This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meter Source Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary Sphere Extent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer? This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro. In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend. To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth. Different Classifications Available to Map Five processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display. Using Time By default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year. In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change. Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009. This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover. Land Cover Processing To provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015. Source data The datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.php CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) 50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

Background & motivation

This upload consist of a dataset of recorded Hand Palm Motion (HPM) gestures. The motions within this HPM dataset involve rotational movements, translational movements, or a combination of both performed by the palm of the hand. These motions were designed for the frame-invariant gesture recognition Proof of Concept, shown in the following video. In this PoC, the goal is to control the movement of the end-effector and gripper fingers of a manipulator arm through hand palm motion gestures. Specifically, the aim is to maintain a high recognition performance when challenged by significant variations in both the tracker reference frame and the sensor reference frame.

Gesture design

Seven hand palm motion gestures were designed. These gestures are explained below. A figure (gestures.svg) of these gestures is also included.

• a) Go Left: move the hand along a straight line.
• b) Go Right: trace a circular path with the hand while allowing a slight rotation of the forearm.
• c) Go Up: move the hand upward while rotating the forearm,.
• d) Go Down: rotate the hand so the palm transitions from facing upward to facing downward.
• e) Open Gripper: starting from a downward facing palm, move the hand forward while rotating it such that the palm faces upward (this generates a screw motion with a positive pitch)
• f) Close Gripper starting from an upward facing palm, move the hand forward while rotating it such that the palm faces downward (this generates a screw motion with a negative pitch).
• g) Go Home: trace an arc by translating the hand.

These hand palm motion gestures are easy to perform, which ensures accessibility for users. Additionally, the gestures were carefully designed such that distinguishing the gestures does not rely on specific coordinate reference frames or directions. That is, the shape of the motion (rectilinear or circular translation, pure rotation, pure screw motion, etc.) contains sufficient information for this distinction.

To reduce transition effects between gestures that are performed successively, all gestures, except for Go Right, were designed to include the motions as explained above, followed by their reverse motions. Hence, after each gesture, the hand returned to the same pose it started from.

Gesture recording setup

The hand palm motions were recorded using an HTC Vive motion capture system, where the user's hand motion was captured by holding an HTC Vive tracker. The HTC Vive system recorded the orientation and position of the tracker with an accuracy of a few degrees and a few millimeters, respectively. The orientation and position trajectories of the tracker were retained as quaternion coordinates and 3D position coordinates sampled at a frequency of 50 Hz. For each of the seven gestures, five trials were recorded, resulting in a total of 7x5=35 recordings.

Dataset augmentation

To introduce the challenge of dealing with contextual variations when recognizing motions, the HPM dataset is augmented toward 420 trials by artificially transforming and perturbing the recorded trajectory data. Specifically, twelve different contexts were designed:

• The context Original 1 consists of the original recordings.
• The context Original 2 serves as a baseline, with no artificial transformations applied.
• The contexts Slower and Faster were obtained by rescaling the time axis and numerically resampling the trajectory coordinates using Screw Linear Interpolation (ScLERP), a generalization of SLERP to SE(3). The resulting transformed trajectories simulate twice as slow and twice as fast executions of the gestures.
• The context First Half includes trajectories that consist of only the first half of the trajectory data. These trajectories hence represent gestures that have not yet been finished. This context allows the evaluation of an approach's ability to recognize trajectories when dealing with incomplete data.
• The six contexts Change in body frame 1-3 and Change in world frame 1-3 incorporate reference frame changes. The resulting transformations are the following:
  • Change in body frame 1: the body frame is translated along its X-axis with 5 cm and rotated about its Z-axis with 180°.
  • Change in body frame 2: the body frame is translated along its Y-axis with 5 cm and rotated about its X-axis with 90°.
  • Change in body frame 3: the body frame is translated along its Z-axis with 5 cm and rotated about its Y-axis with -90°.
    (The origin of the body frame was translated by only 5 cm. Hence, this perturbation remained within reasonable deviations with respect to the size of the human hand.)
  • Change in world frame 1: the world frame is translated along its X-axis with 1 m and rotated about its Z-axis with 180°.
  • Change in world frame 2: the world frame is translated along its Y-axis with 1 m and rotated about its X-axis with 90°.
  • Change in world frame 3: the world frame is translated along its Z-axis with 1 m and rotated about its Y-axis with -90°.
• The context Combination incorporates multiple transformations. That is, the motions were simulated to be performed twice as fast, only the first half of the trajectory data was retained, and both the body and world frames were varied.

Data format

Within this dataset, every trial_x.csv file is a Comma-Separated Values (CSV) file. The trailing number x refers to the order in which the trials were performed. The file trial_x.csv has the following columns:

• The first column represents the time axis, consisting of the time stamps at a sampling frequency of 50Hz.
• The second to fourth columns contain the xyz-position coordinates of the origin of the body's reference frame.
• The fifth to eighth columns contain the quaternion coordinates of the orientation of the body's reference frame. The quaternion coordinates adhere to the scalar first convention.

Citing

The design of these hand palm motion gestures and the development of this dataset is one of the contributions of the work in [link]. This work is submitted to the 2025 IEEE Conference on Automation Science and Engineering (CASE). If you use this HPM dataset, please cite it as follows:

@misc{verduyn2025,
   title={Enhancing Hand Palm Motion Gesture Recognition by Eliminating Reference Frame Bias via Frame-Invariant Similarity Measures}, 
   author={Arno Verduyn and Maxim Vochten and Joris De Schutter},
   year={2025},
   eprint={2503.11352},
   archivePrefix={arXiv},
   primaryClass={cs.RO},
   url={https://arxiv.org/abs/2503.11352}, 
}

WorldClim 2.1 provides downscaled estimates of climate variables as monthly means over the period of 1970-2000 based on interpolated station measurements. Here we provide analytical image services of precipitation for each month along with an annual mean. Each time step is accessible from a processing template.Time Extent: Monthly/Annual 1970-2000Units: mm/monthCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 16 Bit IntegerData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim v2.1Using Processing Templates to Access TimeThere are 13 processing templates applied to this service, each providing access to the 12 monthly and 1 annual mean precipitation layers. To apply these in ArcGIS Online, select the Image Display options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left-hand menu. From the Processing Template pull down menu, select the version to display.What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides a graph of the time series along with the calculated annual mean value.This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and an area count of precipitation may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from month to month to show seasonal patterns.To calculate precipitation by land area, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Source Data: The datasets behind this layer were extracted from GeoTIF files produced by WorldClim at 2.5 minutes resolution. The mean of the 12 GeoTIFs was calculated (annual), and the 13 rasters were converted to Cloud Optimized GeoTIFF format and added to a mosaic dataset.Citation: Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.