Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “crop classification”
Crop classification dataset for testing domain adaptation or distributional shift methods
<p>In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: <a href="https://doi.org/10.1016/j.rse.2021.112488">https://doi.org/10.1016/j.rse.2021.112488</a> (arXiv version: <a href="https://arxiv.org/pdf/2109.01246.pdf">https://arxiv.org/pdf/2109.01246.pdf</a>). </p> <p>In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts.</p> <p>More details on the dataset and processing steps can be found in <a href="https://doi.org/10.1016/j.rse.2021.112488">Kluger et. al. (2021)</a>. Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in <a href="https://openreview.net/forum?id=5HR3vCylqD">Yeh et. al. (2021)</a>, and the raw dataset from France can be made available upon request from the authors of this Zenodo upload.</p> <p>All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf".</p> <p><strong>Preferred Citation:</strong></p> <p>-Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488.</p> <p>-URL to this Zenodo post https://zenodo.org/record/6376160</p>
Multimodala Dataset for multimodal contrastive learning for crop classification
<p>We developed this dataset using an existing dataset name DENETHOR developed by TUM <a href="https://openreview.net/forum?id=uUa4jNMLjrL">https://openreview.net/forum?id=uUa4jNMLjrL</a> to conduct our multi-modal contrastive learning experiments.</p>
Crop Classification Data-set
<p>The dataset consists of crop type training and testing data of more than 10 classes, collected using Ground Truth Surveys, in Harichand region of Khyber Pakhtoonkhwa, Pakistan. </p> <p>The dataset also contains 2 tiff files having Planet-Scope and Sentinel-2 raster data. </p> <p>https://drive.google.com/drive/folders/1SweabTezj78btq9wd3PRZYWrR_4gUuC4</p>
Classification and identification of pinecones mulching on blueberry cultivation based on crop leaf characteristics and hyperspectral data
<p><span>Supplementary Figure S1: Spectra preprocessing before and after.; Table S1: The evaluation results of the classification model of leaf growth and physiology.; Table S2: The evaluation results of the classification model of VIs.; Table S3: The evaluation results of the classification model of VNIR.; Table S4: The evaluation results of the classification model of SWIR.</span></p>
multi class dataset_Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
Open the record for dataset details and reuse information.
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.
Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
Open the record for dataset details and reuse information.
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.