
- 目录所有者
- 出色的 GEE 社区目录
- 数据集可用性
- 2000-01-01T00:00:00Z–2023-12-31T00:00:00Z
- 数据集提供程序
- 美国橡树岭国家实验室
- Earth Engine 代码段
-
ee.ImageCollection("projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL")
- 标签
说明
LandScan 数据集由橡树岭国家实验室 (ORNL) 提供,是一个全面且高分辨率的全球人口分布数据集,可作为各种应用的宝贵资源。LandScan 利用先进的空间建模技术和地理空间数据源,以 30 弧秒的分辨率提供有关人口数量和密度的详细信息,从而深入了解全球人类定居模式的最新动态。LandScan 具有准确性和精细度,可支持城市规划、灾害响应、流行病学和环境研究等多种领域,是决策者和研究人员了解和应对全球各种社会和环境挑战的重要工具。
频段
像素大小
1,000 米
乐队
名称 | 最小值 | 最大值 | 说明 |
---|---|---|---|
b1 |
0* | 21171* | 估算的人口数 |
使用条款
使用条款
Landscan 数据集已获知识共享署名 4.0 国际版许可。只要提供明确的来源归属,用户便可自由地出于商业和非商业目的使用、复制、分发、传输和改编该作品,不受任何限制。
引用
引用:
Sims, K., Reith, A., Bright, E., Kaufman, J., Pyle, J., Epting, J., Gonzales, J., Adams, D., Powell, E., Urban, M., & Rose, A. (2023 年)。LandScan Global 2022 [数据集]。Oak Ridge National Laboratory. https://doi.org/10.48690/1529167
DOI
使用 Earth Engine 进行探索
Code Editor (JavaScript)
var landscan_global = ee.ImageCollection('projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL'); var popcount_intervals = '<RasterSymbolizer>' + ' <ColorMap type="intervals" extended="false" >' + '<ColorMapEntry color="#CCCCCC" quantity="0" label="No Data"/>' + '<ColorMapEntry color="#FFFFBE" quantity="5" label="Population Count (Estimate)"/>' + '<ColorMapEntry color="#FEFF73" quantity="25" label="Population Count (Estimate)"/>' + '<ColorMapEntry color="#FEFF2C" quantity="50" label="Population Count (Estimate)"/>' + '<ColorMapEntry color="#FFAA27" quantity="100" label="Population Count (Estimate)"/>' + '<ColorMapEntry color="#FF6625" quantity="500" label="Population Count (Estimate)"/>' + '<ColorMapEntry color="#FF0023" quantity="2500" label="Population Count (Estimate)"/>' + '<ColorMapEntry color="#CC001A" quantity="5000" label="Population Count (Estimate)"/>' + '<ColorMapEntry color="#730009" quantity="185000" label="Population Count (Estimate)"/>' + '</ColorMap>' + '</RasterSymbolizer>'; // Define a dictionary which will be used to make legend and visualize image on // map var dict = { 'names': [ '0', '1-5', '6-25', '26-50', '51-100', '101-500', '501-2500', '2501-5000', '5001-185000' ], 'colors': [ '#CCCCCC', '#FFFFBE', '#FEFF73', '#FEFF2C', '#FFAA27', '#FF6625', '#FF0023', '#CC001A', '#730009' ] }; // Create a panel to hold the legend widget var legend = ui.Panel({style: {position: 'bottom-left', padding: '8px 15px'}}); // Function to generate the legend function addCategoricalLegend(panel, dict, title) { // Create and add the legend title. var legendTitle = ui.Label({ value: title, style: { fontWeight: 'bold', fontSize: '18px', margin: '0 0 4px 0', padding: '0' } }); panel.add(legendTitle); var loading = ui.Label('Loading legend...', {margin: '2px 0 4px 0'}); panel.add(loading); // Creates and styles 1 row of the legend. var makeRow = function(color, name) { // Create the label that is actually the colored box. var colorBox = ui.Label({ style: { backgroundColor: color, // Use padding to give the box height and width. padding: '8px', margin: '0 0 4px 0' } }); // Create the label filled with the description text. var description = ui.Label({value: name, style: {margin: '0 0 4px 6px'}}); return ui.Panel({ widgets: [colorBox, description], layout: ui.Panel.Layout.Flow('horizontal') }); }; // Get the list of palette colors and class names from the image. var palette = dict['colors']; var names = dict['names']; loading.style().set('shown', false); for (var i = 0; i < names.length; i++) { panel.add(makeRow(palette[i], names[i])); } Map.add(panel); } addCategoricalLegend(legend, dict, 'Population Count(estimate)'); Map.addLayer( landscan_global.sort('system:time_start') .first() .sldStyle(popcount_intervals), {}, 'Population Count Estimate 2000'); Map.addLayer( landscan_global.sort('system:time_start', false) .first() .sldStyle(popcount_intervals), {}, 'Population Count Estimate 2022');