This dataset represents the cadastral maps created by the Geomatics branch in support of real property acquisitions within the Department of Water Resources. The geographic extent of each map frame was created after using all the spatial attributes available in each map to appropriately georeference it and create the extents from the outer frame of the map. The maps were digitally scanned from the original paper format that were archived after moving to the new resources building. As new maps are created by the branch for real property acquisition services, they will be georeference, attributed and updated into this dataset. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.6, dated September 27, 2023. DWR makes no warranties or guarantees either expressed or implied as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Original internal source projection for this dataset was Teale Albers/NAD83. For copies of data in the original projection, please contact DWR. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov as available and appropriate.

This COVADIS data standard concerns communal map documents (CCs). This data standard provides a technical framework describing in detail how to dematerialise these town planning documents in a spatial database that can be used by a GIS tool and interoperable. This standard of data covers both the graphical plans of sectors and the information overlaying them. This standard of COVADIS data was developed on the basis of the specifications for the dematerialisation of planning documents created in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard provides definitions and a structure for organising and storing spatial data from communal maps in an infrastructure, while the CNIG specifications are used to frame the digitisation of these data. Part C ‘Data Structure’ presented in this COVADIS standard provides additional recommendations for the storage of data files. These are specific choices for the common data infrastructure of the ministries responsible for agriculture and sustainable development, which do not apply outside their context.

The topographic frame map system TK100 is a map sheet section defined according to geographical network lines. Starting from the map sheet sections of the TK25 with a size of 10’ geographical longitude and 6’ geographical latitude, the sheet sections of the TK100 result from the aggregation of 16 TK25 sheet sections each, i.e. they have an extension of 40’ geographical longitude and 24’ geographical latitude. The sheet numbering of the TK25 follows a tabular system The first two digits indicate the row (numbered from north to south), the last two digits the column (numbered from west to east). The sheet numbering of the TK100 results from the lower left TK25 sheet number with the prefix "C" (Roman for 100).

This dataset represents the calderas present at or near the surface and comprises arcs with attributes describing the accuracy of location and name. The dataset is part of the Geological Map of New Zealand (Mainland) collection produced by GNS Science. The mapping frame within which the features have been observed is defined as the top of bedrock (i.e. the solid rock that may either be exposed at the topographic surface or covered by unconsolidated deposits). The data structure complies with the GeoSciML 4.1 standard, where relevant, and uses the appropriate CGI Controlled Vocabularies. This dataset forms part of Heron, D.W. (custodian) 2023: Geological Map of New Zealand 1:250 000 (4th ed.) [digital data]. Lower Hutt (NZ): GNS Science. (GNS Science geological map; 1). For more information on data, distribution options and formats visit https://doi.org/10.21420/5XTJ-5718?x=y.

To obtain the data visit https://doi.org/10.21420/8BEH-6P24.

The existing raster dataset corresponds to the year 1952, with data obtained from the UCSB Frame Finder Aerials, an online library collection database of aerial photography. The existing raster dataset contains two different flights, ABM-1952 and PAI-ABC, flown by Southwestern Aerial Surveys and Pacific Air Industries respectively, in order to provide a more comprehensive coverage of the city of Roseville. Some areas display apparent constrasts, such as plowed field vs. unplowed field, due to the fact that each flight was taken in different months in 1952. Both flights are displayed at a scale of 1:20:000The following photo frames were used to create the raster dataset: pai-abc_y8-144, pai-abc_y8-146, pai-abc_y8-140, pai-abc_y8-139, pai-abc_y8-141, pai-abc_y8-143, pai-abc_3k-28, pai-abc_3k-106, abm-1952_1k-68, amb-1952_1k-65, abm-1952_1k-28, abm-1952_1k-12, abm-1952_1k-67, abm-1952_1k-82, abm-1952_1k-80, abm-1952_1k-81, abm-1952_9k-84, abm-1952_9k-81.

Access the Data:

Access the REST Service from https://ags.roseville.ca.us/arcgis/rest/services/PublicServices/. View the data in our Historical Imagery Collection.Add data to ArcMap or ArcPro by clicking on “View Metadata” and selecting “Open in ArcGIS Desktop”.

Description of files ✔️

• clean_map.png - a clean map with no player designations, bombs;

• mask_map.png - map mask for marking its borders;

• map_1.mp4 - Recorded map of 10 professional CS matches:GO on the map "Dust 2".

Video Characteristics ✔️

• size - 364x364px; • frame rate - 23.98 frames/s; • duration - 2 hours 5 minutes 5 seconds.

List of matches 🎉 👇

• PGL Major Antwerp 2022 Challengers Stage bad news eagles vs eternal fire;
• PGL Major Antwerp 2022 Challengers Stage ence vs g2;
• PGL Major Antwerp 2022 Challengers Stage g2 vs spirit;
• PGL Major Antwerp 2022 ence vs natus vincere;
• PGL Major Antwerp 2022 Europe RMR A faze vs big;
• PGL Major Antwerp 2022 Europe RMR A vitality vs eternal fire;
• PGL Major Antwerp 2022 faze vs spirit;
• PGL Major Antwerp 2022 g2 vs outsiders;
• PGL Major Stockholm 2021 astralis vs vitality;
• PGL Major Stockholm 2021 natus vincere vs vitality.

About creating a dataset 👇

I spent a lot of time and effort to create this video minimap of cs go matches. My goal was to find out which places for CT and TT are most often visited. I hope you liked this dataset and it will be useful 🤓.

Duration of all matches 👀

All demos of the matches lasted 622 minutes (or 10 hours and 22 minutes). I was recording a minimap at a speed of (x4), so I managed to spend 4 times less time on it. But it took a long time to remove the "breaks" for the CT and TT sides, which removed the minutes of inactivity of the players on the map. It turned out to put 10 hours 22 minutes in 2 hours 5 minutes, which is 4.9 times less.

This dataset has been superseded by a new edition (4th edition, 2023) available here: https://data.gns.cri.nz/metadata/srv/eng/catalog.search#/metadata/3f6b15da-29a6-11ee-be56-0242ac120002.

This dataset represents the resources present at or near the surface and comprises points with attributes describing the type of resource. The dataset is part of the Geological Map of New Zealand collection produced by GNS Science and represents a slightly updated selection of the data available from the Geological Resource Map of New Zealand (GERM) dataset. The mapping frame within which the features have been observed is defined as surface geology (i.e. the bedrock and superficial deposits that are exposed at the topographic surface or would be visible if the overlying soil was removed). The data structure complies with the GeoSciML 4.1 standard, where relevant, and uses the appropriate CGI Controlled Vocabularies. The dataset is associated with Heron, D.W. (custodian) 2020: Geological Map of New Zealand 1:250 000. GNS Science Geological Map 1 (3rd ed.). Lower Hutt, New Zealand. GNS Science. DOI: https://doi.org/10.21420/03PC-H178.

This COVADIS data standard concerns communal map documents (CCs). This data standard provides a technical framework describing in detail how to dematerialise these town planning documents in a spatial database that can be used by a GIS tool and interoperable. This standard of data covers both the graphical plans of the sectors and the information overlaying them.This standard of COVADIS data has been developed on the basis of the specifications for the dematerialisation of planning documents created in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard provides definitions and a structure for organising and storing spatial data from communal maps in an infrastructure, while the CNIG specification serves to frame the digitisation of these data.Part C ‘Data Structure’ presented in this COVADIS standard provides additional recommendations for the storage of data files. These are specific choices for the common data infrastructure of the ministries responsible for agriculture and sustainable development, which do not apply outside their context.

The data records a single route taken through the suburb of St Lucia, Queensland, Australia. The route was traversed at five different times of the day to capture the difference in appearance between early morning and late afternoon. The route was traversed again, another five times, two weeks later for a total of ten datasets.

The data was recorded with a forward facing webcam attached to the roof of a car.

GPS data is included for each dataset.

Each dataset is labelled with the date and time it was collected in the following format DD/MM/YY_24HOUR. Each dataset has 5 files.

• (webcam_video.avi) video imagery. This file is compressed and no uncompressed version is available.
• (gps_log.txt) raw GPS data as logged
• (frame_log.txt) time stamps of each video frame compatible with GPS data
• (fGPS.txt) processed version of GPS and frame time stamps providing the GPS point for each frame in the video file
• (fGPS.mat) as above, saved for easy importation to MATLAB

This is the Geological Map of New Zealand (Mainland) dataset collection produced by GNS Science. The mapping frame within which the features have been observed is typically defined as surface geology (i.e. the bedrock and superficial deposits that are exposed at the topographic surface or would be visible if the overlying soil was removed). The data structure complies with the GeoSciML 4.1 standard, where relevant, and uses the appropriate CGI Controlled Vocabularies. This dataset collection should be cited as Heron, D.W. (custodian) 2023: Geological Map of New Zealand 1:250 000 (4th ed.) [digital data]. Lower Hutt (NZ): GNS Science. (GNS Science geological map; 1). https://doi.org/10.21420/5XTJ-5718.

The topographic frame map work TK50 is a map sheet section defined according to geographical network lines. Starting from the map sheet sections of the TK25 with a size of 10’ geographical longitude and 6’ geographical latitude, the sheet sections of the TK50 result from the aggregation of 4 TK25 sheet sections each, i.e. they have an extension of 20’ geographical longitude and 12’ geographical latitude. The sheet numbering of the TK25 follows a tabular system The first two digits indicate the row (numbered from north to south), the last two digits the column (numbered from west to east). The sheet numbering of the TK50 results from the lower left TK25 sheet number with the prefix "L" (Roman for 50).

FRUC Datasets (Forest environment dataset)

This dataset was collected as part of the work conducted by the Forestry Robotics @ University of Coimbra team (https://www.youtube.com/@forestryroboticsuc; part of the Institute of Systems and Robotics, https://www.isr.uc.pt/) within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269; http://semfire.ingeniarius.pt/) and the Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention (SafeForest, CENTRO-01-0247-FEDER-045931) research projects. Its purpose is to allow researchers in forestry robotics to have an in-depth analysis of a florests environment; obtain an a priori map for robot operations (e.g. path plannning, landscaping, etc…) and to train segmentation algorithms;

The dataset in question includes data from multiple sensors and absolute, map-referenced localization which can be used to register the sensor data to a fixed coordinate system. It was collected at the Choupal National Woods, Coimbra, Portugal (40◦13′13.3′′N;8◦26′38.1′′W). The dataset was collected during a partly clouded day in a forest environment by performing two circular loop laps amounting to a total distance of approximately 800m, with a total duration of 14 minutes and 22 seconds. The scenario is rich in features relevant to forestry robotics applications, including trees, bushes, tree trunks, etc. To better handle the multimodal nature of the acquired data, the dataset is bundled into rosbags, a file format used by the ROS (Robot Operating System) to record and play back data.

More specifically, the datasets include:

RGB Images from an Intel Realsense D435i

Aligned Depth Images from an Intel Realsense D435i

Left and Right Mono Images from a Mynt Eye s1030

Point Clouds from a Livox Mid-70 LiDAR

Unfiltered acceleration, gyroscopic and magnetic data from a Xsens MTi IMU

Unfiltered acceleration, gyroscopic data from an Intel Realsense D435i

GNSS Fix data from a Xiaomi Mi Mix 3 device

Description of files:

The dataset is contain in choupal.bag.

The rosbag_info.txt contains the information of each rosbag;

The sensor_box.urdf contains all the required transforms;

The sensor_box.stl contains the 3D model of the apparatus;

The choupal.launch publishes the sensor transforms and plays the dataset;

The localization.bag contains the final graph of poses extracted with Cartographer republished as nav_msgs/odom at 4.98Hz.

The localization_15Hz.bag contains a map-referenced localization extracted with Cartographer at a higher frequency, but the poses are interpolated. If you don't require a high frame rate, please use the localization.bag instead.

Usage:

Extract the fruc_dataset_choupal_launch.zip into a catkin workspace

Install the necessary dependencies of the package:

cd [/path/to/catkin_ws]

rosdep install --from-paths src --ignore-src -y -r

Copy the rosbags into the fruc_dataset_choupal_launch/rosbag/

Edit the fruc_dataset_choupal_launch/launch/choupal.launch file to your use case:

Change the file_path argument if the rosbags are not in the default location;

Set localization_file to the path of the desired localization bag, leave it empty to run the dataset without localization.

Compile the package and source the environment:

catkin_make [/your_catkin_workspace/]

source [/your_catkin_workspace/devel/setup.bash]

Launch the files:

roslaunch fruc_dataset_choupal_launch choupal.launch

This COVADIS data standard concerns communal map documents (CCs). This data standard provides a technical framework describing in detail how to dematerialise these town planning documents in a spatial database that can be used by a GIS tool and interoperable. This standard of data covers both the graphical plans of sectors and the information overlaying them. This standard of COVADIS data was developed on the basis of the specifications for the dematerialisation of planning documents created in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The COVADIS data standard provides definitions and a structure for organising and storing spatial data from communal maps in an infrastructure, while the CNIG specifications are used to frame the digitisation of these data. Part C ‘Data Structure’ presented in this COVADIS standard provides additional recommendations for the storage of data files. These are specific choices for the common data infrastructure of the ministries responsible for agriculture and sustainable development, which do not apply outside their context.

Georeferenced (to WGS1984) and cropped set of about 555 historic maps of Burma at a scale of 1 inch per two miles (1:126,720) covering most of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1878 and 1949, have been scanned and shared with the public as "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence.

Each of the map sheet scans was georeferenced using the Latitude-Longitude corner coordinates in Everest 1830 projection. Those map sheets were cropped, keeping only the map area - to allow a seamless mosaic without the mapframe overlapping adjacent map sheets when several map sheets are put together in a GIS. Those cropped map sheets were projected from Everest 1830 to WGS1984 (EPSG:4326) - standard GPS - projection to make them easier to use and combine with other GIS data.

Many grid cells in this dataset are covered by 2 versions of map sheets - those with hill shade and only lat-lon grid and those without hill shade and featuring a LCC map grid.

Those map sheets can be loaded directly in any GIS such as QGIS or ESRI ArcGIS.

• The mm_HI_JBv2024_epsg4326 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl.
  
  • The mm_HI_JBv2024_epsg4326_kmlLinks contains a KML file for each map sheet facilitating their easy use in Google Earth byt linking them the georeferenced map sheet file located in the mm_HI_JBv2024_epsg4326 folder.
  • The mm_historicHI_EPSG4326.gdb contains three ESRI mosaic datasets to easily load all mapsheets, only mapheets with hillshading and lat-lon grid and only "regular" mapsheets without hillshading and LCC grid into ArcGIS
• The mm_HI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.
• The mm_historicTopoHI_JBv2024 is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared via the "Old Survey Of India Maps” Community (e.g. to identify new mapsheets should new maps be released)

All georeferenced map scans are based on maps shared as part of the "Old Survey Of India Maps” via Zenodo. Links to each file can be found in the above mentined excel file and most can be also accessed through the zenodo repository below.

• https://zenodo.org/records/8040798 (128k Maps of South Asia, version 4, Published May 3, 2023)

The file naming convention is to first give the number of the 4 degree x 4 degree block followed by the letter (A to P) of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by the cardinal direction letters (NE, NW, SE, SW) to indicate the 30x30 minutes sized map position in the 1 degree block.

This Number - Letter - Cardinal direction letter designation is followed by the year of the edition, followed by the map series type either HI-hs (hillshaded) or HI-reg (regular), followed by the map sheet title/name.

The original files as shared as part of the "Old Survey Of India Maps” have been renamed to further standardize the file naming, sometimes correcting them and to make them unique in the case several editions of the same map sheet were available.

Lineage: This version (1.01, Upload 2024-08-20) has some file attributes fixed.

This layer is a subset of Global Landcover 1992- 2020 Layer. 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 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: 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 MapFive 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 TimeBy 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 ProcessingTo 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 dataThe 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.phpCitationESA. 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

This dataset represents the structural measurements recorded at or near the surface and comprises points with attributes describing the type of structure and its dip and dip direction where applicable. The dataset is part of the Geology of the Napier-Hastings urban area collection produced by GNS Science. The mapping frame within which the features have been observed is defined as surface geology (i.e. the bedrock and superficial deposits that are exposed at the topographic surface or would be visible if the overlying soil was removed). The data structure complies with the GeoSciML 4.1 standard, where relevant, and uses the appropriate CGI Controlled Vocabularies. This dataset forms part of Lee JM, Begg JG, Bland KJ. 2020. Geological map of the Napier-Hastings urban area [digital data]. Lower Hutt (NZ): GNS Science. (GNS Science geological map; 7a). For more information on data, distribution options and formats visit https://doi.org/10.21420/QCQT-G461?x=y

Additional products associated with this dataset are available from https://doi.org/10.21420/4CR3-9M83?x=y

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Color infrared, stereo pair 1:6,000 scale aerial photography for a digital orthophoto mosaic of Fort Necessity National Battlefield was acquired from an overflight on April 13, 2003 (i.e., during leaf-off conditions) by Kucera International. The photography was delivered to the National Park Service (NPS), quality checked, accepted as provided, and sent to North Carolina State University (NCSU). Upon receipt at NCSU, the air photos were counted to make sure that none were missing, scanned, and placed in the air photo archive maintained at NCSU for the NPS Northeast Region Inventory & Monitoring Program. Associated data and information provided by Kucera, and also stored in the air photo archive, include the airborne GPS/IMU files, the camera calibration certificate for the camera, and the hardcopy flight report for the photography that crosswalks the airborne GPS/IMU data to the photo frame numbers. The mosaic was produced from 41 color infrared air photos scanned at 1200 dpi with 24-bit color depth. The scanned images of the air photos were imported into ERDAS Imagine (.img) format where a photo block was created using airborne GPS and IMU data that Kucera International supplied with the aerial photography. After receiving the digital orthophoto mosaic from North Carolina State University, ecologists at the Pennsylvania Natural Heritage Program developed a formation-level vegetation map. Aerial photointerpretation was informed by viewing the diapositives through a stereoscope, viewing the digital mosaic onscreen, and overlaying the formation-level polygons onto digital topographic quad maps.

This CNIG data standard concerns communal map documents (CCs). This data standard provides a technical framework describing in detail how to dematerialise these town planning documents in a spatial database that can be used by a GIS tool and interoperable. This standard of data covers both the graphical plans of sectors and the information overlaying them. This CNIG data standard was developed on the basis of the specifications for the dematerialisation of planning documents created in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The CNIG data standard provides definitions and a structure for organising and storing spatial data from communal maps in an infrastructure, while the CNIG specifications are used to frame the digitisation of these data. The ‘Data Structure’ section presented in this CNIG standard provides additional recommendations for the storage of data files. These are specific choices for the common data infrastructure of the ministries responsible for agriculture and sustainable development, which do not apply outside their context.

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

In optical DNA mapping technologies sequence-specific intensity variations (DNA barcodes) along stretched and stained DNA molecules are produced. These “fingerprints” of the underlying DNA sequence have a resolution of the order one kilobasepairs and the stretching of the DNA molecules are performed by surface adsorption or nano-channel setups. A post-processing challenge for nano-channel based methods, due to local and global random movement of the DNA molecule during imaging, is how to align different time frames in order to produce reproducible time-averaged DNA barcodes. The current solutions to this challenge are computationally rather slow. With high-throughput applications in mind, we here introduce a parameter-free method for filtering a single time frame noisy barcode (snap-shot optical map), measured in a fraction of a second. By using only a single time frame barcode we circumvent the need for post-processing alignment. We demonstrate that our method is successful at providing filtered barcodes which are less noisy and more similar to time averaged barcodes. The method is based on the application of a low-pass filter on a single noisy barcode using the width of the Point Spread Function of the system as a unique, and known, filtering parameter. We find that after applying our method, the Pearson correlation coefficient (a real number in the range from -1 to 1) between the single time-frame barcode and the time average of the aligned kymograph increases significantly, roughly by 0.2 on average. By comparing to a database of more than 3000 theoretical plasmid barcodes we show that the capabilities to identify plasmids is improved by filtering single time-frame barcodes compared to the unfiltered analogues. Since snap-shot experiments and computational time using our method both are less than a second, this study opens up for high throughput optical DNA mapping with improved reproducibility.