Petitpierre, Remi · di Lenardo, Isabella · Hudson, Polly · et al.

4 files · 1.4 GB · geopackage, zipdeclared

The dataset provides a large-scale vector layer of historical building footprints for the Greater London area at the end of the 19th century. It comprises 1,299,029 individual building footprints, extracted automatically from the Ordnance Survey five-feet-to-the-mile (1:1,056) map. The original maps were surveyed between 1891 and 1895 and published between 1893 and 1896, covering approximately 450 km² of urban and suburban London. The dataset was produced using a deep learning-based semantic segmentation pipeline, achieving approximately 97% precision and 95% recall in building detection. Source The source maps were digitised by the National Library of Scotland at 400 ppi and manually georeferenced in collaboration with the David Rumsey Map Collection. The extraction relies on 753 georeferenced map sheets, provided that six sheets are missing from the original archive. Extraction methodology The dataset was generated through an automated workflow: 262 image patches were annotated manually with three classes: (i) regular buildings; (ii) compound buildings; (iii) building boundaries A Mask2Former [1] semantic segmentation model is used. For training and inference, we followed the approach described by [2]. At inference time, boundary predictions are supplemented by the predictions of a specialist model, trained on cadastral plans [3]. Polygon geometries are extracted, based on predicted building contours, and vectorised. Compound buildings components are merged. Content Two versions of the dataset are provided: london_buildings_1891-96_raw.gpkg Raw output from the automated extraction pipeline (after vectorisation and merging) london_buildings_1891-96_corr_v1.gpkg Minimally corrected version including manual adjustments for major structures (e.g. railway stations, monuments, large buildings) Each geometry has a field buil_class , taking values in ['regular','compound'] , relative to the cartographic representation of building classes and the associated processing approach. In corr_v1, manually added geometries take the value NULL . In addition, we relase the labeled images used for training the segmentation model: annotations.zip ZIP folders containing the training and validation data The archive contains subfolders images , containing the tif image samples and labels , corresponding to the semantic class indexes, in png format. labelmap.txt details the class index codes. Data format Format: Geopackage Coordinate reference system: WGS 84 / Pseudo-Mercator (EPSG:3857) Temporal coverage Survey period: 1891-1895 Publication period: 1893-1896 Data creation: 2024-2026 Descriptive statistics Number of building footprints: 1,300,831 (raw), 1,299,040 (corr_v1) Coverage area: 450 km² Detection performance (raw): Precision: 97% Recall: 95% Use and reuse potential This dataset supports research in: urban history historical GIS urban morphology economic and social history It is particularly suited for studying long-term urban change and fine-grained spatial patterns in industrial-era cities. Related publication This dataset is described in a data paper submitted to the Journal of Open Humanities Data Corresponding author Remi Petitpierre Email: remi.petitpierre@epfl.ch Funding The research was supported by the College of Humanities at EPFL and the European Union Horizon Europe Programme (Grant No. 101233051). License This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Citation If you use this dataset, please cite: @misc{london_footprints_petitpierre_2026, author = {Petitpierre, R{\'{e}}mi and di Lenardo, Isabella and Hudson, Polly and Herold Hendrik and McDonough, Katherine and Hecht, Robert and Vaienti, Beatrice and Fleet, Christopher}, title = {{A Layer of Late Victorian London: The Building Footprints from the 1:1,056 Ordnance Survey Map (1891-1896)}}, year = {2026}, publisher = {EPFL}, url = {https://doi.org/10.5281/zenodo.19497434}} Limitations Lower accuracy for very small buildings Occasional segmentation errors in dense or complex areas Minor inconsistencies near map sheet boundaries Liability The authors assume no liability for the use of this dataset. References Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022). Masked-attention Mask Transformer for Universal Image Segmentation . arXiv. https://doi.org/10.48550/arXiv.2112.01527 Petitpierre R. (2026) Generalizable Multiscale Segmentation of Heterogeneous Map Collections . arXiv. https://doi.org/10.48550/arXiv.2603.05037 Petitpierre, R., di Lenardo, I., & Rappo, L. (2024). Revealing the Structure of Land Ownership through the Automatic Vectorisation of Swiss Cadastral Plans . Digital History Switzerland. https://doi.org/10.13140/RG.2.2.26632.33281

Geisler, Jan · Rakhimberdiev, Eldar · Boom, Michiel P. · et al.

29 files · 124 MB · csv, shapefiledeclared

1. Many migratory birds now reach their Arctic breeding grounds earlier in order to keep pace with advancing springs and shifting nutrient peaks, either by departing earlier from non-breeding grounds or by travelling faster. For dark-bellied brent geese, there is limited potential to travel faster, as their migration to the Siberian breeding grounds is already among the fastest of Arctic geese and swans. Earlier departure would require reaching departure body mass earlier, either through a faster accumulation of energy stores during spring staging or via adjustments earlier in the annual cycle. 2. We examined long-term shifts in spring staging phenology and changes in winter and spring body mass trajectories of brent geese at the population level, with particular emphasis on the effects of winter temperature on body mass and spring body mass on departure timing. 3. We used more than five decades of body mass measurements from individuals caught in the United Kingdom and France, and in the Dutch Wadden Sea to reconstruct changes in spring and winter mass trajectories, respectively. These data were combined with over five decades of migration counts in the Netherlands and more than two decades of counts in Denmark to quantify changes in spring staging phenology. 4. We found that brent geese have not shifted their spring arrival in the Wadden Sea but have advanced departure timing. Furthermore, brent geese were heavier during and after milder winters, and have changed mass trajectories over recent decades. They no longer lose mass during winter and the second spring staging phase, and fuelling rates in the first spring staging phase have declined. Annual variation in body mass was not related to annual departure timing. 5. These results suggest that milder winters have relaxed energetic constraints and improved body condition in brent geese throughout the non-breeding season. Our findings highlight the importance of considering the full annual cycle when assessing how animals with limited capacity to adjust migration timing or speed respond to global change.

Marques da Silva, Gabriel · de Freitas Junior, Adirson Maciel · Feitosa, Flávia da Fonseca

2 files · 500 MB · geopackage, xlsxdeclared

Dicionário e Resumo do Banco de Dados Preditor – Recorte São Paulo (SP) Este conjunto de dados contém o recorte territorial do banco de dados preditor consolidado para o município de São Paulo (no formato GeoPackage: 04_final_consolidado_31_completo_sp.gpkg ) e seu respectivo dicionário e resumo de variáveis (no formato Excel: Dicionário Modelo SP.xlsx ). O banco de dados é estruturado em uma grade celular regular de alta resolução com células de 50 metros por 50 metros (50m x 50m) . Cada célula de 2.500 m² representa uma unidade territorial urbana que agrega variáveis morfológicas urbanas, informações socioeconômicas e escores resultantes de um modelo de aprendizado de máquina (Machine Learning) preditor de assentamentos informais/precários. 1. Visão Geral do Recorte de São Paulo (SP) Total de feições (células de análise) : 388.536 Dimensão das células : 50m x 50m (2.500 m² por célula) Área urbana total analisada : Aproximadamente 971,34 km² (composta pela união das células da grade dentro do município) Total de variáveis analisadas : 111 variáveis originais no GeoPackage, sendo 109 colunas ativas com dados (não-nulos) para São Paulo. Qualidade do preenchimento : Excelente cobertura. Das 109 variáveis ativas, 108 apresentam 100% de cobertura espacial para o município de São Paulo. A única variável com preenchimento parcial é a ranking_candidato (com 93,42% de cobertura). 2. Estrutura do Dicionário (Dicionário Modelo SP.xlsx) O arquivo de dicionário e resumo é composto por 5 colunas que descrevem as variáveis do GeoPackage: Variable (Nome da Variável): O nome técnico da coluna na tabela de atributos do GeoPackage (ex: ID , prob_fcu , ibge_mediapopc ). Type (Tipo do Dado): Indica a natureza do dado físico (ex: object , float64 , int16 / int32 , geometry ). Non_Null_Count (Registros Válidos): Quantidade de feições em São Paulo que possuem dados válidos nesta coluna, desconsiderando campos nulos, vazios ou informados como ausentes (como "None" ou "null"). Coverage_Percentage (Taxa de Cobertura): O percentual de feições de São Paulo que possuem a variável preenchida. Sample_Value (Valor de Amostra): Um exemplo real do dado contido na coluna retirado da primeira linha válida encontrada, servindo para referência rápida sobre o formato da informação. 3. Categorias de Variáveis no Banco de Dados As variáveis presentes no banco de dados preditor de São Paulo dividem-se nos seguintes blocos temáticos: A. Variáveis de Localização e Resolução Territorial : Identificam as células geograficamente e associam os recortes às divisões oficiais (ex: ID , id_rg2017_cd_mun , id_rg2017_mun_nome , id_100m , id_200m , id_grid_1km ). B. Métricas de Classificação do Modelo Preditor : Valores resultantes da aplicação do modelo de inteligência artificial sobre o território (ex: prob_fcu , rank_class , ranking_total , ranking_candidato ). C. Variáveis de Importância (Feature Importance) : O modelo registra quais fatores foram os mais determinantes para a classificação de cada polígono específico (ex: top1_feat a top16_feat , top1_score a top16_score , top1_val a top16_val ). D. Variáveis Socioeconômicas e de Infraestrutura (Censo IBGE) : Indicadores estatísticos herdados dos setores censitários do IBGE (ex: ibge_mediapopc , ibge_mediadomc , m_ibge_renddppo , p_ibge_esginade , p_ibge_lixoinade , p_ibge_naocalca , p_ibge_naoilupub ). E. Variáveis Morfológicas Urbanas (GBA) : Métricas físicas de ocupação do solo e edificações (ex: gba_num_edif , gba_edif_por_ha , idade_ocupacao_menor ). 4. Agradecimentos e Financiamento Este estudo foi desenvolvido no âmbito do Centro de Estudos da Favela (CEFAVELA) (projeto FAPESP nº 2022/12259-8). A.M.F.J. agradece à Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) pela bolsa de pós-doutorado vinculada a este projeto (processo 2025/03115-0). G.M.S. agradece à Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) pela bolsa de Treinamento Técnico (TT/FAS) vinculada a este projeto (processo 2025/24201-2). 5. Como Citar (How to Cite) ABNT : SILVA, Gabriel Marques da; FREITAS JUNIOR, Adirson Maciel de; FEITOSA, Flávia da Fonseca. Dicionário e Resumo do Banco de Dados Preditor – Recorte São Paulo (SP) . Zenodo, 2026. DOI: 10.5281/zenodo.21268382. APA : Silva, G. M., Freitas Junior, A. M., & Feitosa, F. F. (2026). Dicionário e Resumo do Banco de Dados Preditor – Recorte São Paulo (SP) . Zenodo. https://doi.org/10.5281/zenodo.21268382 6. Links Relacionados (Related Links) Centro de Estudos da Favela (CEFavela): https://cefavela.ufabc.edu.br/ Projeto Revelando Favelas (Arcabouço Metodológico): https://cefavela.ufabc.edu.br/revelando-favelas-arcabouco-metodologico-para-identificacao-e-caracterizacao-de-favelas/

Kavanagh, Jack · Anthony, Patrick

6 files · 11 MB · geojson, geopackage, shapefiledeclared

A historical map showing a segment of the boundaries of New Spain in c. 1800. This map is fully open to fellow researchers and is available in multiple open source formats (SHP, GPKG, GeoJSON).

Sarma, R N

2 files · 88 MB · csv, vcfdeclared

This dataset contains the genotype and phenotype data generated for a genome-wide association study (GWAS) of agronomic traits in a diverse panel of 105 Indian mungbean ( Vigna radiata L. Wilczek) accessions . The dataset comprises a filtered genome-wide SNP dataset in Variant Call Format (VCF) and the corresponding Best Linear Unbiased Predictor (BLUP) values for the measured agronomic traits.

Kavanagh, Jack · Anthony, Patrick

6 files · 9.3 MB · geojson, geopackage, shapefiledeclared

A historical map of the boundaries of Prussia in c. 1795. This map is fully open to fellow researchers and is available in multiple open source formats (SHP, GPKG, GeoJSON).

Francescone, Marco

14 files · 380 KB · shapefile, zipdeclared

This dataset contains original geomorphic mapping of surface fault traces along the Dixie Valley Fault (DVF) range front and piedmont zone, central Nevada, USA. Traces were mapped directly from a 1-m bare-earth lidar digital elevation model (DEM), using hillshade and slope-raster visualizations. This dataset accompanies the manuscript: Francescone, M., et al. (in review), LiDAR-Based Fault-Scarp Analysis and Rupture Hazard Assessment: Earthquake Scenarios of the Dixie Valley Fault System (Nevada, USA). See Section 3.1 ("Fault Trace Mapping") of the manuscript for full methodological details

Meekes, Lisa · Tabaro, Francesco · Bexkens, Michiel · et al.

41 files · 8.2 GB · csv, fasta, pdfdeclared

This record contains the Python software for PEPTiGEN, a tool for generating tryptic peptides from prokaryotic gene sequences and their variants, and the associated antimicrobial resistance (AMR) peptide database. The database is provided as an SQL file and a CSV file containing all genes and predicted peptides. The README file contains explanation of the PEPTiGEN tool. The SQL database schema files contains both the database schema of the SQL database used in the PEPTiGEN analysis as the database schema of the AMR peptide datbase.

Dey, Hemal · Shao, Wanyun

14 files · 5.5 MB · csv, jpeg, shapefiledeclared

Despite the proliferation of social vulnerability assessment methodologies, selecting the most appropriate model remains a critical challenge due to inter-model variability. To explore the inter-model variability, this study systematically investigated inter-algorithmic and inter-classification variability to assess how methodological design influences outcomes.

Langermann, Florence · Blaschta, Stephanie · Dietze, Klara · et al.

3 files · 731 KB · geopackagedeclared

Beschreibung (Deutsch) Diese Datenpublikation enthält archäologische Geodaten aus den Forschungsprojekten "The Cultic Centre of the Sun-God of Heliopolis (Egypt)" und „Eclipse and Mutation: The end of the sun temple of Heliopolis" zu den ergrabenen und aufgefundenen Befunden im altägyptischen Heiligtum in Heliopolis im heutigen Kairo. Die Befunde wurden innerhalb einer Reihe von Grabungskampagnen zwischen 2012 bis 2024 ergraben und dokumentiert. Diese Datensammlung stellt eine Zusammenstellug dieser Befunde dar und soll als Grundlage für eine Gesamtkartierung dienen. An der Zusammenstellung der Daten waren maßgeblich die drei verantwortlichen Ausgräberi:innen Stephanie Blaschta , Klara Dietze und Florence Langermann beteiligt. Für die Erstellung der Datenstrukturen und Schemata und dem Zusammenführen der Daten war Michael Schleier verantwortlich. Das Projekt wurde von der Deutschen Forschungsgemeinschaft (DFG) sowie weiteren akademischen und privaten Förderinstitutionen unterstützt. Weitere Informationen: Projektwebseite DAI Datendokumentation des i3mainz, Hochschule Mainz Description (English) This data publication contains archaeological geodata from the research projects "The Cultic Center of the Sun-God of Heliopolis (Egypt)" and "Eclipse and Mutation: The end of the sun temple of Heliopolis" on the excavated and discovered features in the ancient Egyptian sanctuary in Heliopolis in present-day Cairo. The features were excavated and documented during a series of excavation campaigns between 2012 and 2024. This data collection represents a compilation of these features and is intended to serve as the basis for an overall mapping. The three excavators responsible, Stephanie Blaschta , Klara Dietze and Florence Langermann , played a key role in compiling the data. Michael Schleier was responsible for creating the data structures and schemas and merging the data. The dataset is published under a CC BY-SA 4.0 license . The project is funded by the German Research Foundation (DFG) and a wide network of institutional and private sponsors. Further information: Project website at DAI Datendokumentation of the i3mainz, Mainz University of Applied Sciences

Cheng, Yuhan · Bai, Lubin · Zhang, Xiuyuan · et al.

15 files · 140 MB · shapefile, zipdeclared

Data used in the paper "Annotation-Efficient Building Footprint Updating via Historical Map Reuse and Iterative Instance Segmentation"

Goedkoop, Willem · Fölster, Jens · Lau, Danny Chun Pong · et al.

16 files · 145 KB · shapefiledeclared

Main scripts for assessing satellite data using rgee. The loops can run slowly, so testing may require using smaller regions.

Mimet, Anne · Gourmemon, Damien · Amandine, Vergondy · et al.

14 files · 1.6 GB · shapefile, tiffdeclared

================================================================================ README ================================================================================ Dataset Title: Observation points, movement-proxy data, and high-resolution land cover map for the common blackbird ( Turdus merula ) in Angers, France Version: 1.0 Authors : Mimet, Anne ; Gourmelon Damien ; Oulhen, Thomas ; Vergondy, Amandine Date of biological data collection: Observation points : 15/05/2025 to 06/06/2025 Movement-proxy data : 19/04/2024 to 17/05/2024 -------------------------------------------------------------------------------- DESCRIPTION -------------------------------------------------------------------------------- This dataset contains observed presence/absence of blackbird flights across streets, as well as point observations of the common blackbird across Angers, France, during spring. The data were used to create a connectivity model for the common blackbird in Angers. The 68 point observations provided information on the land cover types used as a possible resource by the common blackbird. The presence/absence of flying blackbirds across 190 streets in Munich was used to derive the resistance of the urban landscape to the movement of common blackbirds in a landscape connectivity model. For the point observations, the presence and absence of the common blackbird was visually and acoustically confirmed after 5 minutes of observations within a radius of 25 m. Movement presence/absence was observed along 50 m street transects. Street transects were observed for 9 minutes, and the presence or absence of common blackbirds crossing this street was recorded. The observation points and movement-proxy data were selected along gradients of greenness and traffic density. -------------------------------------------------------------------------------- FILE LIST -------------------------------------------------------------------------------- 1. movement_proxy.shp Site-level data containing presence and absence of common blackbirds crossing the sampled streets, as well as covariates such as the number of pedestrians passing during the sampling period, geographic information, information on the weather, time of sampling, pseudonomized observer. Number of records: 190 Number of variables: 11 related files: movement_proxy.cpg, movement_proxy.dbf, movement_proxy.prj, movement_proxy.shp, movement_proxy.shx The related files are required because data is stored in a shapefile. For this shapefile to be correctly read by any GIS processing software, all related files need to be saved in the same folder. 2. point_observations.shp Site-level data containing presence and absence of common blackbirds at the observation points. Additionally, geographic and temporary information as well as information on the weather are provided. Number of records: 68 Number of variables: 10 related files: point_observations.cpg, point_observations.dbf, point_observations.prj, point_observations.shp, point_observations.shx The related files are required because data is stored in a shapefile. For this shapefile to be correctly read by any GIS processing software, all related files need to be saved in the same folder. 3. LULC_9Class_Angers.tif and assoociated qlm style file Land use and land cover map at 40 cm resolution for Angers. 3. README.txt This file. -------------------------------------------------------------------------------- VARIABLE DESCRIPTIONS: movement_proxy.shp -------------------------------------------------------------------------------- StreetCod Site identifier for street transect - these are the same sites as in the file Point_observations.shp (format: [text]) X Northing coordinate of the centre (latitude) (format: [degree]) Y Easting coordinate of the observation point (longitude) (format: [degree]) Obs Id of the observor (2 observors in the dataset) date Date of the observation (format [%d/%m/%y]) Daytime Starting time of observation (format: [text] hour%h%min) Windspeed Windspeed given by the mobile phone application &laquo; Accuweather &raquo; Temperatur Temperature at the time of observation in °C given by the mobile phone application &laquo; Accuweather &raquo; Pedestrian Number of pedestrians combined passing by during the time of observation CarDensity Number of cars counted on a 3-min period FlyBbird Number of common blackbirds Turdus merula that crossed the street transect during the time of observation (9 min). Every crossing event was counted. When the same individual crossed 2 times, it was counted 2 times -------------------------------------------------------------------------------- VARIABLE DESCRIPTIONS: point_observations.shp -------------------------------------------------------------------------------- StreetCod Site identifier for street transect - this are the same sites as in the file movement_proxy.shp (format: [text]) X Northing coordinate of observation point (latitude) (format: [degree]). Y Easting coordinate of the observation point (longitude) (format: [degree]) Obs Id of the observor (1 in this dataset) date Date of the observation (format [%d/%m/%y]) Daytime Starting time of observation (format: [text] hour%h%min) Windspeed Windspeed given by the mobile phone application &laquo; Accuweather &raquo; Temperatur Temperature at the time of observation in °C given by the mobile phone application &laquo; Accuweather &raquo; AbBlackb Number of common blackbirds Turdus merula that were detected by sight or sound during the 5-min observatin période, over a radius of 25 m. PABlackb Presence-Absence of observed blackbirds derived from the abundance. -------------------------------------------------------------------------------- VARIABLE DESCRIPTIONS: LULC_9Class_Angers.tif -------------------------------------------------------------------------------- 11 : Buildings < 5m 12 : Buildings 5-10m 13 : Buildings 10-18m 14 : Buildings > 18m 20 : Sealed areas 31 : vegetation < 1m 32 : Vegetation 1-3m 33 : Vegetation > 3m 40 : Farmland 50 : Bare soil 60 : Water -------------------------------------------------------------------------------- METHODS SUMMARY -------------------------------------------------------------------------------- Site Selection for Movement-Proxy Data: - 190 sites selected via stratified random sampling across vegetation cover (computed in a radius of 300m around the points) and traffic density (derived from TomTom navigation data for October 2022) - Observations along 50 m street transects - Detection of presence/absence of common blackbirds crossing the sampled streets Site Selection for Point Observations: - 68 sites selected via stratified random sampling across vegetation cover (computed in a radius of 300m around the points) and traffic density (derived from TomTom navigation data for October 2022) - Visual and acoustic detection of presence/absence of common blackbirds within a 25 m radius LULC map: Derived from land cover information from CoSIA (IGN 2023a), refined with a digital terrain model (IGN 2020) and height model (IGN 2023b) to extract building and vegetation height classes. -------------------------------------------------------------------------------- RELATED PUBLICATIONS -------------------------------------------------------------------------------- Lisa Merkens*, Meret Pundsack*, Anne Mimet*, Damien Gourmelon, Wolfgang W. Weisser. City-specific or generalisable resistances? An urban animal connectivity model performs better when parameterised from two cities. Preprint Submitted to Urban Ecosystems -------------------------------------------------------------------------------- FUNDING -------------------------------------------------------------------------------- Région Pays de la Loire through the PULSAR project VitalConnect (2024_05894 ) -------------------------------------------------------------------------------- LICENSE -------------------------------------------------------------------------------- This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). You are free to: - Share: copy and redistribute the material in any medium or format - Adapt: remix, transform, and build upon the material for any purpose Under the following terms: - Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. Full license text: https://creativecommons.org/licenses/by/4.0/ -------------------------------------------------------------------------------- CITATION -------------------------------------------------------------------------------- If you use this dataset, please cite both the dataset and the associated publication: Dataset: Mimet, A., Gourmelon, D, Oulhen, T., Vergondy, A. (2026) Observation points, movement-proxy data, and high-resolution land cover map for the common blackbird (Turdus merula) in Angers, France [Dataset]. Zenodo. Publication: Lisa Merkens*, Meret Pundsack*, Anne Mimet*, Damien Gourmelon, Wolfgang W. Weisser. City-specific or generalisable resistances? An urban animal connectivity model performs better when parameterised from two cities. Preprint Submitted to Urban Ecosystems -------------------------------------------------------------------------------- CONTACT -------------------------------------------------------------------------------- Anne Mimet Université d'Angers Laboratoire BiodivAG, DEP ENS SCIENCES Biologie, UFR SCIENCES 2 Boulevard de Lavoisier, F-49045 Angers, France Email: anne.mimet@univ-angers.fr ORCID: 0000-0001-9498-436X ================================================================================ END OF README ================================================================================

Kavanagh, Jack · Anthony, Patrick

6 files · 1.5 MB · geojson, geopackage, shapefiledeclared

A historical map of the boundaries of the Kazakh Steppe in c. 1848. This map is fully open to fellow researchers and is available in multiple open source formats (SHP, GPKG, GeoJSON).

Akandil, Cengiz · Plekhanova, Elena · Rietze, Nils · et al.

33 files · 1.2 GB · csv, shapefile, tiffdeclared

This repository contains the processed datasets used to analyse the spatial relationship between artificial light at night (ALAN) and Arctic fire occurrence. The dataset includes cumulative ALAN layers, binary masked ALAN layers, fire polygon shapefiles, and a final analysis table. The cumulative ALAN layers provide aggregate digital number (DN) values representing light intensity. The masked ALAN layers are binary rasters, where values of 1 indicate lit areas and values of 0 indicate unlit areas. Fire scar centroids were generated from the fire polygons and used to calculate the distance from each fire scar to the nearest lit area. The final analysis table contains the distance to the nearest lit area for each fire scar and control points. The fire polygon shapefile includes fire scars for the period 2001-2013.

Karamichali, Ioanna · Stefanidou, Eleni · Nakas, Christos · et al.

22 files · 418 MB · csv, pdf, shapefiledeclared

This deliverable presents the Phase 2 results of the BioProWRAP project, developed under the CLIMAAX framework, and focuses on enriching the initial wildfire‑risk assessment of Phase 1 through the integration of drought risk-an interconnected hazard with potential cascading effects-alongside diverse local datasets and live biodiversity inputs (crowdsourcing observations and eDNA). Phase 2 also enhances public engagement and inclusion through educational workshops and guided excursions in the three high‑risk, high‑value areas identified in Phase 1. The work was carried out by the REMTH team with continued scientific support from UTH, Greece.

Kusza, Szilvia · Astuti, Putri Kusuma

1 files · 6.5 MB · vcfdeclared