", "Injured People", "Displaced People", "Human Deaths", "Missing People", and "Homeless People". Among the matched events, darker shades indicate cases where the quantitative impact value exactly matches the LLM extraction, mid-</figcaption></figure>
489 tones indicate a match with a different reported value, and lighter shades show matched events for
490 which no quantitative impact is recorded in IFRC Go or EM-DAT. The red horizontal line marks the
491 100% level corresponding to the total number of LLM events with reported quantitative impacts. Grey,
492 blue, and red squares summarise, for each impact subtype, the proportion of all events (not only
493 matched ones) with quantitative impacts reported in IFRC Go, EM-DAT, and the LLM database,
494 respectively.

495

496

## Data Availability

497 The data will be made accessible via Figshare in csv format without the geometries and in
498 Parquet format with the geometries, under the *Creative Commons Attribution 4.0*
499 International license. The data can be downloaded anonymously by reviewers at:
500 https://figshare.com/s/99da5ff800fc56ae10c0

501

502

## Code Availability

503 The code to produce the database, perform the technical validation and make the analysis
504 plots is available anonymously at: https://figshare.com/s/99da5ff800fc56ae10c0.

505

## Author Contributions

506 LH: Conceptualisation, Methodology, Software, Validation, Formal analysis, Data Curation,
507 Writing - Original Draft; Visualisation
508 L.S.: Conceptualisation, Methodology, Software, Validation, Formal analysis, Data Curation,
509 Writing - Original Draft; Visualisation
510 MMdB: Conceptualisation, Writing - Review & Editing;
511 GCG: Conceptualisation, Data Curation, Writing - Review & Editing
512 AMR: Conceptualisation, Data Curation, Writing - Review & Editing
513 DNB: Supervision, Funding acquisition, Writing - Review & Editing
514 EM: Supervision, Writing - Review & Editing
515 JW: Conceptualisation
516 TMNC: Conceptualisation, Data Curation, Supervision, Writing - Review & Editing.
517 Competing Interests

518 The authors declare no competing interests.

519 Acknowledgements

520 The authors thank Pascal Yiou (LSCE, UMR 8212CEA-CNRS-UVSQ) for reviewing the
521 manuscript. The authors are grateful to the organisers and participants of the 2024 *Como*
522 *Training School on Compound climate-related Events* for providing the structure and
523 motivation which started this work.

524 Funding

525 This work was supported by the French ANRT, which funded LH's PhD. L.H. is employed by
526 Generali France.

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