GRACE Tellus Monthly Mass Grids provides monthly gravitational anomalies relative to a 2004-2010 time-mean baseline. The data contained in this dataset are units of "Equivalent Water Thickness" which represent the deviations of mass in terms of vertical extent of water in centimeters. See the provider's Monthly Mass Grids Overview for more details. This dataset is a filtered version of the GRACE Tellus (GRCTellus) Ocean dataset. The 'EOFR' bottom pressure (OBP) grids are obtained by projecting the data from the regular GRC Ocean grids product onto the Empirical Orthogonal Functions (EOFs) of the Ocean Model for Circulation and Tides (OMCT). This effectively filters out signals in the GRACE data that are inconsistent with the physics and OBP variations in the OMCT ocean model. The EOFR filtered reconstructed OBP fields agree better with radar altimetric sea surface height, reduce leakage around ice sheets and glaciers, and reduce noise in the mid latitudes where OBP variability is lower. (Chambers and Willis, 2010) Note The GRCTellus Ocean datasets are optimized to examine regional ocean bottom pressure, but NOT intended to be spatially averaged to determine global mean ocean mass.

The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface …

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Project Overview: The Caridina and Neocaridina Shrimp Detection Project aims to develop and improve computer vision algorithms for detecting and distinguishing between different shrimp varieties. This project is centered around aquarium fish keeping hobbyist and how computer vision can be beneficial to improving the care of dwarf shrimp. This project will focus on zoning a feeding area and tracking and counting caridina shrimp in area.

Caridina and neo-caridina shrimp are two distinct species that require different water parameters for optimal health. Neocaridina shrimp are generally more hardy and easier to keep than caridina species, while caridina shrimp are known for their striking distinctive patterns. The body structure of both species are similar. However, there are specific features that should allow enough sensitivity to at least distinguish between caridina shrimp.

Descriptions of Each Class Type: The dataset for this project includes thirteen different class types. The neo-caridina species have been grouped together to test if the model can distinguish between caridina and neo-caridina shrimp. The remaining classes are all different types of caridina shrimp.

The RGalaxyPinto and BGalaxyPinto varieties are caridina shrimp, with the only difference being their color: one is wine-red while the other dark-blue-black. Both varieties have distinctive spots on the head region and stripes on their backs, making them ideal for testing the model's ability to distinguish between color.

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The CRS-CBS Crystal Red Shrimp and Crystal Black Shrimp have similar patterns to the Panda Bee shrimp, but the hues are different. Panda shrimp tend to be a deeper and richer color than CRS-CBS shrimp, CRS-CBS tend to have thicker white rings.

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The Panda Bee variety, on the other hand, is known for its panda-like pattern white and black/red rings.The color rings tend to be thicker and more pronounced than the Crystal Red/Black Shrimp.

Within the Caridina species, there are various tiger varieties. These include Fancy Tiger, Raccoon Tiger, Tangerine Tiger, Orange Eyed Tiger (Blonde and Full Body). All of these have stripes along the sides of their bodies. Fancy Tiger shrimp have a similar color to CRS, but with a tiger stripe pattern. Raccoon Tiger and Orange Eyed Tiger Blonde look very similar, but the body of the Raccoon Tiger appears larger, and the Orange Eyed Tiger is known for its orange eyes. Tangerine Tigers vary in stripe pattern and can often be confused with certain neo-caridina, specifically yellow or orange varieties.

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The remaining are popular favorites for breeding and distinct color patterns namely Bluebolt, Shadow Mosura, White Bee/Golden Bee, and King Kong Bee.

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Links to External Resources: Here are some resources that provide additional information on the shrimp varieties and other resources used in this project:

Caridina Shrimp: https://en.wikipedia.org/wiki/Bee_shrimp
Neo-Caridina Shrimp: https://en.wikipedia.org/wiki/Neocaridina
  Roboflow Polygon Zoning/Tracking/Counting:https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb
  Roboflow SAM: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-anything-with-sam.ipynb
  Ultralytics Hub:https://github.com/ultralytics/hub

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 (OSM) data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. Of this, more than 3.6 GW is currently operational. 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. The dataset is publicly available.

Citation Notification

If you use this dataset, please cite the following publication (currently in the process of publication):

Kleebauer, M.; Karamanski, S.; Callies, D.; Braun, M. A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis. ISPRS Int. J. Geo-Inf. 2025, 14, 232. https://doi.org/10.3390/ijgi14060232

We share here maps produced using Earth Engine: https://code.earthengine.google.com/?accept_repo=users/bsenterre/gis

The maps include a land cover classification based on Sentinel-2, at 10m resolution, using an Object-Based Image Analysis approach, for the Seychelles granitic islands. Based on the land cover, landform (modeled using TauDEM), altitude and expert knowledge, we then derived a model of ecosystem types, with 3 maps: current distribution, potential distribution and prehuman distribution.

A report exists (18th May 2022) that describes in detail the methodology, and it is being used for the preparation of a publication. The maps uploaded here are in raster format (geotif), crs=4326, and are accompanied by QGIS legend files (.qml), so they should load in QGIS with their legend automatically.

This Geographic gazetteer for the Malesian region (Indonesia, Philippines, Papua new Guinea, Brunei, SIngapore, Malaysia and Timor Leste) has been created within the Malaysian Butterfly project from Naturalis Biodiversity Center.The geographic table was constituted from combining two large datasets, enrichening the data using Google API with open refine and on the fly adding locations while registerring specimens. The two main sources for the database wereBRAHMS (the Naturalis database for plant specimen)CRS (the Naturalis database for animal specimen)locations were added on the fly during butterfly registration using Google developers geocoder