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13,117 results for “Maps”
FIGURE 41. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 41. Distribution maps for A, Pauridia capensis from Northern, Western and Eastern Cape; B, P. canaliculata from Western Cape, South Africa.
FIGURE 27. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 27. Distribution maps for A, Pauridia aquatica from Northern, Western and Eastern Cape; B, P. alba from Western Cape, South Africa.
FIGURE 24. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 24. Distribution maps for A, Pauridia nana from Northern Cape; B, P. pusilla from Western Cape, South Africa.
FIGURE 22. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 22. Distribution maps for A, Pauridia breviscapa from Northern Cape; B, P. umbraticola from Western Cape, South Africa.
FIGURE 10. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 10. Distribution maps for A, Pauridia flaccida from Western Cape; B, P. maryae from Western and Eastern Cape, South Africa.
FIGURE 7. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 7. Distribution maps for A, Pauridia etesionamibensis from Namibia and Northern Cape, South Africa; B, P. pygmaea from Western Cape, South Africa.
FIGURE 17. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 17. Distribution maps for A, Pauridia minuta from Western and Eastern Cape; B, P. longituba from Western Cape, South Africa.
FIGURE 19. Distribution maps for A in A taxonomic revision of the genus Pauridia (Hypoxidaceae) in southern Africa
FIGURE 19. Distribution maps for A, Pauridia scullyi from Northern Cape; B, P. maximiliani from Western Cape, South Africa.
MAP 3 in Integrative taxonomic revision of the Metarctia Walker, 1855 subgenus Hebena Walker, 1856, with descriptions of six new species and one new subspecies (Lepidoptera: Erebidae: Arctiinae: Syntomini)
MAP 3. Distribution of Metarctia (Hebena) smithi smithi, M. (H.) smithi transvallesiana and M. (H.) haraldsulaki
MAP 2 in Integrative taxonomic revision of the Metarctia Walker, 1855 subgenus Hebena Walker, 1856, with descriptions of six new species and one new subspecies (Lepidoptera: Erebidae: Arctiinae: Syntomini)
MAP 2. Distribution of Metarctia (Hebena) rubra, M. (H.) manfredi, M. (H.) kelleni, M. (H.) elleni and M. (H.) brigitta.
Estimation of the Uncertainties Introduced in Thermal Map Mosaic: A Case of Study with PIX4D Mapper Software
<p>Available data sets, used to analyse problems related to thermal mapping obtained from thermal data acquired from unmanned aerial systems (UAS) equipped with thermal cameras. We focused on an accurate analysis of uncertainties introduced by the PIX4D Mapper software.</p>
A Comprehensive Dataset of Surface Deformation Satellite Maps of The Valley of Toluca, Mexico
<p>A dataset of 1121 ground deformation maps of the Toluca Valley (VT) in Mexico is presented. The given deformation maps cover the VT ground evolution over eight years, from October 2014 to December 2022.</p><p>The VT is located within the State of Mexico between the coordinates of 19°03' to 19°35' north latitude and 99°19' to 99°54' west latitude. The highest altitude point of the VT is the volcano "Nevado de Toluca or Xinantécatl" which is 4,340 meters above sea level. In comparison, the rest of the valley has an average elevation of 2640 meters above sea level. The soil of the VT is mainly composed of Andosols, Feozems, Vertisols, Luvisols, and Cambisols.</p><p>For generating the deformation maps, 240 radar images obtained by the Sentinel 1-A and Sentinel 1-B satellites were used. From these 240 radar images, 2169 pairs of images were formed with a temporal difference of 1, 3, 6, and 12 months. The Differential Interferometric Synthetic Aperture Radar technique was applied to these pairs using SNAP software, resulting in 2169 deformation maps in BEAM-DIMAP format. However, due to limited storage space, only 1121 maps with a coherence greater than 0.299 are presented. Complimentary, a CSV file with the deformation map statistics is also provided.</p>
Swift/BAT-GUANO upper limits fits map for S230914ak
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Swift/BAT-GUANO upper limits map for S230914ak
<p>Swift/BAT-GUANO upper limits fits map for S230914ak. The solid and dashed lines indicate the 90% and 50% GW contour levels, respectively.</p>
2020 forest age map for China with 30 m resolution
<p>We constructed a framework for estimating China’s forest age by combining remote sensing time series analysis and machine learning algorithms based on over 50 000 field measurements and a remote sensing dataset. We use the proposed framework to generate a spatially explicit forest age map for China for the year 2020 and with 30 m resolution. The map is validated by comparison with independent field plots, which produces an R<sup>2</sup> for the estimated forest-age map ranging from 0.51 to 0.63. Nationally, the average forest age is 56.1 years (standard deviation =32.7 years).</p>
Guangdong Lightning Mapping Array: Errors Evaluation and Preliminary Results
<p>The attached is the dataset assoicated with the paper titled "Guangdong Lightning Mapping Array: Errors Evaluation and Preliminary Results" which was submitted to Journal of Earth and Space Science.</p> <p>The Guangdong Lightning Mapping Array (GDLMA), as the first LMA in China, was deployed in Guangzhou, Guangdong province, China, in November 2018 by the Chinese Academy of Meteorological Sciences (CAMS) and New Mexico Institute of Mining and Technology (NMT). An evaluation was conducted using Monte Carlo and an aircraft track. The average timing uncertainty of GDLMA is 35 ns based on the distributions of reduced chi-square values. The detection efficiency of radiation sources within a 100 km range of the center of GDLMA exceeds 90%. Based on the aircraft track, at an altitude of 4-5 km within the network, the average horizontal error was 13 m and the average horizontal error was 41 m, consistent with the Monte Carlo results. Location errors outside the network exhibit noticeable directionality. The ability to characterize lightning channels varies with different location errors. In locations that are far from the network center, only the basic structure of lightning flash can be presented, while closer to the network, the flash channel structure can be mapped well. Compared with Low-to-Mid Frequency E-field Detection Array (MLFEDA), they were generally similar in overall structure, and some lightning flash characteristics such as flash duration and convex hull area exhibited consistency. However, GDLMA demonstrated better channel characterization capability, while MFLEDA performed better in processes such as leader/return strokes.</p>
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
<p><strong>Paper Accept by WACV 2024</strong></p> <p>[paper, supp] [<a href="https://arxiv.org/abs/2309.01907">arXiv</a>]</p> <p><strong>Overview</strong></p> <p>Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages.</p> <pre><strong>Description</strong> ------------ This dataset has been designed for land cover mapping and building change detection tasks. File Structure and Content: --------------------------- 1. **1024.zip**: - Contains images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `small-pre-images`: Images with a minor off-nadir angle difference compared to post-event images. - `big-pre-images`: Images with a large off-nadir angle difference compared to post-event images. - `cd_mask`: Ground truth for the building change detection task. 2. **512-1.zip**, **512-2.zip**, **512-3.zip**: - Contains images of size 512x512 with a GSD of 0.3-0.6m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `pre-event` folder: Images for the pre-event phase. - `cd-mask`: Ground truth for building change detection. Land Cover Mapping Class Grep Map: ---------------------------------- class_grey = { "Bareland": 1, "Rangeland": 2, "Developed Space": 3, "Road": 4, "Tree": 5, "Water": 6, "Agriculture land": 7, "Building": 8, }</pre> <p><strong>Reference</strong></p> <p>@misc{song2023syntheworld,<br> title={SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection}, <br> author={Jian Song and Hongruixuan Chen and Naoto Yokoya},<br> year={2023},<br> eprint={2309.01907},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> }</p> <p> </p>
MAP 1 in Click beetles (Coleoptera: Elateridae) of Khuzestan province (Iran): with three new species, Craspedostethus izehensis sp.n., Dicronychus khuzestanicus sp. n. and Gurjevelater catei sp. n.
MAP 1. The map of location of climate types of Khuzestan province based on the "Agroclimatical Zoning (ACZ) of Iran", UNESCO. Acronyms of the climate types are defined in the table below the maps. Location of the Khuzestan province marks with red color on the map of Iran.
Swift/BAT-GUANO upper limits map for S230920al
<p>Swift/BAT-GUANO upper limits fits map for S230920al. The solid and dashed lines indicate the 90% and 50% GW contour levels, respectively.</p>
Swift/BAT-GUANO upper limits fits map for S230920al
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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.