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17 results for “spot mapping.”
Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps
<p><strong>Title:</strong></p> <p>Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K.; Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875965">https://doi.org/10.5281/zenodo.7875965</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast.</p> <p>The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements.</p> <p>For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 750 × 750 m</p> <p>File name: DataPoorAreas_MMR2000.shp</p> <p>File format: ESRI Shapefile</p> <p>Spatial reference: MMR2000_46N</p>
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
<p>1. Attached files: </p> <p>500_100_fmincon_0.95_1.mat <br /> Experiment with 500 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>500_100_heuristic_0.95_1.mat<br /> Experiment with 500 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_70_fmincon_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, exact algorithm.</p> <p>2000_70_heuristic_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.9_1.mat<br /> Experiment with 2000 users, 100ms max response time, 90% availability, exact algorithm.</p> <p>2000_100_heuristic_0.9_1.mat <br /> Experiment with 2000 users, 100ms max response time, 90% availability, our algorithm.</p> <p>2000_100_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>2000_100_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, exact algorithm.</p> <p>2000_100_heuristic_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, our algorithm.</p> <p>2000_300_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, exact algorithm.</p> <p>2000_300_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, our algorithm.</p> <p>10000_100_fmincon_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>10000_100_heuristic_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2. Data format:</p> <p>MATLAB data format, can be load from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Map. Ancient Vidarbha showing find-spots of Vākāṭaka inscriptions
<p>Map. Ancient Vidarbha showing find-spots of Vākāṭaka inscriptions (red = copper plate charter, brown = stone inscription).</p>
Map. Ancient Karṇāṭaka showing find-spots of Kadamba inscriptions
<p>Map. Ancient Karṇāṭaka showing find-spots of Kadamba inscriptions. © Institute of Oriental Studies of the Russian Academy of Science</p>
Map. Ancient Vidarbha showing find-spots of Vākāṭaka inscriptions
<p>Map. Ancient Vidarbha and neighbouring regions showing find-spots of Vākāṭaka inscriptions. © Institute of Oriental Studies of the Russian Academy of Science</p>
Map. Historic map of the Vidarbha region showing find spots of Vākāṭaka inscriptions
<p>Map. Historic map of the Vidarbha region showing find spots of Vākāṭaka inscriptions</p>
Io hot spots map derived by Juno/JIRAM orbits: 10, 11, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33
<p>Io hot spots map GIS shapefile derived by Juno/JIRAM orbits: 10, 11, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33</p>
Sequence of snow maps produced from Sentinel-2 type of observations (SPOT-5 Take 5) over the Deux Alpes and Alpe d'Huez ski resorts
<p>This is a series of snow cover maps between April 11 and September 8, 2015 over a region that covers the Deux Alpes and Alpe d'Huez ski resorts in France. The snow maps were produced from a SPOT-5 Take 5 images using the "Let-it-snow" processor (v1.0, June 2016: http://tully.ups-tlse.fr/grizonnet/let-it-snow).</p> <p>The SEB folder contains 20 GeoTiff at 10 m résolution in Lambert-93 projection system coded as follows (cf. http://tully.ups-tlse.fr/grizonnet/let-it-snow#products-format):</p> <p> 0: No-snow<br> 100: Snow<br> 205: Cloud including cloud shadow<br> 254: No data</p> <p>The "anim_no05jul_opt" animated gif was produced from these data after performing a simple temporal interpolation given by the following rules:</p> <ul> <li>If a pixel masked by a cloud was marked as snow in the preceding image and in the following image, then it is reclassified as a snow pixel.</li> </ul> <ul> <li>If a pixel masked by a cloud was marked as no-snow in the preceding image and in the following image, then it is reclassified as a no-snow pixel.</li> </ul> <p>The July 05 image was removed from the animation due to a cloud/snow confusion. This type of error should be avoided with Sentinel-2 data thanks to an additional "high cloud" test (see http://tully.ups-tlse.fr/grizonnet/let-it-snow/blob/master/doc/tex/ATBD_CES-Neige.pdf).</p> <p>These data were featured in this blog post : Gascoin, S. "Monitoring the snow cover in ski resorts using Sentinel-2" (19-Sep-2016) http://www.cesbio.ups-tlse.fr/multitemp/?p=8676</p>
Figure 1. - World map representing all the locations mentioned in the dataset. Areas of particular interest are represented with the same colour (⬤ Madagascar, ⬤ Western Indian Ocean, ⬤ Papuasia, ⬤ New Caledonia, ⬤ South Pacific). Grey spots gather all the other locations.
Figure 1. - World map representing all the locations mentioned in the dataset. Areas of particular interest are represented with the same colour (⬤ Madagascar, ⬤ Western Indian Ocean, ⬤ Papuasia, ⬤ New Caledonia, ⬤ South Pacific). Grey spots gather all the other locations.
Figure S4.10. Dynamic weekly suitability map for Atlantic spotted dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.
<p>Dynamic weekly suitability map for Atlantic spotted dolphins around central group islands (Pico, Faial, São Jorge and Terceira), Azores.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Figure S4.9. Dynamic weekly suitability map for Atlantic spotted dolphins around São Miguel island, Azores.
<p>Dynamic weekly suitability map for Atlantic spotted dolphins around São Miguel island, Azores.</p>
Map of the Mediterranean region, showing the find spots of imperial portraits
<p>The figure presented here includes a map of the Mediterranean region, showing the find spots of sculptural portraits of Roman emperors (mostly carved from marble or casted in bronze) that were collected for the purposes of analyzing the representation of Roman emperors in freestanding sculpture. PhD dissertation: S. Heijnen (2022), Portraying Change: The Representation of Roman Emperors in Freestanding Sculpture (ca. 50 BC - ca. 400 AD). Dissertation. Radboud University.</p>
MAP. Stations around New Caledonia: SPANBIOS in yellow squares; EXBODI in red spots. in Additional records of bathyal ascidians (Tunicata) from the New Caledonia region
MAP. Stations around New Caledonia: SPANBIOS in yellow squares; EXBODI in red spots.
Mapping the Binding Hot Spots and Transient Binding Pockets on V-domain Immunoglobulin Suppressor of T Cell Activation (VISTA) Protein Surface
Open the record for dataset details and reuse information.
Text-fig. 1. The geographic position of the localities mentioned in the text. A – position within the Czech Republic. B – Detailed map of the area. Subsilesian Unit: 1 – Kelč, 2 – Špičky, 3 – Horní Těšice; Silesian Unit: 4 – Loučka, 5 – Osíčko, 6 – Rožnov pod Radhoštěm; Ždánice Unit: 7 – Bohuslavice, 8 – Jestřabice, 9 – Kožušice, 10 – Křepice, 11 – Litenčice, 12 – Mouchnice, 13 – Nikolčice, 14 – Nítkovice, 15 – Nosislav, 16 – Židlochovice. The distribution of the units according to Čtyřoký and Stráník (1995). C – map of the Osíčko vicinity with distribution of the Silesian Unit sediments (gray spots) and collecting points (P1 and P2). The distribution of the Silesian Unit sediments according to Stráník (1999). in An Annotated List Of The Oligocene Fish Fauna From The Osíčko Locality (Menilitic Fm.; Moravia, The Czech Republic)
Text-fig. 1. The geographic position of the localities mentioned in the text. A – position within the Czech Republic. B – Detailed map of the area. Subsilesian Unit: 1 – Kelč, 2 – Špičky, 3 – Horní Těšice; Silesian Unit: 4 – Loučka, 5 – Osíčko, 6 – Rožnov pod Radhoštěm; Ždánice Unit: 7 – Bohuslavice, 8 – Jestřabice, 9 – Kožušice, 10 – Křepice, 11 – Litenčice, 12 – Mouchnice, 13 – Nikolčice, 14 – Nítkovice, 15 – Nosislav, 16 – Židlochovice. The distribution of the units according to Čtyřoký and Stráník (1995). C – map of the Osíčko vicinity with distribution of the Silesian Unit sediments (gray spots) and collecting points (P1 and P2). The distribution of the Silesian Unit sediments according to Stráník (1999).
FABDEM V1-0 adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps
<p><strong>Title:</strong></p> <p>FABDEM V1-0 adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): FABDEM V1-0 adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875856">https://doi.org/10.5281/zenodo.7875856</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>This digital elevation model is a version of the FABDEM V1-0 of Hawker et al. (2022; <a href="https://doi.org/10.1088/1748-9326/ac4d4f">https://doi.org/10.1088/1748-9326/ac4d4f</a>) that was adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps (scale 1:50,000) published in 2014 while source data was compiled between 2000 and 2004. The FABDEM V1-0 (Laurence Hawker, Jeffrey Neal (2021): FABDEM V1-0. <a href="https://doi.org/10.5523/bris.25wfy0f9ukoge2gs7a5mqpq2j7">https://doi.org/10.5523/bris.25wfy0f9ukoge2gs7a5mqpq2j7</a>; CC BY-NC-SA 4.0) was projected to the Myanmar 2000 datum and clipped to the Ayeyarwady Delta region of interest. The vertical reference of the FABDEM V1-0 was transformed to EGM96 before applying a conversion to continuous mean sea level based on mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96). Subsequently, inland water bodies were masked using the water body mask of the Copernicus DEM (Airbus Defence and Space, 2020: Copernicus Digital Elevation Model Product Handbook Version 3.0, Airbus, 38 pp.) and cell values with an elevation of more than 7 m below mean sea level were removed.</p> <p>From the topographic maps, the local spot heights outside of areas masked in the AD-DEM (Seeger et al. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Doi; CC-BY 4.0) were subtracted from elevation values of the FABDEM V1-0 at the same locations (~3630 spot heights). Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate the height residuals and export the raster data at ~30 m grid cell resolution. The mask layer of the AD-DEM was applied to the height residual raster in order to correct for interpolations in areas of data paucity. Subsequently, the interpolated height residuals were subtracted from the pre-processed FABDEM. In delta areas outside the masked regions of the height residual raster, the elevation of the pre-processed FABDEM was maintained (see the figure in the uploaded dataset).</p> <p>For further information on processing of local and global elevation data for the Ayeyarwady Delta in Myanmar, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: FABDEM_EGM96_MDT_AD_MMR2000_maskedCop_min7_adjusted_AD.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 30 × 30 m</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.