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ShareScore release 0.9.0
Dataset results
7 results for “Built-up area”
Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap
<p>This dataset contains input, intermediary, and output files for the following paper:</p> <p>Yann Forget, Catherine Linard and Marius Gilbert. "<em>Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap</em>", 2018.</p> <p>The dataset is composed of three archives:</p> <ul> <li><code>input.zip</code> : contains raw input data required to run the study ;</li> <li><code>intermediary.zip</code> : contains processed data required for the analysis ;</li> <li><code>output.zip</code> : contains the output tables and images of the study.</li> </ul> <p>Alternatively, output images of the study can be previewed <a href="http://maupp.ulb.ac.be/page/forget2018/">here</a> in interactive maps.</p> <p>The source code used to produce the outputs is availabe <a href="https://zenodo.org/record/1292005">here</a>.</p>
A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data
<p>The changes of urban built-up areas can reflect the process of urbanization, and it can reflect the population, economy, and cultural development of the city. Therefore, accurate and timely extraction of urban built-up areas plays an important role in the dynamic management of the city. In the existing research, single-source remote sensing data is used to extract urban built-up areas, and there is a problem that the spectrum of urban areas and non-urban areas is easily confused. Multi-source remote sensing data, including luojia-1 remote sensing data, Landsat 8 OLI remote sensing data, etc., can make up for the spectrum confusing issues.</p> <p>We fuse the time series information of night light remote sensing data, neighborhood information and point of interest (POI) data in spatial dimension, and propose a built-up area extraction method that integrates night light time and space information and POI information.</p>
A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data
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Built-up areas of Britain, 1790s-1830S
<p>This dataset includes the built up areas extracted from OS survey drawings, English county maps and Roy's military survey of Scotland.</p>
Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data
<p>Mastering the evolution of urban land cover is important for urban management and planning. In this paper, a method for analyzing land cover evolution within urban built-up areas based on nighttime light data and Landsat data is proposed. The method solves the problem of inaccurate descriptions of urban built-up area boundaries from the use of single-source diurnal or nocturnal remote sensing data and was able to achieve an effective analysis of land cover evolution within built-up areas. Four main procedures are involved: (1) The neighborhood e<span>xtremum</span> method and maximum likelihood method are used to extract nighttime light data and the urban built-up area boundaries from the Landsat data, respectively; (2) multisource urban boundaries are obtained using boundary pixel fusion of the nighttime light data and Landsat urban built-up area boundaries; (3) the maximum likelihood method is used to classify Landsat data within multisource urban boundaries into land cover classes, such as impervious surface, vegetation and water, and to calculate landscape indexes, such as overall landscape trends, degree of fragmentation and degree of aggregation; (4) the changes in the multisource urban boundaries and landscape indexes were obtained using the abovementioned methods, which were supported by multitemporal nighttime light data and Landsat data, to model the urban land cover evolution. Using the cities of Shenyang, Changchun and Harbin in northeastern China as experimental areas, the multitemporal landscape index showed that the integration and aggregation of land cover in the urban areas had an increasing trend, the natural environment of Shenyang and Harbin was improving, while Changchun laid more emphasis on the construction of artificial facilities. At the same time, the method proposed in this paper to extract built-up areas from multi-source city data showed that the user accuracy, production accuracy, overall accuracy and Kappa coefficient are at least 3%, 1%, 1% and 0.04 higher than the single-source data method.</p>
Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data
Open the record for dataset details and reuse information.
The dataset of China's Urban area (CUD) and Urban Built-up area (CUBD)
<p>The dataset is based on national unified high-precision surface coverage data (geographic condition monitoring results), including urban areas and built-up areas of 337 cities above prefecture level in China in 2015 and 2020, with the city differentiation field "CITY" and cities differentiated by administrative codes, e.g. "Urban_110100" for Beijing.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.