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88 results for “High-resolution images”

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zenodo48/100

Wollestraat 29, Bruges (BE): high-resolution images of dry wood cores taken form a medieval floor joists, for tree-ring analysis

<ul><li>Dry-wood cores taken from historical timbers of a floor joists in the medieval building 'De Oude Steen', Wollestraat 29, Bruges (Belgium).</li><li><a href="https://id.erfgoed.net/erfgoedobjecten/29956 ">https://id.erfgoed.net/erfgoedobjecten/29956 </a></li><li>The cores were sampled at 22/02/2023 with a dry-wood borer (internal diameter 12 mm, external diameter 19 mm).</li><li>The cores were surfaced with increasingly finer sanding papers, from P60 up to P4000.</li><li>The cores were photograpphed with a Sony alpha7R IV full frame camera and FE 90 mm F/2.8G macro lens.</li><li>The<a href="https://www.wsl.ch/en/services-produkte/skippy/"> Skippy</a> system served as the image capturing platform.</li><li>The individual digital macro-photos were stitched with PTGui into a mosaic image (.tiff).</li><li>The mosaic images have a resolution of ~4 µm.</li></ul>

opencc-by-4.0Nov 2023View details →

zenodo48/100

High-resolution images from a low-cost imaging device for hyphae in soil

This dataset contains high-resolution images produced by a low-cost imaging device for hyphae in soil called Hyphascope. Using a digital microscope camera (DMC; 600× magnification), the device takes detailed images (0.83 × 0.62 mm imaged area) of a soil profile from evenly spaced camera positions within a user-defined volume. Repeated imaging of a soil profile with Hyphascope enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae. Individual images were combined using the Grid/Collection stitching plugin of the Fiji distribution of imageJ (Preibisch et al. 2009). All images are supplied in the JPG format to limit their file size. For more details on the assembly and application of Hyphascope, see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE.     <div> <h2>Image set 1: 10 × 10 mm soil profile area at 20 - 30 mm soil depth</h2> Imaged at 0.65 μm px-1 (39200 dpi)* in a Quercus serrata grove on 2023/05/25 during a period of high hyphal density in the soil. <h3>Individual images (18 rows × 14 images each)</h3> <ul> <li> set1_foc00000.zip (focus depth 0 mm) </li> </ul> <h3>Combined images</h3> <ul> <li> set1_foc00000_combined.jpg (focus depth 0 mm) </li> </ul> <h2>Image set 2: 5 × 5 mm soil profile area at 100 - 105 mm soil depth</h2> Imaged at 0.52 μm px-1 (49000 dpi) in a Quercus serrata grove on 2023/09/25. <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> set1_foc00000.zip (focus depth 0 mm) </li> <li> set1_foc00025zip (focus depth 0.025 mm) </li> <li>set1_foc00050.zip (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> set1_foc00000_combined.jpg (focus depth 0 mm) </li> <li> set1_foc00025_combined.jpg (focus depth 0.025 mm) </li> <li>set1_foc00050_combined.jpg (focus depth 0.05 mm)</li> </ul> <h2>Image set 3: 5 × 5 mm soil profile area at soil surface level</h2> Imaged at 0.52 μm px-1 (49000 dpi) in a Quercus serrata grove on 2023/10/14 during a rain event. <h3>Individual images (9 rows × 7 images each)</h3> <ul> <li> set2_foc00000.zip (focus depth 0 mm) </li> <li> set2_foc00025zip (focus depth 0.025 mm) </li> <li>set2_foc00050.zip (focus depth 0.05 mm)</li> </ul> <h3>Combined images</h3> <ul> <li> set2_foc00000_combined.jpg (focus depth 0 mm) </li> <li> set2_foc00025_combined.jpg (focus depth 0.025 mm) </li> <li>set2_foc00050_combined.jpg (focus depth 0.05 mm)</li> </ul>     *Units of imaging resolution: <ol> <li>pixel width (μm px-1), i.e. the horizontal or vertical distance on the imaged surface covered by a single pixel; </li> <li>dots per inch (dpi), i.e. the number of pixels along a horizontal or vertical distance of 25.4 mm on the imaged surface.</li> </ol> </div>

opencc-by-4.0Mar 2024View details →

zenodo44/100

Design files for a low-cost high-resolution imaging device for hyphae in soil

This dataset contains the stereolithography (STL) files for the 3D-printed and cut parts of a low-cost high-resolution imaging device for hyphae in soil called Hyphascope. The design of Hyphascope was adopted from the 3D printer i3 MK3S+ by Prusa Research, with a digital microscope camera (DMC; 600× magnification) replacing the filament extruder. Repeated imaging of a soil profile with the imaging device enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae. The parts were created and modified using <a href="https://www.freecad.org">FreeCAD</a> (version 0.20). STL files for the original parts are distributed under the Creative Commons Attribution 4.0 International License, STL files for the remixed parts under the GNU General Public License v2.0. For a detailed description on how to prepare and assemble the parts see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE.   <div> <h2>STL files of 3D-printed parts</h2> <h3>Original parts</h3> </div> <div> <div> <ul> <li>dmc-attachment.stl</li> <li> <div>dmc-attachment-gear.stl</div> </li> <li>dmc-attachment-gear-wider.stl (optional part) </li> <li>dmc-holder-back.stl</li> <li>dmc-holder-front.stl</li> <li>f-axis-motor-gear.stl</li> <li>f-axis-spring-end.stl</li> <li>f-axis-tighteners.stl</li> <li>frame-foot-inserts.stl</li> <li> <div>frame-foot-left.stl</div> </li> <li> <div>frame-foot-right.stl</div> </li> <li> <div>frame-hat.stl</div> </li> <li> <div>frame-hat-insert.stl</div> </li> </ul> </div> <h3>Remixed parts originally designed by Prusa Research</h3> The five parts below are remixed from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts">i3 MK3S+ printable parts</a> and re-distributed under the <a href="http://www.gnu.org/licenses/old-licenses/gpl-2.0.html">GNU General Public License v2.0</a>. </div> <ul> <li> <div>dmc-carriage-back.stl (Remix of x-carriage-back.stl; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</div> </li> <li>dmc-carriage-front.stl (Remix of x-carriage.stl; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</li> <li>x-end-idler-mod.stl (Remix of x-end-idler.stl; the height was increased by 20 mm.)</li> <li>x-end-motor-mod.stl (Remix of x-end-motor.stl; the height was increased by 20 mm and the counterbores of the three motor screws were moved to the opposite side.)</li> <li>z-axis-top-mod.stl (Remix of z-axis-top.stl; 14.8 mm-long spacers were added.)</li> </ul> <h3>Parts designed by Prusa Research</h3> <ul> <li> <div>z-axis-bottom.stl (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> <li> <div>z-screw-cover.stl (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> </ul>   <div> <h2>STL files of cut parts</h2> <h3>Original parts</h3> </div> <ul> <li>box-bottom.stl</li> <li>box-hook.stl</li> <li> <div>box-lid.stl</div> </li> <li> <div>box-lid-frame.stl</div> </li> <li>box-lid-valve-base.stl</li> <li>box-wall.stl</li> <li>box-wall-cables.stl</li> <li> <div>frame.stl</div> </li> </ul>

opencc-by-4.0Mar 2024View details →

zenodo44/100

Semantic segmentation model of construction waste landfill based on high-resolution satellite images

CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module. You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>. <h2>Training details</h2> The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters: <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>

opencc-by-4.0Apr 2024View details →

Figshare44/100

High-Resolution Quantitative Phase Imaging of Plasmonic Metasurfaces with Sensitivity down to a Single Nanoantenna_experimental dataset

This dataset shares the data presented in the paper "Geometric-phase microscopy for high-resolution quantitative phase imaging of plasmonic metasurfaces with sensitivity down to a single nanoantenna" available in open access under <a href="https://doi.org/10.5281/zenodo.3355170">10.5281/zenodo.3355170</a>. The archive contains experimental files titled with references to the figures as they appear in the paper.

opencc-by-4.0Dec 2017View details →

zenodo44/100

Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

File 1: raw_data_BOULDERING.zip Size: 8.8 GB Summary: It contains all of the rasters (planetary images) and labeled boulders (raw data): <ul> <li> a boulder-mapping file, which is the manually digitized outline of boulders. </li> <li> a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted. </li> <li> a global-tiles file, which shows all of the image patches within a raster. </li> </ul> There are multiple locations/images per planetary body. Structure: <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif ├── mars/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif └── moon/   └── image_name/     ├── shp/     │ ├── <image_name>-ROM.shp     │ ├── <image_name>-boulder-mapping.shp     │ └── <image_name>-global-tiles.shp     └── raster/       └── <image_name>.tif</pre>   File 2: best_model.zip Size: 624.7 MB Summary: This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics): <ul> <li> augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information. </li> </ul> <ul> <li> Base-RCNN-FPN.yaml (base model setup file). </li> <li> config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file). </li> <li> Mars-MoonEarth-v050...yaml (model setup file). </li> <li> log.txt (log during training of the algorithm). </li> <li> model_0055999.pth (model weights at second last saving step) </li> <li> model_0063999.pth (model weights at last saving step) </li> </ul> We advice the use of model weights model_0055999.pth (to avoid slight overfitting). File 3: Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images) Size: 252.8 MB Summary: This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files. <ul> <li> The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json". </li> <li> The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset. </li> <li> The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles. </li> <li> The shapefile folder is actually empty (it should not be there!). </li> </ul> Structure: <pre>. └── preprocessed_inputs/   ├── json   ├── pkl   ├── preprocessing/   │   ├── train/   │   │   ├── images   │   │   └── labels   │   ├── validation/   │   │   ├── images   │   │   └── labels   │   └── test/   │       ├── images   │       └── labels   └── shp</pre>

opencc-by-4.0Jul 2023View details →

zenodo40/100

Reproduction packages for the paper "Spectral and Imaging properties of Sgr A∗ from High-Resolution 3 DGRMHD Simulations with Radiative Cooling"

This is a basic reproduction package for the paper"Spectral and Imaging properties of Sgr A&lowast; from High-Resolution 3D GRMHD Simulations with Radiative Cooling" by Yoon et al. (2020). It aims to provide the most important data products to check and reproduce the main results of the paper.

opencc-by-4.0Aug 2020View details →

zenodo40/100

High-resolution images of 1550 Ordovician to Silurian graptolite specimens for global correlation and shale gas exploration

A unique graptolite image dataset consists of  key graptolite species used for dating rocks, global correlation, and “gold caliper” for locating shale gas favourable exploration beds (FEBs) in China.  All images were taken from 1,550 carefully curated graptolite specimens, taxonomically belong to 113 graptolite species or subspecies. They were collected from the Ordovician to Silurian sediments of China and published in 1958-2020. These specimens are preserved as shale and were collected from 154 representative geological sections of China. All specimens are housed at the Nanjing Institute of Geology and Palaeontology (NIGP), Chinese Academy of Sciences (CAS). My working group spent over two years to complete photographing every specimen using a single-lens reflex camera Nikon D800E with Nikkor 60 mm macro-lens and Leica M125 and M205C microscopes equipped with Leica cameras. Every image is well focused and better shows the morphology of graptolite bodies. In total, we took 40,597 images, including 20,644 camera photos (each with a resolution of 4,912 × 7,360) and 19,953 microscope photos (each with a resolution of 2,720 × 2,048). Photos of low contrast or bad focus were removed from the whole collection. We only kept and selected the photos that show the visual morphology of every specimen and the diagnostic character of each graptolite species that the specimens represent. We selected one image for each specimen as the present final dataset, uploaded to and stored in our cloud server. We incorporated revision suggestions from distinguished palaeontologists to generate the ground-truth labels, providing a taxonomical authority of the dataset. The dataset potentially contributes to a range of scientific activities and provides 1) easy access to high-resolution images of 2951 specimens of 113 graptolite species for teaching and training in palaeontology and geologic survey; 2) Global bio-stratigraphic correlation using graptolites, especially with those bio-zone species; 3) A standard fossil specimen image dataset used in shale gas industry to improve exploration efficiency, and 4) The potential aid of developing image-based automated classification model. Every specimen has two photos, one is original, another shows specimen with a scale bar. Occasionally in some large image the scale bar is embedded and beside the fossil specimen. All in JPG format. Single JPG file ranges from 822 KB to 7.055 MB.  Total :10.4 GB.

opencc-by-4.0Jan 2022View details →

zenodo40/100

High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma

This deposition contains only training dataset of ORCHID database. The validation and test dataset related to the same study can be found at DOI: 10.5281/zenodo.12646943.

opencc-by-4.0Jul 2024View details →

zenodo40/100

Images and supporting data for high-resolution μCT of a mouse embryo using a compact laser-driven x-ray betatron source

A high resolution x-ray CT scan of an embryonic mouse sample was performed with the betatron x-ray source produced by a laser wakefield accelerator. This data deposition includes all of the raw images of the mouse sample, information regarding their indexing, featured slices of the tomogram and some further raw data regarding the x-ray source characterisation.

opencc-by-4.0May 2018View details →

zenodo40/100

A high-frequency and high-resolution image time series of the Gornergletscher - Swiss Alps - derived from repeated UAV surveys

This dataset is based on aerial photographs of the Gornergletscher glacial system (Switzerland) collected during ten intensive UAV surveys carried out approximately every two weeks throughout the summer 2017. The final products consist in a series of 10 cm resolution ortho-images, Digital Elevation Models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements.

opencc-by-4.0Nov 2018View details →

zenodo40/100

Figure 2 in High-Resolution Functional Imaging of Native Proteins using Force Distance Curve Based Atomic Force Microscopy

Figure 2. - Juvenile Trachipterus arcticus, 129 mm SL, collected at Faial Island, Azores, 14 May 2014, on the surface. A: Portrait with anterior black facet visible; B: Oblique lateral view with first spines erected; note orange bulbous outgrowths on the prolonged spine; C: Lateral view showing proportions, markings and orientation of fins. Scale bars: A = 1 cm; B, C = 5 cm.

opencc-by-4.0Dec 2015View details →

zenodo40/100

Figure 1 in High-Resolution Functional Imaging of Native Proteins using Force Distance Curve Based Atomic Force Microscopy

Figure 1. - Adult Trachipterus arcticus, about 1.8 m long, observed south of Pico Island, Azores, 18 Aug. 2013, 950 m deep.

opencc-by-4.0Dec 2015View details →

zenodo40/100

Dataset for the publication "Identification of plasticity-induced crack closure by using high-resolution digital image correlation"

This repository publishes the data generated in the article "Identification of plasticity-induced crack closure by using high-resolution digital image correlation" (see arxiv preprint <a href="https://arxiv.org/html/2409.02560v1">Plasticity-induced crack closure identification during fatigue crack growth in AA2024-T3 by using high-resolution digital image correlation (arxiv.org)</a>) This repository is structured with the following subfolders: <ul> <li>0_fe_data: contains the displacement field of the free surface of the 3D finite element model that were used to determine the crack opening curves and, in the following, the crack opening value Kop</li> <li>1_hrdic_data: contains the high-resolution DIC displacement field data at a crack length of 27.8 mm at different load levels, starting from minimum load 1.5 kN to maximum load 15 kN</li> </ul>

opencc-by-4.0Sep 2024View details →

zenodo40/100

High-resolution DEMs and ortho images of Langtang village post-2015 Gorkha earthquake in Nepal

Datasets related to the article "Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-seismic Avalanche in the Himalayas." The data were collected around Langtang village, which was destroyed by snow and ice avalanches triggered by the 2015 Gorkha earthquake. The datasets include digital elevation models (DEMs) and orthoimages, gathered using three types of UAVs equipped with different cameras in October 2015. Description of files: -a7_DEM_50cm.tif: 0.5 m resolution DEM derived from a quadcopter UAV equipped with a Sony α7R (36.3-megapixel sensor). -a7_ortho_9cm.tif: 0.09 m resolution orthoimage derived from the same data as in a7_DEM_50cm.tif. -ebee_DEM_50cm.tif: 0.5 m resolution DEM derived from a fixed-wing UAV equipped with a Canon IXUS 125HS (16-megapixel sensor). -ebee_ortho_15cm.tif: 0.15 m resolution orthoimage derived from the same data as in ebee_DEM_50cm.tif. -gr_DEM_50cm.tif: 0.5 m resolution DEM derived from a fixed-wing UAV equipped with a Ricoh GR (14.2-megapixel sensor). -gr_ortho_12cm.tif: 0.12 m resolution orthoimage derived from the same data as in gr_DEM_50cm.tif. Please refer to the related journal article for more details on the datasets. Sunako S, Fujita K, Yamaguchi S, Inoue H, Immerzeel WW, Izumi T and Kayastha RB (2024) Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-Seismic Avalanche in the Himalayas. J. Disaster Res. 19(5), 865–873 (doi:10.20965/jdr.2024.p0865)

opencc-by-4.0Sep 2024View details →

zenodo40/100

Dataset on UAV High-resolution Images from Grassland with Broad-leaved Dock (Rumex Obtusifolius)

The dataset consists of orthophotos (build from UAV images) from a grassland field in which several Rumex obtusifolius plants were detected. The field is located in Germany (Kleve). The UAV images were acquired at 10, 15, and 30 meters height. Moreover, the labels/annotations from the Rumex obtusifolius plants in the images are also provided.

opencc-by-4.0Dec 2020View details →

zenodo40/100

Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation

Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference,  integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.

opencc-by-4.0Jan 2023View details →

dryad40/100

Segmented high-resolution transmission electron microscopy images of nanoparticles

Open the record for dataset details and reuse information.

publicJul 2023View details →

zenodo36/100

Deep Learning with Satellite Images Enables High-Resolution Income Estimation: a Case Study of Buenos Aires

This repository contains the datasets required for replicating the results in Abbate et al (forthcoming). The datasets also include per capita income estimates at a 50x50 meter resolution for the years 2013, 2018, and 2022, using satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census+survey data. The model, based on the EfficientnetV2 architecture, achieved high accuracy in predicting household incomes (R2=0.878), surpassing existing methods in spatial resolution and performance.  Inside the Replication Package folder, the user can replicate the main results from the paper. This includes: <ol> <li> Small Area Estimation (SAE) Replication: <ul> <li> Argentina Household Survey Data (EPH): Processed microdata for 2010, 2013, 2018, and 2022 (ARG_*_EPHC-S2_*.dta). </li> <li> Argentina Census Microdata: Raw 2010 census microdata (censo2010_fullraw_p.dta). </li> <li> Census Tract Map: Shapefile of 2010 census tracts (radios_eph_with_link.shp). </li> <li> SAE Output: The final small_area_estimates.parquet file containing census tract-level population and estimated income, which serves as labels for the CNN model. </li> </ul> </li> <li> CNN-based Income Prediction Replication (Paper Results): <ul> <li> CNN Model Income Predictions: Gridded 50x50m income estimates for Buenos Aires for 2013, 2018, and 2022 (income_estimates_*.shp). </li> <li> Normalization Scalars: A CSV file (scalars_ln_pred_inc_mean_trimTrue.csv) to convert the model's log-scale outputs into real income values (2010 PPP-adjusted Argentinian pesos). </li> <li> World Settlement Footprint (WSF): Satellite-based data (WSF2015_v2_-60_-36.tif) used to mask predictions in uninhabited areas. </li> </ul> </li> </ol> Key prediction datasets are published in shapefile format, while input data for SAE and other auxiliary files are in formats like .dta, .parquet, .csv, and .tif. Results can be replicated by connecting these datasets with the scripts available at the GitHub repo linked below. For researchers who wish to replicate the full analysis pipeline starting from the original source imagery, the data must be acquired commercially. The proprietary Pleiades and Pleiades NEO satellite imagery is owned by Airbus and can be purchased through their data portal: https://space-solutions.airbus.com/imagery/. To facilitate this process, we provide the unique product identifiers for each scene used in this study. These identifiers can be used to query the Airbus archive and purchase the exact scenes. <ul> <li>Pléiades: for 2013 imagery the IDs are DS_PHR1A_201302051411520_FR1_PX_W059S35_0807_03124, DS_PHR1A_201302071357305_FR1_PX_W059S35_0410_06105 and DS_PHR1A_201302071357509_FR1_PX_W059S35_0609_05426, and for 2018, DS_PHR1A_201803251356358_FR1_PX_W059S35_0909_03875, DS_PHR1A_201808021356574_FR1_PX_W059S35_0509_06938 and DS_PHR1A_201808021357186_FR1_PX_W059S35_0706_06104.</li> <li>Pleiades NEO: for 2022 imagery the IDs used are 000047717_1_22_STD_A, 000047717_1_24_STD_A, 000047717_1_25_STD_A, 000047717_1_26_STD_A, 000058605_1_3_STD_A, 000058605_1_4_STD_A, 000058605_1_7_STD_A, and 000058608_1_2_STD_A.</li> </ul> Important Usage Note: Since the predictions for each 50x50m cell individually present some random variation, we recommend that the results are used by averaging out the estimations for each area of interest (e.g., municipalities, neighborhoods, sections, or census tracts) and not at an individual cell level. As detailed throughout the paper, the aggregated results, even in small areas such as census tracts, predict household incomes with precision. Furthermore, inside this repository, it is possible to access and use the model’s trained parameters to make predictions about different satellite images. Data can be visualized by accessing: <a href="https://ingresoamba.netlify.app">https://ingresoamba.netlify.app</a>

opencc-by-4.0Aug 2024View details →

zenodo36/100

Presentation in HEARING: High-resolution structural and functional EAR imaging 2023: Three-dimensional vibration of the human tympanic membrane using a scanning laser doppler vibrometer

This is for Bastian Baselt's poster presentation in HEARING: High-resolution structural and functional EAR imaging, Ascona, Switzerland in 2023, and the related data.

opencc-by-4.0Apr 2024View details →

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