The land surface forms were identified using the method developed by the Missouri Resource Assessment Partnership (MoRAP). The MoRAP method is an automated land surface form classification based on Hammond's (1964a, 1964b) classification. MoRAP made modifications to Hammond's classification, which allowed finer-resolution elevation data to be used as input data and analyses to be made using 1 km2 moving window (True, 2002; True et al., 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True, 2002). Slope is classified as gently sloping or not gently sloping using a threshold value of 8%. Local relief, the difference between the maximum and minimum elevation in a 1km2 neighborhood for analysis, is classified into five classes (0-15m, 16-30m, 31-90m, 91-150m, and >150m). Slope classes and relief classes were subsequently combined to produce eight land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains). In the implementation for the contiguous United States, Sayre et al. (2009) further refined the MoRAP methodology to identify a new land surface form class, "high mountains/deep canyons", by using an additional local relief class (>400 m). This method was implemented for Africa using a void-filled 90m SRTM elevation dataset which was created from the 30m SRTM elevation data provided by the National Geospatial-Intelligence Agency. In the preliminary output, which had nine land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains, and high mountains/deep canyons), artifacts were identified over flat desert areas affecting the classification between the two lowest relief classes, "flat plains" and "smooth plains." Since this problem was especially pronounced in areas where the input SRTM elevation data originally had data-voids, the problem could have been caused by anomalies or artifacts in the input data, which resulted from the void-filling processes. Instead of further investigating causes of the problem, the two land surface form classes were combined. In addition, the "low hills" class which had a very low occurrence was combined with the "hills" class. As a result, seven land surface form classes were identified in the final dataset (smooth plains, irregular plains, escarpments, hills, breaks/foothills, low mountains, and high mountains/deep canyons). References: Hammond, E.H., 1964a. Analysis of Properties in Land Form Geography - An Application to Broad-Scale Land Form Mapping. Annals of the Association of American Geographers, v. 54, no. 1, p. 11-19. Hammond, E.H. 1964b. Classes of land surface form in the forty-eight states, U.S.A. Annals of the Association of American Geographers. 54(1): map supplement no. 4, 1: 5,000,000. Sayre, R., P. Comer, H. Warner, and J. Cress. 2009. A new map of standardized terrestrial ecosystems of the conterminous United States: U. S. Geological Survey professional Paper 1768, 17 p. True, D. 2002. Landforms of the Lower Mid-West. Missouri Resource Assessment Partnership. MoRAP Map Series MS-2003-001, scale 1:1,500,000. http://www.cerc.usgs.gov/morap/Assets/maps/Landforms_of_the_Lower_Mid-West_MS-2002-01.pdf. True, D., T. Gordon, and D. Diamond. 2000. How the size of a sliding window impacts the generation of landforms. Missouri Resources Assessment Partnership. http://www.cerc.cr.usgs.gov/morap/projects/landform_model/landforms2001_files/frame.htm.
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This layer is a georeferenced image of an historic continental map of Africa created around 1836. This map contains an accurate outline of the continent. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, or other information associated with the principal map. This map was georeferenced by the Stanford University Geospatial Center using an Azimuthal Equidistant Auxiliary Sphere projection. This map is part of a selection of digitally scanned and georeferenced historic maps of Africa held at Stanford University Libraries.
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The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Central African Republic: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.
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A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current cropland extent maps are either inaccurate, have coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland area map for the African continent is therefore recognised as a gap in the current crop monitoring services. Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35 SouthTemporal Coverage: 2019Spatial Resolution: 10 x 10 meterUpdate Frequency: TBDNumber of Bands: 3 BandsParent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMADSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Digital Earth Africa’s cropland extent maps for Eastern, Western, and Northern Africa show the estimated location of croplands in these countries for the period of January to December 2019:
Eastern: Tanzania, Kenya, Uganda, Ethiopia, Rwanda and BurundiWestern: Nigeria, Benin, Togo, Ghana, Cote d'Ivoire, Liberia, Sierra Leone, Guinea and Guinea-BissauNorthern: Morocco, Algeria, Tunisia, Libya and EgyptSahel: Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, Sudan, South Sudan, Somalia and DjiboutiSouthern: South Africa, Namibia, Botswana, Lesotho and Eswatini
Cropland is defined as:
"a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date."
This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation.
The provisional cropland extent maps have a resolution of 10 metres and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built separately using extensive training data from Eastern, Western, and Northern Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository.
Independent validation datasets suggest the following accuracies:
The Eastern Africa cropland extent map has an overall accuracy of 90.3 %, and an f-score of 0.85 The Western Africa cropland extent map has an overall accuracy of 83.6 %, and an f-score of 0.75 The Northern Africa cropland extent map has an overall accuracy of 94.0 %, and an f-score of 0.91The Sahel Africa cropland extent map has an overall accuracy of 87.9 %, and an f-score of 0.78The Southern Africa cropland extent map has an overall accuracy of 86.4 %, and an f-score of 0.75
The algorithms for all regions tend to report more omission errors (labelling actual crops as non-crops) than commission errors (labelling non-crops as crops). Where commission errors occur, they tend to be focussed around wetlands and seasonal grasslands which spectrally resemble some kinds of cropping.
Available BandsBand IDDescriptionValue rangeData typeNoData/Fill valuemaskcrop extent (pixel)0 - 1uint80probcrop probability (pixel)0 - 100uint80filteredcrop extent (object-based)0 - 1uint80
mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification.
prob: This band displays the prediction probabilities for the ‘crop’ class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask.
filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm (see this paper for details on the algorithm used). During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the ‘salt and pepper’ effect typical of classifying pixels is diminished.
More details on this dataset can be found here.
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Maps with wind speed and wind power density potential for Sub-Saharan Africa. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).
This map features near real-time traffic information for different countries in Africa, designed for a night time display. This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The color coded map leverages historical, real time and predictive traffic data. Historical traffic is based on the average of observed speeds over the past three years. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.
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According to Cognitive Market Research, the global Digital Maps market size was USD XX million in 2023 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
The global Digital Maps market will expand significantly by XX% CAGR between 2024 to 2031.
North America held the major market of more than XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
Europe accounted for a share of over XX% of the global market size of USD XX million.
Asia Pacific held a market of around XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
Latin America's market will have more than XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
Middle East and Africa held the major market of around XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
The Tracking and Telematics segment is set to rise GPS tracking enables fleet managers to monitor their cars around the clock, avoiding expensive problems like speeding and other careless driving behaviors like abrupt acceleration.
The digital maps market is driven by mobile computing devices that are increasingly used for navigation, and the increased usage of geographic data.
The retail and real estate segment held the highest Digital Maps market revenue share in 2023.
Market Dynamics of Digital Maps:
Key drivers of the Digital Maps Market
Mobile Computing Devices Are Increasingly Used for Navigation leading to market expansion-
Since technology is changing rapidly, two categories of mobile computing devices—tablets and smartphones—are developing and becoming more diverse. One of the newest features accessible in this category is map software, which is now frequently preinstalled on smartphones. Meitrack Group launched the MD500S, a four-channel AI mobile DVR, for the first time in 2022. The MD500S is a 4-channel MDVR with excellent stability that supports DMS, GNSS tracking, video recording, and ADAS. Source- https://www.meitrack.com/ai-mobile-dvr/#:~:text=Mini%204CH%20AI%20Mobile%20DVR,surveillance%20solutions%20that%20uses%20H.
It's no secret that people who own smartphones routinely use built-in mapping apps to find directions and other driving assistance. Furthermore, these individuals use georeferenced data from GPS and GIS apps to find nearby establishments such as cafes, movie theatres, and other sites of interest. Mobile computing devices are now commonly used to acquire accurate 3D spatial information. A personal digital assistant (PDA) is a software agent that uses information from the user's computer, location, and various web sources to accomplish tasks or offer services. Thus, mobile computing devices are increasingly used for navigation leading to market expansion.
The usage of geographic data has increased leading to market expansion-
Since it is used in so many different industries and businesses—including risk and emergency management, infrastructure management, marketing, urban planning, resource management (oil, gas, mining, and other resources), business planning, logistics, and more—geospatial information has seen a boom in recent years. Since location is one of the essential components of context, geo-information also serves as a basis for applications in the future. For example, Atos SE provides services to companies in supply chain management, data centers, infrastructure development, urban planning, risk and emergency management, navigation, and healthcare by utilizing geographic information system (GIS) platforms with location-based services (LBS).
Furthermore, augmented reality-based technologies make use of 3D platforms and GIS data to offer virtual information about people and their environment. Businesses can offer users customized ads by using this information to better understand their needs.Thus, the usage of geographic data has increased leading to market expansion.
Restraints of the Digital Maps Market
Lack of knowledgeable and skilled technicia...
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This data repository provides the datasets associated with analysis conducted in Kerner et al. (2024), citation below.
The CSV file intercomparison-results.csv
gives a table metrics for each of 11 land cover maps (plus a majority vote ensemble of all maps) evaluated using the reference dataset for each of 8 countries (Kenya, Rwanda, Uganda, Tanzania, Mali, Malawi, Togo, and Zambia). The reference datasets are provided as zip files. To ensure these datasets can be used for independent evaluation and comparison between maps in the future, the reference datasets should ONLY be used for final, independent evaluation of data products/model outputs; they should NOT be used for training models, tuning hyperparameters, or any other decisions during model/map development.
The provided tif files contain the consensus maps (sum of all 11 maps) used to compute consensus statistics in Kerner et al. (2024).
Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? Under review.
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The monitoring of tropical forests has benefited from the increased availability of high-resolution earth observation data. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. The availability of time series of remote sensing images at 10-meters is changing this paradigm. In the context of REDD+ national reporting requirements, we investigate a methodology that is reproducible and adaptable in order to ensure user appropriation. The overall methodology consists of three main steps: (i) the generation of Sentinel-1 (S1) and Sentinel-2 (S2) layers, (ii) the collection of an ad-hoc training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows are compared in terms of their capability to capture the canopy cover of forests in East Africa. The method is tested at scale, over Tanzania. Two big data computing platforms are combined to exploit the important volume of satellite data available over a yearly period. The study also explores the accuracy of two products derived from these mapping approaches: i) binary tree cover/no tree cover (TC/NTC) map, and ii) map of canopy cover classes. We demonstrate the potential of the combination of S1 SAR and S2 optical sensors to derive an accurate map of forest cover in East Africa at a spatial resolution of 10 meters for the year 2018. Our approach uses the high temporal resolution of S2 that allows to produce bimonthly cloud-free composites that reflect the seasonality of the vegetation. An overall accuracy (OA) of about 95% is reached for the TC/NTC map. When mapping different categories of forest and canopy cover, we obtain an OA over 85% both with a per pixel accuracy approach and when considering the neighbouring pixels in the classification training. The potential of S1 and S2 data for single trees discrimination is also assessed. The reference dataset (training and validation), the three best maps and the codes to produce the S1 and S2 composites on Google Earth Engine are shared here.
The Afrobarometer project assesses attitudes and public opinion on democracy, markets, and civil society in several sub-Saharan African.This dataset was compiled from the studies in Round 3 of the Afrobarometer survey, conducted from 2005-2006 in 18 African countries (Benin, Botswana, Cape Verde, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, Zimbabwe).
The Afrobarometer surveys have national coverage
Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe Ghana Mali Nigeria Tanzania Uganda Cape Verde Mozambique Senegal Kenya Benin Madagascar
Basic units of analysis that the study investigates include: individuals and groups
The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.
What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.
Sample survey data [ssd]
A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.
The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.
Sample Universe
The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.
What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.
Sample Design
The sample design is a clustered, stratified, multi-stage, area probability sample.
To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.
In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:
The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages
A first-stage to stratify and randomly select primary sampling units;
A second-stage to randomly select sampling start-points;
A third stage to randomly choose households;
A final-stage involving the random selection of individual respondents
We shall deal with each of these stages in turn.
STAGE ONE: Selection of Primary Sampling Units (PSUs)
The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.
We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.
Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.
Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.
Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.
Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.
The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.
These PSUs should then be allocated
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This study aims to develop more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, to update the knowledge on its spatial extent, level of fragmentation and conservation status. We tested 1023 model possibilities stemming from combination of Sentinel-1(S1) and Sentinel-2(S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series and topographic information, using random forest modelling approach. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296000 ha of Eastern Arc Forests (EAF) left, which is 16-27% less than estimated by previous products. Furthermore, acknowledging small forest fragments (1-10 ha) implies that the EAFs are more fragmented than previously considered. The generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans, to balance this situation
General information about NOAA-AVHRR can be queried by interested users in the category 'Sensor' and 'Source'. Some basic information is given hereafter.
The Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA 6,
8, 10 and TIROS-N measured in four spectral bands, while the NOAA 7, 9
and 11 are measured in 5 bands. The primary objective of the AVHRR
instrument is to provide cloud top and sea surface temperatures
through passively measured visible, near infra-red and infra-red
spectral radiation bands. Nevertheless these data are widely used for
terrestrial applications, such as land cover mapping and vegetation
monitoring.
The available data provide a long term AVHRR Global Area Coverage
(GAC) data set with particular emphasis placed on the continent of
Africa. Normalized Difference Vegetation Index (NDVI), channel 2
reflectance, channel 3 and 4 brightness temperatures, an approximate
surface temperature and a cloud probability image are available on a
daily basis from January 1982 to December 1992 for the whole African
Continent at a resolution of 5 km.
The remaining data from the entire GAC time series (July 1981 to the
present) will be processed by the end of 1995.
Channels 1 and 2 are converted to radiance using the August desert
calibration coefficients published by Holben et al. (1990).
Radiances in channels 1 and 2 are converted to 'top of atmosphere'
reflectances (ToA). Brightness temperatures are calculated for
channels 3, 4 and 5 using the inverse Planck function (Kidwell 1991).
Channels 4 and 5 are corrected for non-linearity of sensor response to
give true brightness temperatures using pre-launch correction
coefficients (Planet 1988). The brightness temperature and
reflectance images are scaled to 8 bit integers.
The ToA reflectances and brightness temperatures are used to identify
cloudy pixels. The channel 1 and 2 reflectances are then used to
compute the NDVI, and channels 4 and 5 true brightness temperatures
used to compute an approximate surface temperature using a 'split
window' technique (Price 1984). This does not take variations in
emissivity into account, and so is only of limited accuracy.
The NDVI, channel 2 reflectance, channels 3 and 4 brightness
temperature, surface temperature and the cloud probability channel are
then geometrically corrected. Geometric correction involves three
steps; navigation using the ELPs from the raw data, correlation with a
reference image data base to provide ground control points for fine
correction, and resampling into a daily continental scale mosaic.
For Africa a Mercator map projection is used. The Africa GAC mosaic
map center coordinates are 0#161#, 17.25#161# E, pixel size at the
equator is 5 by 5 km, giving an image of 1800 lines by 1600 columns,
with top left co-ordinates 37.59#161# N, 18.64#161# W, bottom right
37.59#161# S, 53.64#161# E. As a function of the Mercator projection
the resolution degrades by approximately 20% at the northern and
southern limits; for example the ground area represented by each pixel
is 30 km2 at 35#161# N or S, (4 km, x axis by 7.5 km, y axis) compared
with 25 km2 at the equator.
The processed data sets are stored as ERDAS 7.4 format files on a
gigatek optical disk juke box. Data are currently made available
through formal collaborative research agreements between outside
laboratories and the Joint Research Centre. In such instances data
costs are for marginal cost of reproduction only.
These data will become available to the international research
community through the EC and European Space Agency initiative, the
Centre for Earth Observation (CEO).
The data sets have already provided new information concerning inter
and intra annual variations in vegetation fire dynamics for Africa and
have been used to derive forest seasonality information through the
JRC`s thematic projects such as TRopical Ecosystem and Environment
observations by Satellite (TREES).
See separate entry for TREES.
Example data can be found on the CEO World Wide Web home page:
"http://www.ceo.org/".
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BASE YEAR | 2024 |
HISTORICAL DATA | 2019 - 2024 |
REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
MARKET SIZE 2023 | 2.47(USD Billion) |
MARKET SIZE 2024 | 2.67(USD Billion) |
MARKET SIZE 2032 | 5.0(USD Billion) |
SEGMENTS COVERED | Application, Technology, End Use, Deployment Mode, Regional |
COUNTRIES COVERED | North America, Europe, APAC, South America, MEA |
KEY MARKET DYNAMICS | Increasing demand for autonomous vehicles, Advancements in satellite technology, Growth in urban planning applications, Rising need for accurate geolocation, Expansion of drone delivery services |
MARKET FORECAST UNITS | USD Billion |
KEY COMPANIES PROFILED | Pitney Bowes, HERE Technologies, Google, Microsoft, Autodesk, NavVis, Hexagon, TomTom, Mapbox, Apple, DigitalGlobe, Oracle, Leica Geosystems, Samsung Electronics, Esri |
MARKET FORECAST PERIOD | 2025 - 2032 |
KEY MARKET OPPORTUNITIES | Increased demand for autonomous vehicles, Expansion in smart city projects, Growth in logistics and supply chain, Advancements in drone technology, Rising adoption of AR/VR applications |
COMPOUND ANNUAL GROWTH RATE (CAGR) | 8.18% (2025 - 2032) |
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Land area (sq. km) in South Africa was reported at 1213090 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in South Africa: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here
The Soil and Terrain Database for Northeastern Africa contains land resource information on soils, physiography, geology and vegetation for the following ten countries: Burundi, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan and Uganda. The information is accessible with an easy-to-use viewer program and is also stored in vector Arc/Info export format. Information on individual soil properties with class values is also given. A land suitability assessment for irrigated and upland crops for each unit is included. The scale ofthe source material is variable and ranges between 1:1 million and 1:2 million. A user manual for the viewer program and background information on the collected and correlated land resource materials are contained in filed documents.
Soils are classified in the Revised Legend; physiographic and lithology information was collected using an earlier draft version of the SOTER manual.
The Inter-Governmental Authority on Development (IGAD) -- Sudan, Kenya, Djibouti, Somalia, Uganda, Eritrea, Ethiopia -- Crop Production System Zones (CPSZ) software is a detailed database that provides background information about actual farming in the region. It comes with a program (CVIEW, a CPSZ viewer) that displays maps, zooms in and out, and provides export facilities for the maps in image format and for the actual data in text format. The elementary mapping unit is a compromise between administrative units and agro-ecological zones: whenever steep agro-ecological gradients exist, administrative units are subdivided, thus resulting in 1200 mapping units that are homogeneous from an agro-ecological point of view, while retaining the compatibility with the administrative units used for most socio-economic variables in agricultural planning.
The just over 500 mappable variables are subdivided into several categories covering the spectrum from agronomy and livestock to the physical environment. For each mapping unit, detailed information is also presented on the crop calendar, typical yields and main pests and diseases.
This CD-ROM contains a collection of land and natural resource information for Northeastern Africa, in particular for the IGAD countries bordering the Nile basin. It includes data on administrative boundaries, rivers and lakes, soil and terrain, climatology, land use, physiography, geology and natural vegetation in easily accessible format.
Soil and Terrain Database for Northeasterm Africa (1:1 Million Scale) and Crop Production System Zones of the IGAD Subregion is provided on CD-ROM by the FAO, Land and Water Digital Media Series (Number 2). The CD-ROM can be purchased (Price: US$40) from FAO, Sales and Marketing Group, Viale delle Terme di Caracalla 0100 Rome, Italy (Fax: +39-06-5705-3360 E-mail: publications-sales@fao.org).
This dataset consists of very high resolution urban land cover maps for two African cities, Mekelle, Ethiopia and Polokwane, South Africa for 2020. Maps were generated from Planet SuperDove satellite imagery at 3.125-m spatial resolution, and Worldview-3 satellite imagery (Maxar Techologies) at two spatial resolutions, 2 m for multispectral imagery and 0.5-m spatial resolution for pansharpened imagery. An object-based image classification approach was used to produce a multi-class land cover product for each image source. The aim of this work was to support fine scale urban land cover analyses and comparative assessments between different high resolution satellite imagery sources. The data are provided in shapefile format.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in South Africa: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Dataset Summary
This dataset provides the most accurate and comprehensive geospatial information on wind turbines in South Africa as of 2025. It includes precise turbine coordinates, detailed technical attributes, and spatially harmonized metadata across 42 wind farms. The dataset contains 1,487 individual turbine entries with validated information on turbine type, rated capacity, rotor diameter, commissioning year, and administrative regions. It was compiled by integrating OpenStreetMap (OSM) data, satellite imagery from Google and Bing, a RetinaNet-based deep learning model for coordinate correction, and manual verification.
Data Structure
Format: GeoJSON
Coordinate Reference System (CRS): WGS 84 (EPSG:4326
)
Number of features: 1,487
Geometry type: Point (turbine locations)
Key attributes:
id
: Unique internal identifier
osm_id
: Reference ID from OpenStreetMap
gid
, country
, type1
, name1
, type2
, name2
: Administrative region (based on GADM)
farm_name
: Name of the wind farm
commissioning_year
: Year the turbine was commissioned
number_of_turbines
: Total number of turbines at the wind farm
total_farm_capacity
: Total installed capacity of the wind farm (MW)
capacity_per_turbine
: Rated power per turbine (MW)
turbine_type
: Manufacturer and model of the turbine
geometry
: Point geometry (longitude, latitude)
Publication Abstract
Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a harmonized and spatially refined dataset of wind turbines in South Africa, combining OpenStreetMap data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1,487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. The Geo-Coordinates were validated and corrected using a RetinaNet-based object detection model applied to both Google and Bing satellite imagery. Instead of relying solely on spatial precision, the curation process emphasized attribute completeness and consistency. Through systematic verification and cross-referencing with multiple public sources, the final dataset achieves a high level of attribute completeness and internal consistency across all turbines, including turbine type, rated capacity, and commissioning year. The resulting dataset is the most accurate and comprehensive publicly available dataset on wind turbines in South Africa to date. It provides a robust foundation for spatial analysis, energy modeling, and policy assessment related to wind energy development.
Citation Notification
If you use this dataset, please cite the following publication (currently in the process of publication):
Kleebauer, M. et. al (2025). A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
We mapped Low Elevation Coastal Zones at or below 10m in elevation and adjacent to the coastline for West Africa, from Senegal to Nigeria. This analysis was conducted using MERIT DEM data, which was created by removing multiple error types from SRTM3 v2.1 and AW3D-30m v1 to reduce vertical height bias (Yamakzai et al. 2018). Given this increased vertical accuracy, MERIT DEM can map 10-meter LECZs with an 89% accuracy (Gesch 2018).
To determine the 10-meter LECZ, we identified pixels that had a value less than 10 and were adjacent to the coast or a coastal water body. We also masked permanent water bodies from the zone to better visually represent the surrounding land areas most at risk.
Limitations
LIDAR derived Digital Elevation Models (DEMs), along with current, bathymetric and storm surge data, is widely acknowledged to be the most accurate way of modeling fine-scale SLR (Luger and Gunduz 2015, Gesch 2018, Kulp and Strauss 2015). Although this is largely recognized as the most accurate approach, LIDAR data is expensive to obtain, often unavailable in many parts of the world, and would require a large amount of processing power to analyze at the scale of the West African Coastline. Remotely sensed, globally available DEMs are also commonly used to map SLR vulnerability, although it has been shown that global DEMs are not suitable for mapping fine scale sea-level rise over relatively short time horizons with any acceptable amount of accuracy (Leon et al. 2014, Gesch 2018). Given these accuracy and data availability issues, we were not able to model seal level rise itself, but rather were able to identify 10-meter Low Elevation Coastal Zones (LECZs) for the entire west coast of Africa (Senegal to Nigeria). We also highlighted other key areas within the LECZ that are particularly vulnerable to the impacts of sea level rise for information and planning purposes.
Map projection : It is currently Africa Albers Equal Area Conic (WGS84).
Data links
· MERIT DEM : https://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/
· https://www.wabicc.org/mdocs-posts/mapping-west-africas-low-elevation-coastal-zones/
Data source
This data layer was developed using MERIT DEM data, which is created by removing multiple error types from SRTM3 v2.1 and AW3D-30m v1 to reduce vertical height bias. This dataset was produced by Yamakzai et al. 2018.
Citation (s)
Cori G., 2019. Mapping weest Africa’s low elevation costal zones. USAID, WA BiCC, Tetra Tech.
Gesch, D., 2018. Best Practices for Elevation-Based Assessments of Sea-Level Rise and Coastal Flooding Exposure. Frontiers in Earth Science, 6.
Gunduz, Orhan & Tulger Kara, Gülşah. (2015). ‘Influence of DEM Resolution on GIS-Based Inundation Analysis’. 9th World Congress of the European Water Resources Association (EWRA). İstanbul, Turkey.
Kulp, S. and Strauss, B., 2015. ‘The Effect Of DEM Quality On Sea Level Rise Exposure Analysis’. AGU Fall Meeting. 2015.
Leon, J., Heuvelink, G. and Phinn, S., 2014. Incorporating DEM Uncertainty in Coastal Inundation Mapping. PLoS ONE, 9(9), p.e108727.
Yamazaki D., D. Ikeshima, R. Tawatari, T. Yamaguchi, F. O'Loughlin, J.C. Neal, C.C. Sampson, S. Kanae & P.D. Bates. A high accuracy map of global terrain elevations. Geophysical Research Letters, vol.44, pp.5844-5853, 2017 doi: 10.1002/2017GL072874.
Geographic coverageSenegal to Nigeria.
Layer creation date : 7/31/20.
Contacts : Cori Grainger (cori.grainger@tetratech.com), Vaneska Litz (vaneska.litz@tetratech.com), Stephen Kelleher (Stephen.Kelleher@wabicc.org).
The land surface forms were identified using the method developed by the Missouri Resource Assessment Partnership (MoRAP). The MoRAP method is an automated land surface form classification based on Hammond's (1964a, 1964b) classification. MoRAP made modifications to Hammond's classification, which allowed finer-resolution elevation data to be used as input data and analyses to be made using 1 km2 moving window (True, 2002; True et al., 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True, 2002). Slope is classified as gently sloping or not gently sloping using a threshold value of 8%. Local relief, the difference between the maximum and minimum elevation in a 1km2 neighborhood for analysis, is classified into five classes (0-15m, 16-30m, 31-90m, 91-150m, and >150m). Slope classes and relief classes were subsequently combined to produce eight land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains). In the implementation for the contiguous United States, Sayre et al. (2009) further refined the MoRAP methodology to identify a new land surface form class, "high mountains/deep canyons", by using an additional local relief class (>400 m). This method was implemented for Africa using a void-filled 90m SRTM elevation dataset which was created from the 30m SRTM elevation data provided by the National Geospatial-Intelligence Agency. In the preliminary output, which had nine land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains, and high mountains/deep canyons), artifacts were identified over flat desert areas affecting the classification between the two lowest relief classes, "flat plains" and "smooth plains." Since this problem was especially pronounced in areas where the input SRTM elevation data originally had data-voids, the problem could have been caused by anomalies or artifacts in the input data, which resulted from the void-filling processes. Instead of further investigating causes of the problem, the two land surface form classes were combined. In addition, the "low hills" class which had a very low occurrence was combined with the "hills" class. As a result, seven land surface form classes were identified in the final dataset (smooth plains, irregular plains, escarpments, hills, breaks/foothills, low mountains, and high mountains/deep canyons). References: Hammond, E.H., 1964a. Analysis of Properties in Land Form Geography - An Application to Broad-Scale Land Form Mapping. Annals of the Association of American Geographers, v. 54, no. 1, p. 11-19. Hammond, E.H. 1964b. Classes of land surface form in the forty-eight states, U.S.A. Annals of the Association of American Geographers. 54(1): map supplement no. 4, 1: 5,000,000. Sayre, R., P. Comer, H. Warner, and J. Cress. 2009. A new map of standardized terrestrial ecosystems of the conterminous United States: U. S. Geological Survey professional Paper 1768, 17 p. True, D. 2002. Landforms of the Lower Mid-West. Missouri Resource Assessment Partnership. MoRAP Map Series MS-2003-001, scale 1:1,500,000. http://www.cerc.usgs.gov/morap/Assets/maps/Landforms_of_the_Lower_Mid-West_MS-2002-01.pdf. True, D., T. Gordon, and D. Diamond. 2000. How the size of a sliding window impacts the generation of landforms. Missouri Resources Assessment Partnership. http://www.cerc.cr.usgs.gov/morap/projects/landform_model/landforms2001_files/frame.htm.