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13,117 results for “Maps”
A map describing routes used in goose monitoring at Meise Botanic Garden
<p>The geese living in Meise Botanic Garden were surveyed between October 11, 2011 - July 10, 2017. Each day that the geese were monitored one route was chosen at random. This map describes those routes</p> <p><strong>routemap.png</strong></p> <p>A map of the survey routes within the Botanic Garden. CC BY-SA</p> <p>The background map is derived from the OpenStreetMap see <a href="https://www.openstreetmap.org/copyright">https://www.openstreetmap.org/copyright</a></p>
3D neighborhood motion maps (From 6 to 26-neighborhoods)
<p>The file is a sqlite3 database which contains 3D neighborhood motions maps of together with sample points. It was computed while using a variation of the algorithm described in: Pluta K., Moroz G., Kenmochi Y., Romon P. (2016) Quadric Arrangement in Classifying Rigid Motions of a 3D Digital Image. In: Gerdt V., Koepf W., Seiler W., Vorozhtsov E. (eds) Computer Algebra in Scientific Computing. CASC 2016. Lecture Notes in Computer Science, vol 9890. Springer, doi:10.1007/978-3-319-45641-6_27</p> <p>To refer to a version of the algorithm used to compute the file use DOI:10.5281/zenodo.573013 (https://zenodo.org/badge/latestdoi/53963129).</p> <p>To view the content of the databases we refer to: https://github.com/copyme/NeighborhoodMotionMapsTools/tree/master/3DNMMViewerDB</p> <p>Note that in the files' names the prefix 'Nx' refers to different neighborhoods, i.e., N1 – 6-neighborhood, N2 – 18-neighborhood and N3 – 26-neighborhood.</p> <p>This is a consolidating submission for a purpose of a new publication.</p>
Ground truth matches between maps with high disparity
<p>File describing the correct matches between the region of sketch maps and their model map, or between partial sensor built maps.</p> <p>The region were built using MAORIS segmentation algorithm</p>
Segmentation of Nuclei in Histopathology Images by deep regression of the distance map
<p>This dataset has been annonced in our accepted paper "Segmentation of Nuclei in Histopathology Images by deep regression of the distance map" in Transcation on Medical Imaging on the 13th of August.<br> This dataset consists of 50 annotated images, divided into 11 patients.</p> <p> </p> <p>v1.1 (27/02/19): Small corrections to a few pixel that were labelled nuclei but weren't.</p>
Madhya Pradesh : water level map (08/2006)
<p>Madhya Pradesh : water level map (08/2006)</p>
Madhya Pradesh : water level and water fluctuation level maps (2006-2007)
<p>Madhya Pradesh : water level and water fluctuation level maps (2006-2007)</p>
Paramāra study area : geological, geomorphological, lineament, and water resource maps
<p>Paramāra study area : geological, geomorphological, lineament, and water resource maps</p>
A-MAPS: Augmented MAPS Dataset with Rhythm and Key Annotations
<p>The MAPS dataset is one of the most used benchmark dataset for automatic music transcription. We propose here an updated version of the ground truth MIDI files, containing, on top of the original pitch, onset and offsets, additional annotations.</p> <p>The annotations include:</p> <ul> <li> <p>Tempo curve</p> </li> <li> <p>Time signature</p> </li> <li> <p>Durations of notes in fraction of a quarter note (some of them are approximate)</p> </li> <li> <p>Key signature (always written as the major relative)</p> </li> <li> <p>Separate left and right hand staff</p> </li> <li> <p>Text annotations from the score (tempo indications, coda...).</p> </li> </ul> <p>If you use these annotations in a published research project, please cite:<br> Adrien Ycart and Emmanouil Benetos. “A-MAPS: Augmented MAPS Dataset with Rhythm and Key Annotations” <em>19th International Society for Music Information Retrieval Conference Late Breaking and Demo Papers, </em>September 2018, Paris, France.</p> <p>More information is available at: <a href="http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.html">http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.html</a></p> <p><strong>Version 1.2 (2019-03-11):</strong></p> <ul> <li>Add 3 missing MAPS files to the dataset: MAPS_MUS-chpn-e01_ENSTDkCl.mid, MAPS_MUS-chpn-e01_SptkBGAm.mid, MAPS_MUS-chpn-e01_StbgTGd2.mid. <strong>WARNING:</strong> <strong>They correspond to the same piece as MAPS_MUS-chpn_op10_e01_AkPnCGdD.mid</strong></li> <li>Tempo curve slightly edited to avoid unnatural tempo changes (see <a href="http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.html">this page</a> for more info).</li> </ul> <p><strong>Version 1.1 (2018-09-19):</strong></p> <ul> <li>BUG FIXED: in some files, the time signature, key signature and text annotations used to be incorrectly shifted by one quarter note. This is now fixed.</li> <li>Sustain pedal activations are now the same as in the MAPS dataset, they are all in the "Piano left" track.</li> </ul> <p> </p>
InterTVA. Single-trial beta maps for event-related voice localizer.
<p>The InterTVA dataset has been acquired with two main objectives. First, from a neuroscientific perspective, it aims at studying the inter-individual differences observed in people's ability at performing voice perception and voice identification tasks. Secondly, from a methodological perspective, it should allow benchmarking multi-view machine learning methods. Indeed, it includes several MRI modalities: anatomical MRI, diffusion MRI and several sessions of functional MRI -- one resting state run, one event-related voice localizer run (passive listening of vocal and non-vocal sounds), and four runs during which the subject performed a voice identification task.</p> <p>The present dataset contains a pre-processed version of the data acquired during the event-related voice localizer run. A GLM was performed using one regressor for each of the 144 trials, during each of which the participant was passively listening to a vocal or non-vocal stimulus. We therefore provide 144 beta maps for 39 subjects out of the 40 participants (one was excluded for excessive motion). This allows performing group-level MVPA using an inter-subject pattern analysis (ISPA) approach, as described in the following paper:</p> <p>Q. Wang, B. Cagna, T. Chaminade, et S. Takerkart, « Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA », <em>NeuroImage</em>, vol. 204, p. 116205, janv. 2020. https://doi.org/10.1016/j.neuroimage.2019.116205</p> <p>The source code to perform ISPA is also available: http://www.github.com/SylvainTakerkart/inter_subject_pattern_analysis</p> <p>If you use this data, please cite the following paper which describes it exhaustively:</p> <p>V. Aglieri, B. Cagna, P. Belin, S. Takerkart, "Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis", Data in Brief, vol. 29, p. 105170, April 2020. https://doi.org/10.1016/j.dib.2020.105170</p>
Quality and Success in Open Source Software: A Systematic Mapping
<p>Reference_List.pdf includes all of the articles that is related with the study, "Quality and Success in Open Source Software: A Systematic Mapping".</p> <p>Reference_List2.pdf includes one part of Reference_List (37 articles) and all of the articles that included in the study, "A Review on the Relation of Quality and Success in Open Source Software".</p>
Murgia Alta: grassland change map (2012-2015).
<p>Murgia Alta: grassland change map (2012-2015).</p> <p>A change map for the land cover class "GRASSLAND" in "Murgia Alta" PA, obtained by the Cross Correlation Analysis algorithm (CCA) [1,2] considering at time T1 the layer "GRASSLAND" from COPERNICUS Service VHR layer dated 2012 and at time T2 a Sentinel-2A image dated August 7th, 2015.</p> <p>The map was produced at 20 meters spatial resolution and projected in WGS84/UTM33N.</p> <p>The map has binary values where value 1 indicates pixels changed from GRASSLAND to other whereas value 0 indicates No changed/Not considered pixels.</p> <p>The Overall Accuracy (OA) of the map was: OA=94.21%±0.10%.</p> <p>[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).</p> <p>[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, <em>Remote Sensing of Environment</em>, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257</p>
Canary Islands La Palma: pinar canario change map (2003-2015)
<p>A change map for the land cover class "PINAR CANARIO" in "La Palma" PA, obtained by the Cross Correlation Analysis algorithm (CCA) [1,2] considering at time T1 the layer "PINAR CANARIO" from an existing land cover map dated 2003 and at time T2 a Landsat 8 image dated July 8th, 2015.</p> <p>The map was produced at 30 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates pixels changed from PINAR CANARIO to other whereas value 0 indicates No changed/Not considered pixels.</p> <p>[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).</p> <p>[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, Remote Sensing of Environment, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257</p>
Canary Islands La Palma: cloud cover map (2015)
<p>A multi-temporal cloud cover map related to 10 Landsat 8 images in "La Palma" PA from August to December 2015.</p> <p>The 10 layers were related to the following dates: August 9<sup>th</sup>, August 25<sup>th</sup>, September 10<sup>th</sup>, September 26<sup>th</sup>, October 12<sup>nd</sup>, October 28<sup>th</sup>, November 13rd, November 29<sup>th</sup>, December 15<sup>th</sup>, December 31<sup>st</sup>.</p> <p>The maps was produced at 30 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates cloud cover pixels whereas value 0 indicates No cloud cover pixels.</p>
Canary Islands La Palma: pinar disperso change map (2003-2015)
<p>A change map for the land cover class "PINAR DISPERSO" in "La Palma" PA, obtained by the Cross Correlation Analysis algorithm (CCA) [1,2] considering at time T1 the layer "PINAR DISPERSO" from an existing land cover map dated 2003 and at time T2 a Landsat 8 image dated July 8th, 2015.</p> <p>The map was produced at 30 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates pixels changed from PINAR DISPERSO to other whereas value 0 indicates No changed/Not considered pixels.</p> <p>[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).</p> <p>[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, Remote Sensing of Environment, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257</p>
Gran Paradiso: grassland change map (2012-2016)
<p>A change map for the land cover class "GRASSLAND" in "Gran Paradiso" PA, obtained by the Cross Correlation Analysis algorithm (CCA) [1,2] considering at time T1 the layer "GRASSLAND" from COPERNICUS Service VHR layer dated 2012 and at time T2 a Sentinel-2A image dated August 13rd, 2016.</p> <p>The map was produced at 20 meters spatial resolution and projected in WGS84/UTM32N.</p> <p>The map has binary values where value 1 indicates pixels changed from GRASSLAND to other whereas value 0 indicates No changed/Not considered pixels.</p> <p>The Overall Accuracy (OA) of the map was: OA=93.16%±0.14%.</p> <p>[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).</p> <p>[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, <em>Remote Sensing of Environment</em>, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257</p>
Canary Islands La Palma: improductivo change map (2003-2015)
<p>A change map for the land cover class "IMPRODUCTIVO" in "La Palma" PA, obtained by the Cross Correlation Analysis algorithm (CCA) [1,2] considering at time T1 the layer "IMPRODUCTIVO" from an existing land cover map dated 2003 and at time T2 a Landsat 8 image dated July 8th, 2015.</p> <p>The map was produced at 30 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates pixels changed from IMPRODUCTIVO to other whereas value 0 indicates No changed/Not considered pixels.</p> <p>[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).</p> <p>[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, Remote Sensing of Environment, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257</p>
Murgia Alta: Ailantus altissima invasive species presence map (2012)
<p>An Ailanthus altissima invasive species presence map in "Murgia Alta" PA, for 2012, obtained by a two stage algorithm [1]. The first stage considers the deciduous vegetation layer obtained by a multiclass knowledge-based classification of 4 multi-seasonal VHR Worldview-2 images. In the second stage an SVM classifier was used to detect, in a two-classes problem, the Ailanthus invasive species within the deciduous layer vegetation boundaries overimposed on 2 VHR Worldview-2 images. The Worldview-2 images were dated May 19<sup>th</sup>, 2011, October 10<sup>th</sup>, 2011, January 22th, 2012 and July 6<sup>th</sup>, 2012. The October and July images were considered in the second stage.</p> <p>The map was produced at 2 meters spatial resolution and projected in WGS84/UTM33N.</p> <p>The map has binary values where value 1 indicates Ailanthus altissima pixels whereas value 0 indicates No Ailanthus altissima.</p> <p>The Overall Accuracy (OA) of the map was: OA=97.96%±0.14%.</p> <p>[1] C. Tarantino, F. Casella, M. Adamo, R. Lucas, C. Beierkuhnlein, P. Blonda. (2018). “Ailanthus altissima mapping from multi-temporal very high resolution satellite images”, ISPRS <em>Journal of Photogrammetry and Remote Sensing</em>, 147, 90-103, <a href="https://doi.org/10.1016/j.isprsjprs.2018.11.013">https://doi.org/10.1016/j.isprsjprs.2018.11.013</a></p>
Murgia Alta: land cover map (2013)
<p>A land cover map in "Murgia Alta" PA, for 2013, obtained by considering a Landsat intra-annual time series of NDVI spectral index computed for 27 images. A Maximum Likelihood classifier was used for a 13 classes problem. The images were preprocessed, atmospherically corrected, masked for clouds and shadow clouds cover and interpolated for the gaps filling.</p> <p>The map was produced at 30 meters spatial resolution and projected in WGS84/UTM33N.</p> <p>The Overall Accuracy (OA) of the map was: OA=83.62%±0.90%.</p> <p>The map has 14 values related as follows in LCCS-FAO taxonomy:</p> <p>Value 0 = Unclassified</p> <p>Value 1 = A12/A1.D1.E2</p> <p>Value 2 = A12/A1.D1.E1</p> <p>Value 3 = A12/A1.D2.E1</p> <p>Value 4 = A12/A2.A6</p> <p>Value 5 = A11/(A1ORA2).A7.A9</p> <p>Value 6 = A11/A1.A7.A10</p> <p>Value 7 = A11/A2.A7.A10</p> <p>Value 8 = A11/A3.A4.S3</p> <p>Value 9 = A11/A3.A4.S7</p> <p>Value 10 = A11/A3.A4.S5(LEGUMES)</p> <p>Value 11 = A11/A3.A4.S5(VEGETABLES)</p> <p>Value 12 = B15/A2.A6</p> <p>Value 13 = BURNED AREAS</p>
Canary Islands La Palma: cloud cover map (2016)
<p>A multi-temporal cloud cover map related to 5 Sentinel-2A images in "La Palma" PA from December 2015to April 2016.</p> <p>The 5 layers representing the cloud cover (as OR among different temporal and spatial acquisitions in each month) were related to the following dates: December 2015, January 2016, February 2016, March 2016 and April 2016 .</p> <p>The maps was produced at 60 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates cloud cover pixels whereas value 0 indicates No cloud cover pixels.</p>
Murgia Alta: grassland change map (2006-2014).
<p>A change map for the land cover class "GRASSLAND" in "Murgia Alta" PA, obtained by the Cross Correlation Analysis algorithm (CCA) [1,2] considering at time T1 the layer "GRASSLAND" from an existing land use map dated 2006 (provided by Puglia Region) and at time T2 a Landsat 8 image dated August 10th, 2014.</p> <p>The map was produced at 30 meters spatial resolution and projected in WGS84/UTM33N.</p> <p>The map has binary values where value 1 indicates pixels changed from GRASSLAND to other whereas value 0 indicates No changed/Not considered pixels.</p> <p>The Overall Accuracy (OA) of the map was: OA=95.70%±0.30%.</p> <p>[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).</p> <p>[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, <em>Remote Sensing of Environment</em>, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257</p>
ScienceDex guides
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