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.
38
datasets available to search
ShareScore release 0.9.0
Dataset results
38 results for “Land Use Classification”
Land cover classification of Central Arizona-Phoenix using Landsat Thematic Mapper (TM) data - year 1998
Land cover classification for the Central Arizona-Phoenix CAP LTER study region using Landsat Thematic Mapper (TM) data - for the year 1998
Best learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Best learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description <a href="https://zenodo.org/deposit/7099785">here</a>). </p><p>Models: GP non spatial, GP spatial (sum), GP spatial (product),RF non spatial, RF spatial, MLP non spatial, MLP spatial, LTAE non spatial, LTAE spatial</p><p>For further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p><p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Boundary Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Boundary data set used to evaluate the continuity predictions for different models in boundary zones. It is composed of labelled and unlabelled pixels for a boundary size of 100m and 200m.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>To compute the predictions in the boundary zones with different models (GP, RF, MLP, LTAE), the code is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Best Learned Models: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Best learned model learned with the dataset available <a href="http://https://doi.org/10.5281/zenodo.8033058">here</a> for the mTAN-GP, mTAN-MLP, mTAN-LTAE, and raw-LTAE.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_mtan_gp_irregular_sits">open source repository</a>.</p>
Classification Data set: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. This dataset contains irregular and unaligned SITS with their corresponding masks. 9 different random sampling are provided. This data set was used to train mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_mtan_gp_irregular_sits">open source repository</a>.</p>
Land cover classification of the central Arizona-Phoenix area using Landsat (MSS) data - year 1973
These data represent a land use classification of the central Arizona-Phoenix area. They were created using a Landsat MSS image for the year 1973.
Land use and land cover (LULC) classification of the CAP LTER study area using 2010 Landsat imagery
The land use and land cover (LULC) mapping generated from the 30 meter resolution Landsat TM5 is prepared for CAP LTER analyses. The series of products includes three levels LULC classifications, from coarser land-cover types to finer, hybrid LULC types, arranged in three thematic maps with contrasting numbers of LULC categories: (1) 12, (2) 15, and (3) 21. The percent of vegetation cover (vegetation fraction) in the residential area is provided. The image has a resampled spatial resolution of 15 meters due to the image classification procedure.
CAP LTER land cover classification using 2010 National Agriculture Imagery Program (NAIP) Imagery
Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science at large, and increasingly for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER). This project provides the first fine-resolution land-cover mapping of the CAP LTER using 2010 NAIP four-band data. It demonstrates a new object-based method capable of delivering robust outputs for the range of research activities not only undertaken by the program of study but for such studies that appear to be emerging worldwide. The classification process incorporates cadastral GIS data to assist the land-cover type extraction at the parcel scale, and a hierarchical network approach that balances computation time with classification accuracy. Decision rules that may prove useful for other high resolution image classification in other arid-land metropolitan areas are provided.
Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1993
Land cover classification for the Central Arizona-Phoenix CAP LTER study region using Landsat Thematic Mapper (TM) data - for the year 1993
Land cover classification of Central Arizona-Phoenix using Landsat Thematic Mapper (TM) data - year 1985
Land cover classification for the CAP LTER study region using Landsat Thematic Mapper (TM) data - year 1985
Decadal Land Use and Land Cover Classifications across India, 1985, 1995, 2005
This data set provides land use and land cover (LULC) classification products at 100-m resolution for India at decadal intervals for 1985, 1995 and 2005. The data were derived from Landsat 4 and 5 Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multispectral (MSS) data, India Remote Sensing satellites (IRS) Resourcesat Linear Imaging Self-Scanning Sensor-1 or III (LISS-I, LISS-III) data, ground truth surveys, and visual interpretation. The data were classified according to the International Geosphere-Biosphere Programme (IGBP) classification scheme.
Land Cover and Land Use Classification for the State of New Hampshire, 1996-2001
The New Hampshire Geographically Referenced Analysis and Information Transfer System (GRANIT) land cover data set provides a land cover and land use product at 30-m resolution with 23 individual classes across the state. The classification is based largely on the analysis of 12 Landsat Thematic Mapper (TM and ETM+) images. Over 1,400 new classification training site data points were collected to supplement 1,200 archived sites from previous projects. The classification represents a snapshot in time from 1996 to 2001. This time range spans the dates of the most recent acquisitions of a TM scene for each region of the state and the dates of the most recent field data collection.
SMEX02 Land Surface Information: Land Use Classification, Version 1
This data set consists of land use classification data collected for the Iowa Soil Moisture Experiment 2002 (SMEX02) study region. The land use classification image provides information about vegetation present in the study area.
SMEX03 Land Use Classification Data: Georgia, Version 1
This data set is a subset of data acquired from the United States Geological Survey's (USGS) National Land Cover Dataset (NLCD). The NLCD is comprised of Landsat data collected during 2001 that are representative of the land cover conditions present in the Georgia, USA regional study area during the Soil Moisture Experiment of 2003 (SMEX03).
SMEX03 Land Use Classification Data: Oklahoma, Version 1
This data set consists of land use classification data derived from Landsat 5 data for the Soil Moisture Experiment 2003 (SMEX03).
SMEX05 Land Use Classification Data: Iowa, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains land use classification data for the Walnut Creek watershed area of Ames, Iowa USA. For the Ames study region, the National Agricultural Statistical Service (NASS) Land Cover Estimate was used to represent land cover classes.
SMEX04 Land Use Classification Data, Arizona, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set is comprised of land use classification data necessary for modeling and scaling hydrologic variables as part of the Soil Moisture Experiment of 2004 (SMEX04).
SMEX04 Land Use Classification Data, Sonora, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set is comprised of land use classification data necessary for modeling and scaling hydrologic variables as part of the Soil Moisture Experiment of 2004 (SMEX04). Six Landsat 5 Thematic Mapper (TM) scenes and one Digital Elevation Model (DEM) were used to construct land use classification for both SMEX04 study regions: Arizona, USA and Sonora, Mexico.
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.