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23 results for “human settlement”
Global Human Settlement Layer per zoom-level 18 Quadtree tile for selected countries as Spatialite database with OpenStreetMap building completeness assessment
<p>This Spatialite database contains the built-up area of the Global Human Settlement Layer (GHSL) per zoom-level 18 Quadtree tile. Additionally, it provides a comparison of the GHSL with buildings in OpenStreetMap: For each tile the built-up ratio between the building footprints and the GHSL is given and a binary completeness assessment (buildings complete, not complete) is provided for easy use. This dataset was created using the obmgapanalysis tool: https://git.gfz-potsdam.de/dynamicexposure/openbuildingmap/obmgapanalysis</p>
The next frontier: Human settlements in the marine environment
<p>Human settlements have impacted most terrestrial environments on Earth. With a rapidly growing global population, surging demands for resources, and unprecedented alteration to Earth's ecosystems, the conservation of our only suitable habitat is a contemporary zeitgeist. Moreover, humanity's nature for enterprise and exploitation continuously pushes the frontiers of exploration, while the governance associated with expansion beyond terra firma is uncertain and rife with conflict. Though the notion of human expansion into the oceans is becoming more orthodox, we have largely overlooked Earth's oceans as space for human habitat. Here, we build on the case for humanity's future over the marine environment and discuss how exploring this frontier will result in advances for industries, economies, and exploration. The "Blue Acceleration" could transform the nascent phenomenon of marine cities into reality, and we support this notions with a global marine spatial plan to assess the suitability of marine settlements. Our analysis shows that every oceanic basin supports regions for sustainable human settlements. More broadly, we discuss the implications of a human-marine frontier and the benefits that will ensue for science, humanity and our planet. Finally, we argue that the human-marine frontier should be an immediate steppingstone for future exploration, innovation and discovery.</p>
The global 30-m human settlement dataset from 1985 to 2020
<p>GHS30 is the first global fine human settlement extents derived from the global 30 m impervious-surface (ISA) dynamic dataset and the global residential population of GHS-POP. It utilizes a refined classification system containing 4 human settlement types and covers the time span from 1985 to 2020 with an update cycle of 5-year. In specific, the global human settlement patches were delineated from the ISA data using a developed model that consisted of kernel density estimation, initial boundaries delineation, and morphological processing. The classification schema oriented to the typologies of human settlement patches were defined by combining the size of population and the population density within each patch that calculated from GHS-POP.</p> <p>The GEE code used has been deposited in GitHub at <a href="https://github.com/oucong1004/globalHumanSettlement">https://github.com/oucong1004/globalHumanSettlement</a>.</p>
The next frontier: Human settlements in the marine environment
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Trait shifts in bird communities from primary forest to human settlements in Mexican seasonal forests: Are there ruderal birds?
<p><span><span><span><span>Agriculture, cattle grazing, and human settlements negatively affect bird biodiversity, driving the loss of ecologically specialized species and favoring the dominance of generalists. Because ecological pressures define organisms' success by acting on their intrinsic traits, biodiversity loss due to anthropization might cause directional trait shifts. Here, we use a trait-based approach to find empirical evidence of trait-shifts in bird communities across an anthropization gradient in seasonal forests in central Mexico. We performed point-count bird surveys within a region of tropical deciduous and seasonal oak forests considering three degrees of anthropization: primary forest, secondary growth, and human settlements. A multivariate analysis (PCA) showed similar trait-covariation patterns for both forest types; in the anthropized habitat the bird communities exhibited shorter life-cycles, higher fecundity, and broader ecological niches (diet, foraging habitat, and nesting resources) than those in the primary forests. Our finding of directional trait shifts resembles Evolutionary Ecological Strategies Theory (EES) predictions for successful organisms within highly disturbed anthropized habitats, which are known as a "ruderal adaptative strategy" in the EES framework. The use of trait-based approaches could improve ecological generalizations in bird communities, leading to a better understanding of avian biodiversity's responses to anthropization.</span></span></span></span></p>
Supplementary Data: Human settlement pressure drives slow-moving landslide exposure
<p>These datasets (.Rmd, .Rroj., .rds) are ready to use within the R software for statistical programming with the R Studio Graphical User Interface (https://posit.co/download/rstudio-desktop/). Please copy the folder structure into one single directory and follow the instructions given in the .Rmd file. Files and folders are described in the README.md file</p> <p>0_ferrer_etal_2024.Rproj</p> <p>README.md</p> <p>1_R_Notebook:</p> <ul> <li>SlowMovingLandslide_Exposure.rmd</li> <li>Ferrer2024_FloodExposureAnalysis.Rmd</li> </ul> <p>2_data:</p> <ul> <li>1_input: <ul> <li>sm_database_2023_01_17.csv</li> <li>sm_database_references.pdf</li> <li>gmba_v2</li> <li>ipcc_regions</li> <li>natrual_earth</li> </ul> </li> <li>2_processed: <ul> <li>model_data_2023_01_17.csv</li> <li>summary_database.csv</li> </ul> </li> </ul> <p>3_results:</p> <ul> <li>1_model <ul> <li>2023_12_31.model.rds</li> </ul> </li> <li>2_figures: <ul> <li>main_figure_elements</li> </ul> </li> <li>3_ext_figures</li> </ul> <p>4_manuscript_figures:</p> <ul> <li>1_main figures</li> <li>2_extended_figures</li> </ul> <p><br>5_floding_analysis:</p> <ul> <li>flood_comp_mod.csv</li> <li>flood_comp_model.rds</li> </ul>
Wikidata Dump human settlements
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/1607">View on wdumper</a> </p> <p> <b>entity count</b>: 565490, <b>statement count</b>: 3852167, <b>triple count</b>: 9179041 </p>
Trait shifts in bird communities from primary forest to human settlements in Mexican seasonal forests: Are there ruderal birds?
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Data from: A tropical paradise for all? Nest-site selection shifts by an endemic Neotropical woodpecker associated with human settlements
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Data from: Proximity to human settlements can reduce vigilance, but increase alarm call responses in African antelopes
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FIGURE 2. MAPCRM 85138 in Cetopirus complanatus (Cirripedia: Coronulidae) from the late Middle Pleistocene human settlement of Pinnacle Point 13 B (Mossel Bay, South Africa)
FIGURE 2. MAPCRM 85138, Cetopirus complanatus, single rostral compartment collected at site PP13B (South Africa) in late Middle Pleistocene deposits: a, external view; b, internal view; c, radial view. Scale bar = 10 mm.
Replication files for the publication "The Global Long-Term Effects of Storm Surge Flooding on Human Settlements in Coastal Areas"
<p>This repisority contains code and data to replicate the main results of the publication Kunze & Strobl (forthcoming) "The Global Long-Term Effects of Storm Surge Flooding on Human Settlements in Coastal Areas".</p>
SD4EO - Physically Based Rendering images of human settlements for solar panel detection
<p>This dataset contains part of the results of the SD4EO project, including images corresponding to the use case of solar panel detection on human settlements. The dataset contains images that have been simulated corresponding to low altitude aerial images. Each image in the dataset includes pixel-level labels for each element present in the image, ensuring perfect accuracy due to the synthetic nature of the images. This eliminates common errors in manual or semi-automatic labeling processes.</p> <p><strong>Image labeling</strong></p> <p><strong>(Open Street Map color code)</strong></p> <div> <table> <tbody> <tr> <td> <p><strong>Element</strong></p> </td> <td> <p><strong>R</strong></p> </td> <td> <p><strong>G</strong></p> </td> <td> <p><strong>B</strong></p> </td> </tr> <tr> <td> <p>Road</p> </td> <td> <p>255</p> </td> <td> <p>255</p> </td> <td> <p>255</p> </td> </tr> <tr> <td> <p>Building</p> </td> <td> <p>196</p> </td> <td> <p>182</p> </td> <td> <p>171</p> </td> </tr> <tr> <td> <p>Highway</p> </td> <td> <p>232</p> </td> <td> <p>146</p> </td> <td> <p>162</p> </td> </tr> <tr> <td> <p>Industrial zone</p> </td> <td> <p>234</p> </td> <td> <p>203</p> </td> <td> <p>228</p> </td> </tr> <tr> <td> <p>Parking</p> </td> <td> <p>238</p> </td> <td> <p>238</p> </td> <td> <p>238</p> </td> </tr> <tr> <td> <p>Railway</p> </td> <td> <p>112</p> </td> <td> <p>112</p> </td> <td> <p>112</p> </td> </tr> <tr> <td> <p>Residential zone</p> </td> <td> <p>224</p> </td> <td> <p>223</p> </td> <td> <p>223</p> </td> </tr> <tr> <td> <p>Vegetation</p> </td> <td> <p>174</p> </td> <td> <p>223</p> </td> <td> <p>163</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>170</p> </td> <td> <p>211</p> </td> <td> <p>223</p> </td> </tr> </tbody> </table> </div> <p>For solar panels masks white color is used to mark the pixels corresponding to solar panels.</p> <p><br><strong>Image name convention</strong></p> <p>The name convention follows the next schema of fields, separated by the character “_”</p> <p>- ID number</p> <p>- Sub ID number</p> <p>- Meters per pixel resolution</p> <p>- Type of image: color, labels, solarpanels</p> <p><strong>Creation and funding</strong></p> <p>All the images have been generated using a tool developed in Unity. This tool will be soon available to enable the generation of new datasets.</p> <p>This research work has been funded by the European Space Agency (ESA) under the FutureEO program and the SD4EO project (Contract No.: 4000142334/23/I-DT), supervised by the ESA Φ-lab.</p> <p><strong>License and attribution</strong></p> <p>This dataset <span>is</span> licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC <span>BY</span> <span>4.0</span>).</p> <p><span>When using the images from this dataset, please attribute them as follows: "Synthetic images created by the research group ARTEC - IRTIC - University of Valencia".</span></p>
Data from: Tigers, terrain, and human settlement influence the occupancy of leopards (Panthera pardus) in southwestern Tarai, Nepal
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Investigating the impact of human settlements upon the availability of larval habitats and Aedes albopictus population
<b>Description: </b><p>This experiment analysed the impact of human settlements upon Aedes albopictus populations. Surveys were conducted in natural environments (twice-logged forest) and human settlements in oil palm and logged forest. At each study site, three surveys were conducted: <br><br>1) Aquatic Habitat Survey: within the study area, all bodies of water were analysed for abiotic characteristics – container type, water volume, air temperature, water temperature, canopy cover and turbidity. <br>2) Larval Survey: for each body of water, the number of and stage of larvae/pupae was recorded. Larvae/pupae were extracted from the water, reared to adults and Ae. albopictus were identified. <br>3) Adult Population Survey: human landing catches were conducted to collect Ae. albopictus adults. Abundance and sex ratio was recorded for each location, and the wing span of each individual was taken. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/202"><b>Investigating the impact of human settlements upon the availability of larval habitats and Aedes albopictus populations</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>SAFE - Tropical Forest Ecology Masters (Studentship)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JDL.8 (59))</li><li>Medical Research & Ethics Committee (Ethics licence NMRR-17-3242-39250 (IIR))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3929764">here</a></p><p><b>Files: </b>This dataset consists of 4 files: M_Vollans_Larval_Habitats_19v2.xlsx, Larval_Adult_ID.zip, HLC_Wing_Length.zip, Larval_Habitat_Photos.zip</p><p><b>M_Vollans_Larval_Habitats_19v2.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Aquatic Habitat Survey</b> (described in worksheet AquaticHabitatSurvey)</p><p>Description: AquaticHabitatSurvey</p><p>Number of fields: 30</p><p>Number of data rows: 89</p><p>Fields: </p><ul><li><b>Date</b>: Date of survey (Field type: date)</li><li><b>Land_Type</b>: Details of Land-Type (Field type: categorical)</li><li><b>Location</b>: Details of location. OP = Oil Palm, LF = Logged Forest (Field type: categorical)</li><li><b>Replicate</b>: Details location, as named in location sheet. To 'replicate' resolution. (Field type: location)</li><li><b>Number_10m^2_Square</b>: Number 10m^2 surveyed (Field type: replicate)</li><li><b>Sampling_Region</b>: Details of 10m^2 the aquatic habitat corresponds to: in human settlements, this is the type of building, and in natural environments, the compass direction of the square from the second order point (N/S/E/W). (Field type: replicate)</li><li><b>Elevation</b>: Elevation from GPS (Field type: numeric)</li><li><b>GPS_file_name</b>: File name saved on GPS (Field type: id)</li><li><b>Access_Available</b>: Record of whether able to access the aquatic habitat (Field type: categorical)</li><li><b>Comments</b>: General comments on aquatic habitat (Field type: comments)</li><li><b>Canopy_cover</b>: Record of canopy cover. NA where inside. Data incomplete. (Field type: numeric)</li><li><b>Inside/Outside</b>: Record of whether the aquatic habitat is inside or outside (NA for natural land types, where all larval habitats are outside). (Field type: categorical)</li><li><b>Water_Turbidity</b>: Water turbidity, subjective categorization by a consistant surveyer. (Field type: categorical)</li><li><b>Water_Temperature</b>: Temperature of the water in the aquatic habitat. NOTE: potential faulty equipment. This data was not used in analysis. (Field type: numeric)</li><li><b>Air_Temperature</b>: Temperature of the air directly above the aquatic habitat. (Field type: numeric)</li><li><b>Type_Aquatic_Habitat</b>: Record of the type of aquatic habitat (Field type: categorical)</li><li><b>In_Constant_Use</b>: Record of whether the aquatic habitat is in constant use. (Field type: categorical)</li><li><b>Water_Volume</b>: Total water volume of the aquatic habitat (Field type: numeric)</li><li><b>Radius</b>: Radius of the aquatic habitat (Field type: numeric)</li><li><b>Diameter</b>: Diameter of the aquatic habitat (Field type: numeric)</li><li><b>Length</b>: Length of the aquatic habitat (Field type: numeric)</li><li><b>Width</b>: Width of the aquatic habitat (Field type: numeric)</li><li><b>Water_Depth</b>: Depth of the water in the aquatic habitat (Field type: numeric)</li><li><b>Larval/Pupal_Presence</b>: Record of whether larvae/ pupae are present in the aquatic habitat. (Field type: categorical)</li><li><b>Total_Number_Larvae/Pupae</b>: The total number of larvae / pupae found within an aquatic habitat. (Field type: numeric)</li><li><b>1/2_Instar_Larvae_Number</b>: The number of 1st and 2nd instar larvae found within an aquatic habitat. (Field type: numeric)</li><li><b>3/4_Instar_Larvae_Number</b>: The number of 3rd and 4th instar larvae found within an aquatic habitat. (Field type: numeric)</li><li><b>Pupae_Number</b>: The number of pupae found within an aquatic habitat. (Field type: numeric)</li><li><b>Further_comments</b>: Further comments on aquatic habitat. (Field type: comments)</li><li><b>Photo.folder.name</b>: Folder name containing photos of the study site. (Field type: id)</li></ul></li><li><p><b>Larva lAdult ID</b> (described in worksheet LarvalAdultID)</p><p>Description: LarvalAdultID</p><p>Number of fields: 6</p><p>Number of data rows: 76</p><p>Fields: </p><ul><li><b>Date_of_ID</b>: Date of survey (Field type: date)</li><li><b>Ae.albopictus</b>: Record of whether adults are Ae. albopictus, or some other member of the Culicidae (Field type: taxa)</li><li><b>Location</b>: Details of location. (Field type: location)</li><li><b>Wing_length</b>: Measurement of wing length. (Field type: numeric trait)</li><li><b>Photo_folder_ID</b>: Folder name containing photos for adult ID and wing length. (Field type: id)</li><li><b>Comments</b>: General comments about identification / wingspan measurement. (Field type: comments)</li></ul></li><li><p><b>HLC overview</b> (described in worksheet HLCoverview)</p><p>Description: HLCoverview</p><p>Number of fields: 8</p><p>Number of data rows: 16</p><p>Fields: </p><ul><li><b>Date</b>: Date of survey (Field type: date)</li><li><b>Location</b>: Broad location of where HLC occurred. (Field type: location)</li><li><b>Comments</b>: Qualitative details about exact location. (Field type: comments)</li><li><b>Ae.albopictus</b>: Number of Ae. albopictus obtained during a single 1.5 hour sample. (Field type: abundance)</li><li><b>Number_of_males</b>: Number of Ae. albopictus males (Field type: numeric)</li><li><b>Sex_Ratio_(M/Total)</b>: Sex Ratio (number of males / total number) (Field type: numeric)</li><li><b>Survey_complete</b>: Record if it was possible to complete the HLC survey. (Field type: categorical)</li><li><b>Further_comments</b>: Further comments (Field type: comments)</li></ul></li><li><p><b>HLC adult ID</b> (described in worksheet HLCadultID)</p><p>Description: HLCadultID</p><p>Number of fields: 8</p><p>Number of data rows: 111</p><p>Fields: </p><ul><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Location</b>: Broad location of where HLC occurred. (Field type: location)</li><li><b>Specific_Location</b>: Qualitative details about exact location. (Field type: comments)</li><li><b>Sex</b>: Sex (Field type: categorical)</li><li><b>Wing Length</b>: Wing length measurement (Field type: numeric trait)</li><li><b>Weather</b>: Weather description (Field type: categorical)</li><li><b>Comments</b>: Comments on wing dissection issues. (Field type: comments)</li><li><b>Photo_Code</b>: Folder name containing wing length photos. (Field type: id)</li></ul></li></ol><p><b>Larval_Adult_ID.zip</b></p><p>Description: Zip file containing JPEG images to ID adult mosquitoes reared from collected larvae. </p><p><b>HLC_Wing_Length.zip</b></p><p>Description: Zip file containing JPEG images of the wingspan of adult mosquitoes collected via HLC. </p><p><b>Larval_Habitat_Photos.zip</b></p><p>Description: Zip file containing photos of some of the larval habitats encountered, and some general landscapes. </p><p><b>Date range: </b>2019-04-03 to 2019-05-06</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Diptera <br> -  -  -  -  -  Culicidae <br> -  -  -  -  -  -  <i>Aedes</i> <br> -  -  -  -  -  -  -  <i>Aedes albopictus</i> <br></div><p></p>
FIGURE 1 in Cetopirus complanatus (Cirripedia: Coronulidae) from the late Middle Pleistocene human settlement of Pinnacle Point 13 B (Mossel Bay, South Africa)
FIGURE 1. Location and landscape of the study area.
Data from: Temporal factors and distance to human settlement affect nest survival of the endangered Yellow-headed Parrot in Belize, Central America
<p>The endangered Yellow-headed Parrot (<em>Amazona oratrix</em>) has experienced a considerable reduction in abundance and distribution. Identifying natural and anthropogenic causes of nest failure is a critical step towards developing conservation actions that increase nest survival. In this study, we examined daily nest survival in relation to temporal, habitat and anthropogenic factors, as well as nest site properties. We monitored nests (<em>n </em>= 124) across six study sites in Belize during 2017 and 2018 and independently modeled the effects of predation, abandonment and poaching on daily nest survival rates. Overall, the estimated cumulative nest survival probability was 0.18 (95% CI = 0.12 – 0.25). Predation was the main cause of nest failure, followed by abandonment and poaching. Our results showed that nest predation and abandonment usually occurred early in the nesting cycle. Day within the nesting season negatively influenced daily survival for abandoned nests and had a quadratic effect on survival for poached nests.<strong> </strong>Poaching events occurred at a specific date range later in the season, with nests farther from the nearest human settlement having higher daily survival. Findings from this study highlight the additive mortality effect that nest poaching is having on Yellow-headed Parrot populations in Belize and show that managers can anticipate the timing and location of nests most vulnerable to poaching.<span> </span></p>
Data from: Temporal factors and distance to human settlement affect nest survival of the endangered Yellow-headed Parrot in Belize, Central America
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Data from: Forbidden fruit: human settlement and abundant fruit create an ecological trap for an apex omnivore
Habitat choice is an evolutionary product of animals experiencing increased fitness when preferentially occupying high-quality habitat. However, an ecological trap (ET) can occur when an animal is presented with novel conditions and the animal's assessment of habitat quality is poorly matched to its resulting fitness. We tested for an ET for grizzly (brown) bears using demographic and movement data collected in an area with rich food resources and concentrated human settlement. We derived measures of habitat attractiveness from occurrence models of bear food resources and estimated demographic parameters using DNA mark–recapture information collected over 8 years (2006–2013). We then paired this information with grizzly bear mortality records to investigate kill and movement rates. Our results demonstrate that a valley high in both berry resources and human density was more attractive than surrounding areas, and bears occupying this region faced 17% lower apparent survival. Despite lower fitness, we detected a net flow of bears into the ET, which contributed to a study-wide population decline. This work highlights the presence and pervasiveness of an ET for an apex omnivore that lacks the evolutionary cues, under human-induced rapid ecological change, to assess trade-offs between food resources and human-caused mortality, which results in maladaptive habitat selection.
SD4EO - Physically Based Rendering images of human settlements (Sentinel-2 B2B3B4B8)
<p>This dataset contains part of the results of the SD4EO project, including images corresponding to the use case of human settlement monitoring. The dataset contains images that have been simulated corresponding to different sensors of the Sentinel 2 satellites. Each image in the dataset includes pixel-level labels for each element present in the image, ensuring perfect accuracy due to the synthetic nature of the images. This eliminates common errors in manual or semi-automatic labeling processes.</p> <p><strong>Multispectral images included</strong><br>Sentinel 2: Images from bands 2, 3, 4 and 8.</p> <p><strong>Image labeling</strong></p> <p><strong>(Open Street Map color code)</strong></p> <div> <table> <tbody> <tr> <td> <p><strong>Element</strong></p> </td> <td> <p><strong>R</strong></p> </td> <td> <p><strong>G</strong></p> </td> <td> <p><strong>B</strong></p> </td> </tr> <tr> <td> <p>Road</p> </td> <td> <p>255</p> </td> <td> <p>255</p> </td> <td> <p>255</p> </td> </tr> <tr> <td> <p>Building</p> </td> <td> <p>196</p> </td> <td> <p>182</p> </td> <td> <p>171</p> </td> </tr> <tr> <td> <p>Highway</p> </td> <td> <p>232</p> </td> <td> <p>146</p> </td> <td> <p>162</p> </td> </tr> <tr> <td> <p>Industrial zone</p> </td> <td> <p>234</p> </td> <td> <p>203</p> </td> <td> <p>228</p> </td> </tr> <tr> <td> <p>Parking</p> </td> <td> <p>238</p> </td> <td> <p>238</p> </td> <td> <p>238</p> </td> </tr> <tr> <td> <p>Railway</p> </td> <td> <p>112</p> </td> <td> <p>112</p> </td> <td> <p>112</p> </td> </tr> <tr> <td> <p>Residential zone</p> </td> <td> <p>224</p> </td> <td> <p>223</p> </td> <td> <p>223</p> </td> </tr> <tr> <td> <p>Vegetation</p> </td> <td> <p>174</p> </td> <td> <p>223</p> </td> <td> <p>163</p> </td> </tr> <tr> <td> <p>Water</p> </td> <td> <p>170</p> </td> <td> <p>211</p> </td> <td> <p>223</p> </td> </tr> </tbody> </table> <p><strong>(Building Masks)</strong></p> <p>Green - Non residential</p> <p>Red - Residential</p> </div> <p><br><strong>Image name convention</strong></p> <p>The name convention follows the next schema of fields, separated by the character “_”</p> <p>- ID number</p> <p>- Meters per pixel resolution</p> <p>- Type of image (Sentinel 2 bands, labels or buildings masks): B2B3B4, B8, labels, buildings</p> <p><strong>Image formats</strong></p> <p>PNG format is used to display each band in normalized values [0..1].</p> <p>TIFF format is used to save each band with floating point precision in the original range of the satellite images.</p> <table> <tbody> <tr> <td> <p><strong>Type of data</strong></p> </td> <td> <p><strong>PNG range</strong></p> </td> <td> <p><strong>TIFF range</strong></p> </td> </tr> <tr> <td> <p>B2</p> </td> <td> <p>0 - 255</p> </td> <td> <p>0 - 2000</p> </td> </tr> <tr> <td> <p>B3</p> </td> <td> <p>0 - 255</p> </td> <td> <p>0 - 2500</p> </td> </tr> <tr> <td> <p>B4</p> </td> <td> <p>0 - 255</p> </td> <td> <p>0 - 3000</p> </td> </tr> <tr> <td> <p>B8</p> </td> <td> <p>0 - 255</p> </td> <td> <p>1180 - 5736</p> </td> </tr> </tbody> </table> <p><strong>Creation and funding</strong></p> <p>All the images have been generated using a tool developed in Unity. This tool will be soon available to enable the generation of new datasets.</p> <p>This research work has been funded by the European Space Agency (ESA) under the FutureEO program and the SD4EO project (Contract No.: 4000142334/23/I-DT), supervised by the ESA Φ-lab.</p> <p><strong>License and attribution</strong></p> <p>This dataset is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p> <p>When using the images from this dataset, please attribute them as follows: "Synthetic images created by the research group ARTEC - IRTIC - University of Valencia".</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.