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300 results for “Urban area”
Figure 4 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 4. Length frequency distribution of the fibers.
Figure 2 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 2. Plastic fibres from fish intestines.
SD4EO: AI-based synthetic solar panel dataset on urban areas
<p>This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>. It consists of aerial images of urban areas that mimic certain regions inside Poitiers, Bordeaux and Toulouse.<br>The images have been synthetized with a generative diffusion model, conditioned with schematic maps, and later augmented to include solar panels in the most sunlighted portions of the building roofs.</p> <p>Each entry has 4 types of files:</p> <ul> <li>A PNG image containing the binary mask with pixel-by-pixel segmentation of areas where solar panels have been installed.</li> <li>A PNG image which shows a variant of the synthetic image, each featuring differently placed panels with white support structures.</li> <li>A TXT file containing the locations of axis-aligned bounding boxes in YOLOv8 format (also compatible with YOLOv5). These are included only if panels have been added to the image, with one file per panel variant, to facilitate YOLO training without needing to modify its Dataset class for file reading.</li> <li>An NPZ file storing the coordinates of the bounding boxes aligned with the solar panels (not with the axes), intended for use in more advanced object detection models like YOLOv8.1 or Mask R-CNN.</li> </ul> <p>The dataset includes 46,872 synthetic images, which have been further enhanced by adding solar panels in strategic locations. The complete dataset with metadata has been subdivided into 18 ZIP files (each containing a different subfolder), totalling 6.5GB. This division was made to avoid overloading the storage system. By distributing the 156,127 files across multiple folders, we prevent potential issues on users' computers related to exceeding the maximum number of inodes in the file system and/or the operating system.</p> <p>The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.</p>
Imaging subsurface structure of an urban area based on Diffuse-Field Theory concept using seismic ambient noise
<p>Ambient noise data for a small urban area of NER India. The data set constitute all the raw files that were used for figures in the paper by Bora et al.</p>
Automated Truck Lanes in Urban Area for Through and Cross Border Traffic
<p>Autonomous trucks will soon be operational across our nation’s roadways. Yet, it is unclear how the design of our highway infrastructure should be modified to accommodate autonomous trucks, to enable them to operate in such a way to maximize the economic, capacity and safety benefits. The University of Texas at El Paso (UTEP) is proposing to develop and demonstrate, through microscopic traffic simulations, the concept of operations of autonomous truck lanes along the interstate freeways. Using the I-10 Freeway in the El Paso, TX region as the testbed, the research team will: (i) assess the existing structural, geometric and traffic designs in handling fully automated trucks, including the entrances, exits, and connectors; (ii) perform microscopic traffic simulations at critical locations to demonstrate design issues and the recommended design improvements; (iii) conduct a preliminary cost estimation on such infrastructure improvements.</p>
Data for "How to use scale invariant properties of imperviousness in urban areas to handle missing data ?"
<p>The data set corresponds the data presented in the data paper : “How to use scale invariant properties of imperviousness in urban areas to handle missing data ?“ which has been submitted to Water Resources Research ” (https://agupubs.onlinelibrary.wiley.com/journal/19447973).</p> <p>It corresponds to :</p> <p>- the rainfall data collected on 2019-06-02 with 5 min and 30 s time steps by a disdrometer installed on the roof of Ecole des Ponts ParisTech building.</p> <p>- land use distribution for the Jouy-en-Josas catchment (1 = forest, 2= road, 3=Grass, 4=building, 5=Gully, 6=missing data), with pixel size of 10 m and 2 m.</p> <p>More details can be found in the file and in the paper.</p>
Figure 1 in On some avifauna from Jojari River - A Wastewater Wetland in Semi-Urban Area of Jodhpur City, Rajasthan
Figure 1. Location of Jojari River in Jodhpur city of Rajasthan.
Figure 2 in On some avifauna from Jojari River - A Wastewater Wetland in Semi-Urban Area of Jodhpur City, Rajasthan
Figure 2. Number of order of bird species found in Jojari River, Rajasthan.
Figure 1 in Roost characteristics and habitat preferences of Indian flying fox (Pteropus giganteus) in urban areas of Lahore, Pakistan
Figure 1. GIS-based map of Jinnah garden showing roosts of Indian flying fox populations.
Figure 1 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 1. Location of Kharkiv on the map of Europe, and a general view of the city area.
Figure 5 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 5. Sex ratio in adult individuals of N. noctula during 2013 in Kharkiv.
Figure 6 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 6. Sex ratio in first-year individuals of N. noctula during 2013 in Kharkiv.
Figure 7 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 7. Sex ratio of E. serotinus for all age groups together during 2013 in Kharkiv.
Figure 3. A in Prevalence of intestinal nematodes infection in school children of urban areas of district Lower Dir, Pakistan
Figure 3. A. lumbricoides fertile egg.
Figure 2. A in Prevalence of intestinal nematodes infection in school children of urban areas of district Lower Dir, Pakistan
Figure 2. A. lumbricoides unfertile egg.
Figure 6 in Prevalence of intestinal nematodes infection in school children of urban areas of district Lower Dir, Pakistan
Figure 6. Trichuris trichiura.
Figure 5 in Prevalence of intestinal nematodes infection in school children of urban areas of district Lower Dir, Pakistan
Figure 5. Trichuris trichiura.
Operational Dataset for Phased Array Radar Network for Natural Hazard Monitoring and Warnings in Urban Environments over the Greater Bay Area, China
<p>This is the dataset for the BAMS paper: Operational Phased Array Radar Network for Natural Hazard Monitoring and Warnings in<br>Urban Environments over the Greater Bay Area, China</p> <p>Due to policy restrictions, long-term data cannot be openly shared. Access to these data requires further arrangements through a formal agreement. For inquiries regarding data access, please contact us to discuss the terms and conditions.</p>
Comparison among three different Digital Surface Models and their respective hydraulic outcomes in the flood-prone urban area of Navaluenga (Ávila, Spain)
<p>Three different Digital Surface Models (DSMs) generated from LiDAR data are presented. The LiDAR information has been considered as raw data (DSM3) and subjected to some transformations to better represent the urban environment (DSM1). DSM2 is an intermediate state between DSM1 and DSM3. </p> <p>On the other hand, a hydraulic model has been run for each DSM and for two return periods (25 and 500 years), obtaining in all cases the graphical outputs of depths, velocities, Froude numbers and hazard. </p> <p>The different DSMs are named DSM1, DSM2 and DSM3, which can be downloaded in TIN format. The hydraulic outputs associated with the different DSMs can be downloaded in raster format and are named as follows: the Digital Surface Model to which it refers, the return period considered and the type of hydraulic output (depth, velocity, Froude number and hazard).</p> <p>DSM1: Digital Surface Model 1 (TIN format).<br> dsm1_25depth: Depths obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25haz: Hazard obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format).<br> dsm1_25veloc: Velocities obtained by considering the DSM1 and the flow associated with the 25-years return period (raster format). <br> dsm1_500depth: Depths obtained when considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500froud: Froude numbers obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500haz: Hazard obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).<br> dsm1_500veloc: Velocities obtained by considering the DSM1 and the flow associated with the 500-years return period (raster format).</p> <p>DSM2: Digital Surface Model 2 (TIN format).<br> dsm2_25depth: Depths obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25haz: Hazard obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format).<br> dsm2_25veloc: Velocities obtained by considering the DSM2 and the flow associated with the 25-years return period (raster format). <br> dsm2_500depth: Depths obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500froud: Froude numbers obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500haz: Hazard obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).<br> dsm2_500veloc: Velocities obtained by considering the DSM2 and the flow associated with the 500-years return period (raster format).</p> <p>DSM3: Digital Surface Model 2 (TIN format).<br> dsm3_25depth: Depths obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25haz: Hazard obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format).<br> dsm3_25veloc: Velocities obtained by considering the DSM3 and the flow associated with the 25-years return period (raster format). <br> dsm3_500depth: Depths obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500froud: Froude numbers obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500haz: Hazard obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).<br> dsm3_500veloc: Velocities obtained by considering the DSM3 and the flow associated with the 500-years return period (raster format).</p>
Comparing the Influence of Global Warming and Urban Anthropogenic Heat on Extreme Precipitation in Urbanized Pearl River Delta Area Based on WRF Dynamical Downscaling
<p>The simulation outputs from the Weather Research and Forecasting (WRF) v3.8.1 coupled with single layer urban canopy model from three experiments (HIST_AH300, HIST_AH0, and RCP85_AH300).</p> <p>Variables include hourly precipitation, wind, specific humidity, relative humidity, convective available potential energy, convective inhibition, temperature, model height, land use land cover, and topography.</p> <p> </p>
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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.