Low Resolution

## Overview

Low Resolution is a dataset for object detection tasks - it contains Object Low Resolution annotations for 987 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios.Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this.The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

This data set (NmHRIR3G) consists of daily composites constructed from Nimbus 1, Nimbus 2, and Nimbus 3 satellites High Resolution Infrared Radiometer (HRIR) data for the region between 60 N and 60 S. Measurements were obtained during 1964, 1966, and 1969. Data are available as GeoTIFFs and browse images. For the HDF5 formatted version of these data, see the Nimbus High Resolution Infrared Radiometer Remapped Digital Data Daily L3, HDF5 data set.

Matlab(R) codes and raw processed images of feature selecting super resolution structured illumination microscopy numerical simulations.

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The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) provides a new level of detail in global topographic data. Previously, the best available global DEM was GTOPO30 with a horizontal grid spacing of 30 arc-seconds. The GMTED2010 product suite contains seven new raster elevation products for each of the 30-, 15-, and 7.5-arc-second spatial resolutions and incorporates the current best available global elevation data. The new elevation products have been produced using the following aggregation methods: minimum elevation, maximum elevation, mean elevation, median elevation, standard deviation of elevation, systematic subsample, and breakline emphasis. Metadata have also been produced to identify the source and attributes of all the input elevation data used to derive the output products. Many of these products will be suitable for various regional continental-scale land cover mapping, extraction of drainage features for hydrologic modeling, and geometric and radiometric correction of medium and coarse resolution satellite image data. The global aggregated vertical accuracy of GMTED2010 can be summarized in terms of the resolution and RMSE of the products with respect to a global set of control points (estimated global accuracy of 6 m RMSE) provided by the National Geospatial-Intelligence Agency (NGA). At 30 arc-seconds, the GMTED2010 RMSE range is between 25 and 42 meters; at 15 arc-seconds, the RMSE range is between 29 and 32 meters; and at 7.5 arc-seconds, the RMSE range is between 26 and 30 meters. GMTED2010 is a major improvement in consistency and vertical accuracy over GTOPO30, which has a 66 m RMSE globally compared to the same NGA control points. In areas where new sources of higher resolution data were available, the GMTED2010 products are substantially better than the aggregated global statistics; however, large areas still exist, particularly above 60 degrees North latitude, that lack good elevation data. As new data become available, especially in areas that have poor coverage in the current model, it is hoped that new versions of GMTED2010 might be generated and thus gradually improve the global model.

The High Resolution Digital Elevation Model Mosaic provides a unique and continuous representation of the high resolution elevation data available across the country. The High Resolution Digital Elevation Model (HRDEM) product used is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The mosaic is available for both the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) from web mapping services. It is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This strategy aims to increase Canada's coverage of high-resolution elevation data and increase the accessibility of the products. Unlike the HRDEM product in the same series, which is distributed by acquisition project without integration between projects, the mosaic is created to provide a single, continuous representation of strategy data. The most recent datasets for a given territory are used to generate the mosaic. This mosaic is disseminated through the Data Cube Platform, implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The mosaic is available from Web Map Services (WMS), Web Coverage Services (WCS) and SpatioTemporal Asset Catalog (STAC) collections. Accessible data includes the Digital Terrain Model (DTM), the Digital Surface Model (DSM) and derived products such as shaded relief and slope. The mosaic is referenced to the Canadian Height Reference System 2013 (CGVD2013) which is the reference standard for orthometric heights across Canada. Source data for HRDEM datasets used to create the mosaic is acquired through multiple projects with different partners. Collaboration is a key factor to the success of the National Elevation Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This data is a composite upper-air sounding data set which contains all upper-air sounding data collected from 25 platforms (excluding the reference sonde) during IHOP_2002. The data in the composite is at the highest possible resolution (i.e. the 'native' resolution). For more information about this data set please consult the Readme. Note: as of May 12th,2003, this dataset contains reprocessed dropsonde profiles from the Lear and Falcon. ATD released the new dropsonde dataset May 1st,2003.

The DeepFlood dataset provides high-resolution georeferenced images from both manned andunmanned aerial platforms, featuring detailed labels that go beyond simple binary distinctions.These labels include inundated vegetation, dry vegetation, open water, and others,making the dataset highly applicable for flood mapping across various landscapes. It uniquelyincorporates SAR imagery alongside optical and UAV images, enabling a multi-modal approachto accurately delineate flooded areas.

Explore the Entity Resolution Software market, projected to reach $10.9 billion by 2033 with a 15% CAGR. Discover key drivers, trends, restraints, leading companies, and regional insights for data unification and customer identity management.

Coupled urban-atmospheric models are extensively used to understand the urban environment and its impact on atmospheric processes. A common requirement of these models is information about the “urban fraction” (fraction of model grid covered by impervious surface area (ISA)). The European Space Agency (ESA) WorldCover product provides a global land cover map for the base year of 2020 at a spatial resolution of 10 m. The dataset is based on Sentinel-1 and Sentinel-2 data with an overall accuracy of 74.4%. In this study we process the WorldCover dataset and provide a ready-to-use “urban fraction” that can be incorporated in urban modelling systems. The dataset contains GeoTIFF and Weather Research and Forecasting Pre-processing System (WRF-WPS) format files for 1, 0.5, 0.25, 0.009 (~1 km), 0.0027 (~300 m), and 0.0009 (~100 m) degree spatial resolutions. The GeoTIFF files can be converted to other urban mesoscale modelling systems. Please check the README.txt for more information on using the dataset.

Replication data and code for the publication : High-resolution Downscaling of Disposable Income in Europe using Open-source Data

This dataset was published with the dataset descriptor "Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset".

ACKNOWLEDGEMENTS

The project ”ZUKIPRO” is funded as part of the ”Future Centers” program by the FederalMinistry of Labour and Social Affairs and the European Union through the European SocialFund Plus (ESF Plus).Roles and Contributions.

High resolution land cover data set for New York City. This is the 3ft version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

The USGS and the NGA have collaborated on the development of a notably enhanced global elevation model named the GMTED2010 that replaces GTOPO30 as the elevation dataset of choice for global and continental scale applications. The new model has been generated at three separate resolutions (horizontal post spacings) of 30 arc-seconds (about 1 kilometer), 15 arc-seconds (about 500 meters), and 7.5 arc-seconds (about 250 meters). This new product suite provides global coverage of all land areas from lat 84°N to 56°S for most products, and coverage from 84°N to 90°S for several products. Some areas, namely Greenland and Antarctica, do not have data available at the 15- and 7.5-arc-second resolutions because the input source data do not support that level of detail. An additional advantage of the new multi-resolution global model over GTOPO30 is that seven new raster elevation products are available at each resolution.

Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI) introduces machine learning methods to incorporate high-resolution Urban Heat Island (UHI) effects into low-resolution historical reanalysis and future climate model datasets. The dataset includes models trained to estimate UHI in Los Angeles and Seattle, along with open-source software and additional training data for the 50 most populous cities in the contiguous United States. The study demonstrates the application of these methods in evaluating climate change impacts and heat mitigation strategies within high-resolution urban microclimate modeling. The dataset aims to provide a computationally efficient and adaptable solution for urban planners to address various heat planning questions and prioritize heat mitigation strategies. The open-source models, software, and data will contribute to the development of more heat-resilient and sustainable urban environments in the face of climate change.

A resolution of the City Council of the City of Tempe, Arizona, authorizing the mayor to establish an open data program, including an open data policy and open data portal, and authorizing the City Manager to implement an open data program.

Maximum depth (deepest) in the Swedish Maritime Administration's depth database within each box in a regular grid with 20 m resolution. The actual position of the depth is maintained. Depth is generated only in squares with at least one measured value. The data set does not contain any data quality information.

This data release contains pretrained models, all training and testing data for the PSSR paper published in Nature Methods: Deep learning-based point-scanning super-resolution imaging (https://dx.doi.org/10.1038/s41592-021-01080-z) Data are organized in two categories: - Main models: pretrained models, training and testing data for major PSSR models, including - EM (neural tissue imaged on a tSEM) - Mitotracker (live imaging of cultured U2OS cells on a ZEISS Airyscan 880 confocal) - Neuronal mitochondria (hippocampal neurons from neonatal rats transfected with mito-dsRed imaged on a ZEISS Airyscan 880 confocal) - Supporting experiments: data for the supporting experiments, including - comparison between PSSR and BM3D denosing for both EM and fluorescence Mitotracker data - crappifier comparison for both EM and fluorescence Mitotracker data - compariosn between PSSR, CARE and Rolling Average for fluorescence Mitotracker data These files are available for download from the following link: https://3dem.org/public-data/tapis/public/3dem.storage.public/2021_Manor_PSSR/

CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01deg. horizontal resolution over the Mediterranean Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.