This dataset includes a series of R scripts required to carry out some of the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.

The scripts have been designed within the context of the R Processing Provider, a plugin that integrates the R processing environment into QGIS. For all the information about how to use these scripts in QGIS, please refer to Chapter 1 of the book referred to above.

The dataset includes 15 different scripts, which can implement the calculation of different metrics in QGIS:

Change statistics such as absolute change, relative change and annual rate of change (Change_Statistics.rsx)

Areal and spatial agreement metrics, either overall (Overall Areal Inconsistency.rsx, Overall Spatial Agreement.rsx, Overall Spatial Inconsistency.rsx) or per category (Individual Areal Inconsistency.rsx, Individual Spatial Agreement.rsx)

The four components of change (gross gains, gross losses, net change and swap) proposed by Pontius Jr. (2004) (LUCCBudget.rsx)

The intensity analysis proposed by Aldwaik and Pontius (2012) (Intensity_analysis.rsx)

The Flow matrix proposed by Runfola and Pontius (2013) (Stable_change_flow_matrix.rsx, Flow_matrix_graf.rsx)

Pearson and Spearman correlations (Correlation.rsx)

The Receiver Operating Characteristic (ROC) (ROCAnalysis.rsx)

The Goodness of Fit (GOF) calculated using the MapCurves method proposed by Hargrove et al. (2006) (MapCurves_raster.rsx, MapCurves_vector.rsx)

The spatial distribution of overall, user and producer’s accuracies, obtained through Geographical Weighted Regression methods (Local accuracy assessment statistics.rsx).

Descriptions of all these methods can be found in different chapters of the aforementioned book.

The dataset also includes a readme file listing all the scripts provided, detailing their authors and the references on which their methods are based.

Tracking an animal's location from video has many applications, from providing information on health and welfare to validating sensor-based technologies. Typically, accurate location estimation from video is achieved using cameras with overhead (top-down) views, but structural and financial limitations may require mounting cameras at other angles. We describe a user-friendly solution to manually extract an animal's location from non-overhead video. Our method uses QGIS, an open-source geographic information system, to: (1) assign facility-based coordinates to pixel coordinates in non-overhead frames; 2) use the referenced coordinates to transform the non-overhead frames to an overhead view; and 3) determine facility-based x, y coordinates of animals from the transformed frames. Using this method, we could determine an object's facility-based x, y coordinates with an accuracy of 0.13 ± 0.09 m (mean ± SD; range: 0.01–0.47 m) when compared to the ground truth (coordinates manually recorded with a laser tape measurer). We demonstrate how this method can be used to answer research questions about space-use behaviors in captive animals, using 6 ewe-lamb pairs housed in a group pen. As predicted, we found that lambs maintained closer proximity to their dam compared to other ewes in the group and lamb-dam range sizes were strongly correlated. However, the distance traveled by lambs and their dams did not correlate, suggesting that activity levels differed within the pair. This method demonstrates how user-friendly, open-source GIS tools can be used to accurately estimate animal location and derive space-use behaviors from non-overhead video frames. This method will expand capacity to obtain spatial data from animals in facilities where it is not possible to mount cameras overhead.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

This dataset contains the results of Land Use/Land Cover changes (LULC) analysis within the Bagno Chlebowo peatland (52°43'54''N, 16°44'7''E). Data was created as a result of analysis of archival and contemporary cartographic materials (Ur-messtischblatt maps, sheet 1713 from 1832; Messtischblatt from 1892, Polajewo sheet) and the BDOT10k geospatial database (from 2020).The data was created in GIS software and may be displayed, validated, and edited in an open software, e.g., QGIS.The dataset contains the following LULC classes: wetland, forest, open water, open area, and streams.The shx, dbf, cpg, prj, and qmd are supported by the open QGIS software and are auxiliary files for the correct operation of the shp file. The final results of the spatial analysis are displayed in LULC1823-2020.pdf.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

Water resources management is of primary importance for better understanding the impact on scenarios of climate change. The mean monthly runoff, soil moisture and aquifer recharge long-run forecast can support decisions to manage water demand, to recover degraded areas, water security, irrigation, electrical energy generation and urban water supply. The integrative and comprehensive analysis considering the spatial and temporal representation of hydrological process such as the distribution of rainfall, land cover and land use, ground elevation is a challenge. Therefore, these input data are important to modeling the water balance. We present the Rainfall-Runoff Balance Enhanced Model (RUBEM) as a grided hydrological model capable to represent the canopy interception, runoff, soil moisture on the non-saturated soil layer, baseflow and aquifer recharge. The RUBEM includes evapotranspiration and the interception based on the leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR) and normalized difference vegetation index (NVDI). The land use and land cover are updated during the simulations. The RUBEM was tested for tree tropical watersheds in Brazil with different hydrological and soil properties zones. The Piracicaba River has 10,701 km² (latitude 22.7o S), Ipojuca River has 3,471 km² (latitude 8.3o S) and Alto Iguaçu River with 2,696 km² (latitude 25.6o S). The input data from 2000 to 2010 was used to calibrate the runoff and the Nash-Sutcliffe indicator (NSI) results in 0.63, 0.48 and 0.60, respectively. The data input from 2011 to 2018 was the validation model period and NSI results in 0.66, 0.43 and 0.77. According to the NSI results, the model had a suitable calibration and validation in different hydrological zones and soils constitutions. The RUBEM is an important grided hydrological model with capabilities to support researchers, policymakers, and decision-makers under spatial and temporal water balance analysis to water managements plans, recovery degradation areas and long-run forecast.

A global self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries. All geospatial data is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Global Zip Code Database (Geospatial data)

• Address capture and validation

• Map and visualization

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Data export methodology

Our location data packages are offered in variable formats, including .csv. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Fully and accurately geocoded

• Administrative areas with a level range of 0-4

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

For additional insights, you can combine the map data with:

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Times

Why do companies choose our location databases

• Enterprise-grade service

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Abstract

The dataset is a geodatabase focusing on the distribution of freshwater fish species in Northern Greece. The study area encompasses various lakes and rivers within the regions of Thrace, Eastern, Central, and Western Macedonia, and Epirus. It classifies fish species into three categories based on their conservation status according to the IUCN Red List: Critically Endangered, Endangered, and Vulnerable. The data analysis reveals that the study area is characterized by high fish diversity, particularly in certain ecosystems such as the Evros River, Strymonas River, Aliakmonas River, Axios River, Volvi Lake, Nestos River, and Prespa Lake. These ecosystems serve as important habitats for various fish species. Mapping of the dataset shows the geographic distribution of threatened fish species, indicating that Northern Greece is a hotspot for species facing extinction risks. Overall, the dataset provides valuable insights for researchers, policymakers, and conservationists in understanding the status of fish fauna in Northern Greece and developing strategies for the protection and preservation of these important ecosystems.

Methods

Data Collection: The dataset was collected through a combination of field surveys, literature reviews, and the compilation of existing data from various reliable sources. Here's an overview of how the dataset was collected and processed:

• Freshwater Fishes and Lampreys of Greece: An Annotated Checklist
• The Red Book of Endangered Animals of Greece
• The "Red List of Threatened Species"
• The study "Monitoring and Evaluation of the Conservation Status of Fish Fauna Species of Community Interest in Greece"
• The international online fish database FishBase

Data Digitization and Georeferencing: To create a comprehensive database, we digitized and georeferenced the collected data from various sources. This involved converting information from papers, reports, and surveys into digital formats and associating them with specific geographic coordinates. Georeferencing allowed us to map the distribution of fish species within the study area accurately.

Data Integration: The digitized and georeferenced data were then integrated into a unified geodatabase. The geodatabase is a central repository that contains both spatial and descriptive data, facilitating further analysis and interpretation of the dataset.

Data Analysis: We analyzed the collected data to assess the distribution of fish species in Northern Greece, evaluate their conservation status according to the IUCN Red List categories, and identify the threats they face in their respective ecosystems. The analysis involved spatial mapping to visualize the distribution patterns of threatened fish species.

Data Validation: To ensure the accuracy and reliability of the dataset, we cross-referenced the information from different sources and validated it against known facts about the species and their habitats. This process helped to eliminate any discrepancies or errors in the dataset.

Interpretation and Findings: Finally, we interpreted the analyzed data and derived key findings about the diversity and conservation status of freshwater fish species in Northern Greece. The results were presented in the research paper, along with maps and visualizations to communicate the spatial patterns effectively.

Overall, the dataset represents a comprehensive and well-processed collection of information about fish fauna in the study area. It combines both spatial and descriptive data, providing valuable insights for understanding the distribution and conservation needs of freshwater fish populations in Northern Greece.

Usage notes

The data included with the submission is stored in a geodatabase format, specifically an ESRI Geodatabase (.gdb). A geodatabase is a container that can hold various types of geospatial data, including feature classes, attribute tables, and raster datasets. It provides a structured and organized way to store and manage geographic information.

To open and work with the geodatabase, you will need GIS software that supports ESRI Geodatabase formats. The primary software for accessing and manipulating ESRI Geodatabases is ESRI ArcGIS, which is a proprietary GIS software suite. However, there are open-source alternatives available that can also work with Geodatabase files.

Open-source software such as QGIS has support for reading and interacting with Geodatabase files. By using QGIS, you can access the data stored in the geodatabase and perform various geospatial analyses and visualizations. QGIS is a powerful and widely used open-source Geographic Information System that provides similar functionality to ESRI ArcGIS.

For tabular data within the geodatabase, you can export the tables as CSV files and open them with software like Microsoft Excel or the open-source alternative, LibreOffice Calc, for further analysis and manipulation.

Overall, the data provided in the submission is in a geodatabase format, and you can use ESRI ArcGIS or open-source alternatives like QGIS to access and work with the geospatial data it contains.

Set of vector maps of the territory of SFR Yugoslavia with the administrative division into municipalities in the period from 1970 to 1991, as well as the territories of Yugoslavia’s successor states – Bosnia and Herzegovina, Croatia, Kosovo, Montenegro, North Macedonia, Serbia, and Slovenia – with the administrative division into municipalities in the period from 1992 to 2020. Vector data is stored as ESRI shapefiles and contains vector data - polygons. Municipal data valid on 1 July of a given year. All maps were newly created using information on individual municipalities published in Enciklopedija Jugoslavije and the official gazettes of the successor states: Službeni list Bosne i Hercegovine, Narodne novine, Gazeta Zyrtare e Republikës së Kosovës, Službeni list Crne Gore, Služben vesnik na Republika Severna Makedonija, Uradni list Republike Slovenije, Službeni glasnik Republike Srbije.

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019. Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar. The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are: - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM) - Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed. Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range. This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

A continuous dataset of Land Surface Temperature (LST) is vital for climatological and environmental studies. LST can be regarded as a combination of seasonal mean temperature (climatology) and daily anomaly, which is attributed mainly to the synoptic-scale atmospheric circulation (weather). To reproduce LST in cloudy pixels, time series (2002-2019) of cloud-free 1km MODIS Aqua LST images were generated and the pixel-based seasonality (climatology) was calculated using temporal Fourier analysis. To add the anomaly, we used the NCEP Climate Forecast System Version 2 (CFSv2) model, which provides air surface temperature under both cloudy and clear sky conditions. The combination of the two sources of data enables the estimation of LST in cloudy pixels. Data structure The dataset consists of geo-located continuous LST (Day, Night and Daily) which calculates LST values of cloudy pixels. The spatial domain of the data is the Eastern Mediterranean, at the resolution of the MYD11A1 product (~1 Km). Data are stored in GeoTIFF format as signed 16-bit integers using a scale factor of 0.02, with one file per day, each defined by 4 dimensions (Night LST Cont., Day LST Cont., Daily Average LST Cont., QA). The QA band stores information about the presence of cloud in the original pixel. If in both original files, Day LST and Night LST there was NoData due to clouds, then the QA value is 0. QA value of 1 indicates NoData at original Day LST, 2 indicates NoData at Night LST and 3 indicates valid data at both, day and night. File names follow this naming convention: LST_

Our World Administrative Boundaries Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

The Science Case in the Caribbean region presents records on landslides, precipitation, maps used as inputs of hazard models and drone imagery over the region of interest.
For the Carribean study-case, an analysis of open and proprietary satellite based dataset was used to facilitate the setup and evaluation of physically-based multi-hazard models. These allow for qualification and quantification of spatio-temporal multi-hazard patterns. These form a crucial input into the general hazard and risk assessment workflow.

Presented here are the datasets employed for Case Study 4 in Deliverable D3.1 with a short description, produced and saved within the following folders:

Dominica_landslide: the landslides datasets mapped by ITC using high-resolution satellite imagery. It is intended to calibrate and validate the flood and landslide modelling. The folder contains four shapefiles:

· Landslide_Part.shp - Shapefile containing landslide extent, flash flood extents, and their attributes.

· Cloud.shp – Shapefile represents the cloud-filled areas in the satellite imagery where no mapping was possible.

· The other two shapefiles are self-explanatory.

GPM_Maria: NASA Global Precipitation Mission (GPM) precipitation maps processed for model input in LISEM. GPM is a hybrid fusion with satellite datasets for precipitation estimates. Mean as input data to represent precipitation in the landslide and flood modelling.

Maps_Models_Input : Soil and land use and channels, lots of custom work, SOILGRIDS, and SPOT image classification; all the datasets are ready for model input for OpenLISEM and LISEM Hazard or FastFlood. The dataset is meant to calibrate and validate the flood and landslide modelling.

The raster files are either in Geotiff format or PCraster map format. Both can be opened by GIS systems such as GDAL or QGIS. The projection of each file is in UTM20N.

Some key files are:

• dem.map -elevation model, the height of the landscape in meters above sea level.
• lai.map - leaf area index, estimated using empirical relationships based on NDVI (Normalized Difference Vegetation Index)). The source data to calculate NDVI is Sentinel-2.
• KSat.map - Saturated hydraulic conductivity of the soil, estimated based on a combination of SOILGRIDS soil texture, Saxton et al. (2006) Pedotransfer functions, and a national soil map for Dominica.
• clay.map - Clay texture fraction, SoilGrids resampling
• silt.map - Silt texture fraction, SoilGrids resampling
• sand.map - Sand texture fraction, SoilGrids resampling
• cover.map - Vegetation cover as a fraction, estimated using linear correlation with NDVI.
• lu_new.map - Spot satellite image classification at 10 meters resolution for predominant land use types.
• n.map - Mannings surface roughness coefficient, specific value based on the land use type.
• ndvi.map - Normalized Differential Vegetation Index, based on Sentinel-2 images in summer.
• ldd.map - Drainage network map for the island, which can be used for flow accumulation and streamflow detection
• catchments.map - Catchment ID's based on the ldd.map drainage network.
• Channelldd.map - Channel-only drainage network map, calibrated manually to have all channels on the island represented correctly.
• Soildepth - Soil depth in meters, based on a physically-based soil depth model in meters and observational data obtained from landslide-sites during fieldwork in 2018.
• Slope.map - Slope map in gradient of the elevation model (m/m) in the steepest direction

StakeholderQuestionnaire_Survey_ITC: The stakeholder questionnaires particularly relating to the tools developed partly by this project on rapid hazard modelling. Stakeholder Engagement survey and Stakeholder Survey Results prepared and implemented by Sruthie Rajendran as part of her MSc Thesis Twin Framework For Decision Support In Flood Risk Management supervised by Dr. M.N. Koeva (Mila) and Dr. B. van den Bout (Bastian) submitted in July 2024.

·Drone_Images_ 2024: Images captured using a DJI drone of part of the Study area in February 2024. The file comprises three different regions: Coulibistrie, Pichelin and Point Michel. The 3D models for Coulibistrie were generated from the nadir drone images using photogrammetric techniques employed by the software Pix4D. The image Coordinate System is WGS 84 (EGM 96 Geoid0), but the Output Coordinate System of the 3D model is WGS 84 / UTM zone 20N (EGM 96 Geoid). The other two folders contain only the drone images captured for that particular region's Pichelin and Point Michel. The dataset is used with other datasets to prepare and create the digital twin framework tailored for flood risk management in the study area.

Our United States zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

This dataset supports the METRO·MIX research project, which investigates urban proximity and land-use mix as foundational criteria for promoting healthier, more compact metropolitan areas. The data are organized into three main components: General Data, City Data, and 15-Minute City Data, covering the Spanish cities of Barcelona, Madrid, Málaga, and A Coruña between 2021 and 2023. The General Data includes harmonized national-scale information derived from cadastral records and demographic statistics provided by the Spanish Land Registry and the National Institute of Statistics (INE), with variables such as land use categories, building function, population structure, and socioeconomic indicators. The City Data component provides spatially disaggregated information at the census section level for each city, integrating official records with field-collected data on urban functions. This data was processed to derive indices such as the Residential/Non-Residential Balance (RNR Index) and the Land Use Mix Index (LUM Index), facilitating comparative urban analysis. The 15-Minute City Data focuses on neighborhood-scale accessibility and functional diversity, particularly in Barcelona. It incorporates high-resolution, geolocated data on ground-floor commercial activities, categorized and verified through in-situ fieldwork and a custom-built mobile application. All datasets were processed using GIS software (QGIS 3.32) and validated through a multi-step quality control process, including spatial checks, field verification, and harmonization protocols. The dataset is structured in open formats (CSV, GeoJSON, Shapefiles) and intended to support further analysis in urban planning, geography, and public health research.

This database contains nine land use / land cover datasets collected in a standardized manner between 2013 and 2022 in seven tropical countries within the framework of the international JECAM initiative: Burkina Faso (Koumbia), Madagascar (Antsirabe), Brazil (São Paulo and Tocantins), Senegal (Nioro, Niakhar, Mboro, Tattaguine and Koussanar), Kenya (Muranga), Cambodia (Kandal) and South Africa (Mpumalanga) (cf Study_sites‧kml). These quality-controlled datasets are distinguished by ground data collected at field scale by local experts, with precise geographic coordinates, and following a common protocol. This database, which contains 31879 records (24 287 crop and 7 592 non-crop) is a geographic layer in Shapefile format in a Geographic Coordinates System with Datum WGS84. Field surveys were conducted yearly in each study zone, either around the growing peak of the cropping season, for the sites with a main growing season linked to the rainy season such as Burkina Faso, or seasonally, for the sites with multiple cropping (e‧g. São Paulo site). The GPS waypoints were gathered following an opportunistic sampling approach along the roads or tracks according to their accessibility, while ensuring the best representativity of the existing cropping systems in place. GPS waypoints were also recorded on different types of non-crop classes (e‧g. natural vegetation, settlement areas, water bodies) to allow differentiating crop and non-crop classes. Waypoints were only recorded for homogenous fields/entities of at least 20 x 20 m². To facilitate the location of sampling areas and the remote acquisition of waypoints, field operators were equipped with GPS tablets providing access to a QGIS project with Very High Spatial Resolution (VHSR) images ordered just before the surveys. For each waypoint, a set of attributes, corresponding to the cropping practices (crop type, cropping pattern, management techniques) were recorded (for more informations about data, see data paper being published). These datasets can be used to validate existing cropland and crop types/practices maps in the tropics, but also, to assess the performances and the robustness of classification methods of cropland and crop types/practices in a large range of Southern farming systems.

Our San Marino zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Our Colombia zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Our Martinique zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Our Jersey zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.