Data are available for download at http://arcticdata.io/data/10.18739/A2KW57K57 Permafrost can be indirectly detected via remote sensing techniques through the presence of ice-wedge polygons, which are a ubiquitous ground surface feature in tundra regions. Ice-wedge polygons form through repeated annual cracking of the ground during cold winter days. In spring, the cracks fill in with snowmelt water, creating ice wedges, which are connected across the landscape in an underground network and that can grow to several meters depth and width. The growing ice wedges push the soil upwards, forming ridges that bound low-centered ice-wedge polygons. If the top of the ice wedge melts, the ground subsides and the ridges become troughs and the ice-wedge polygons become high-centered. Here, a Convolutional Neural Network is used to map the boundaries of individual ice-wedge polygons based on high-resolution commercial satellite imagery obtained from the Polar Geospatial Center. This satellite imagery used for the detection of ice-wedge polygons represent years between 2001 and 2021, so this dataset represents ice-wedge polygons mapped from different years. This dataset does not include a time series (i.e. same area mapped more than once). The shapefiles are masked, reprojected, and processed into GeoPackages with calculated attributes for each ice-wedge polygon such as circumference and width. The GeoPackages are then rasterized with new calculated attributes for ice-wedge polygon coverage such a coverage density. This release represents the region classified as “high ice” by Brown et al. 1997. The dataset is available to explore on the Permafrost Discovery Gateway (PDG), an online platform that aims to make big geospatial permafrost data accessible to enable knowledge-generation by researchers and the public. The PDG project creates various pan-Arctic data products down to the sub-meter and monthly resolution. Access the PDG Imagery Viewer here: https://arcticdata.io/catalog/portals/permafrost Data limitations in use: This data is part of an initial release of the pan-Arctic data product for ice-wedge polygons, and it is expected that there are constraints on its accuracy and completeness. Users are encouraged to provide feedback regarding how they use this data and issues they encounter during post-processing. Please reach out to the dataset contact or a member of the PDG team via support@arcticdata.io.

The EEA coastline dataset is created for detailed analysis with a Minimum Mapping Unit of e.g. 1:100000, for geographical Europe. The coastline is a hybrid product obtained from satellite imagery from two projects: 1) EUHYDRO (Pan-European hydrographic and drainage database) [https://land.copernicus.eu/pan-european/satellite-derived-products/eu-hydro/view] and 2) GSHHG (A Global Self-consistent, Hierarchical, High-resolution Geography Database) [http://www.soest.hawaii.edu/pwessel/gshhg/]. The defining criteria was altitude level = 0 from EUDEM [https://land.copernicus.eu/pan-european/satellite-derived-products/eu-dem/view]. Outside the coverage of the EUDEM, the coastline from GSHHG was used without modifications. A few manual amendments to the dataset were necessary to meet requirements from EU Nature Directives, Water Framework Directive and Marine Strategy Framework Directive. In 2015, several corrections were made in the Kalogeroi Islands (coordinates 38.169, 25.287) and two other Greek little islets (coordinates 36.766264, 23.604318), as well as in the peninsula of Porkkala (around coordinates 59.99, 24.42). In this revision (v3, 2017), 2 big lagoons have been removed from Baltic region, because, according to HELCOM, are freshwater lagoons. This dataset is a polygon usable as a water-land mask.

Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

These polygon features represent digitization of the glacier margins for the 37 named glaciers of Glacier National Park (GNP) and two glaciers on U.S. Forest Service’s Flathead National Forest land, derived from 2015 satellite imagery. The polygons represent only the main body portion of each glacier as it appeared in 2015 satellite imagery. Disconnected patches are not included as this dataset represents only the main body features of the named glaciers in GNP and environs. Polygons were digitized from WorldView imagery acquired on the following source dates: 20150822, 20150912, 20150915, 20150925 (World View 01 satellite). Initial digitization was completed by Melissa Brett, PSU graduate student. This set of polygons represents revisions based on supplemental imagery (20140825, 20141019, 20160915 - WorldView-01, oblique images in USGS collection, GoogleEarth collection), and local knowledge and interpretation by Dan Fagre and Lisa McKeon (USGS) in February - August, 2016. A Wac ...

A set of Keyhole Markup Language (KML) polygons were generated to describe regional-level ecological differences in plant and animal communities within California Deserts. All polygons were acquired and generated with Google Earth satellite.

Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 271 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

These polygon features represent digitization of the glacier margins for the 37 named glaciers of Glacier National Park (GNP) and two glaciers on U.S. Forest Service’s Flathead National Forest land, derived from 2015 satellite imagery. The polygons represent only the main body portion of each glacier as it appeared in 2015 satellite imagery. Disconnected patches are not included as this dataset represents only the main body features of the named glaciers in GNP and environs. Polygons were digitized from WorldView imagery acquired on the following source dates: 20150822, 20150912, 20150915, 20150925 (World View 01 satellite). Initial digitization was completed by Melissa Brett, PSU graduate student. This set of polygons represents revisions based on supplemental imagery (20140825, 20141019, 20160915 - WorldView-01, oblique images in USGS collection, GoogleEarth collection), and local knowledge and interpretation by Dan Fagre and Lisa McKeon (USGS) in February - August, 2016. A Wacom Pro digital tablet was used by USGS staff to trace outlines and make revisions to the PSU margins. Glaciers were digitized at 1:2000 scale, with lowest off-nadir image chosen when multiple WorldView images were available for the same day. File attributes list specific photos used in analysis, including documentation of the off-nadir angle to determine imagery used. Since multiple images in time series contribute to this analysis, if previous image showed perennial snow that was absent from the glacier (bedrock visible), then that portion was deemed "seasonal/perennial snow" in subsequent photos and not included in the digitization of 2015 glacier margins.

Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 280 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

The Satellite Components Segmentation Dataset caters to the manufacturing sector, particularly in aerospace and satellite production, featuring internet-collected images with resolutions ranging from 960 x 720 to 1537 x 1018 pixels. This dataset is aimed at semantic segmentation and polygon annotations, covering a wide array of satellite components such as sailboards, antennas, nozzles, and more, to support precision manufacturing and assembly processes.

Version 2 of the dataset https://zenodo.org/records/10566644

Changes from first version include:

added crowdsourced verification labels to the dataset gathered from the interactive app (link below) explained here: https://www.nina.no/Om-NINA/Aktuelt/Nyheter/article/kartlegg-nedbygging-av-natur-selv

added the year of change crowdsourced labels

added the type of built-up expansion as labelled by the NRK team

Data can be viewed interactively here: https://nina.earthengine.app/view/nedbygging

(see Norwegian description below)

1. Dataset Information

   • Title: Map of built-up expansion over Norway 2017-2022

   • Author(s): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)

   • Contact Information: zander.venter@nina.no

   • Date of Data Generation: 06.01.2024

   • Version: 1

   • Description: This is the dataset used in the NRK article published on 06.01.2024. The data contains polygons outlining potential “nedbygging” (hereafter translated to “built-up expansion” in English) events between 2017 and 2022 over Norway. The built-up expansion polygons were identified using a combination of Sentinel-2 satellite imagery, a fully convolutional neural network (a type of AI model) from Google called Dynamic World and NINA’s time series analysis thereof. The method to create the map will be published by NINA at a later date. The original map was created by NINA, but NRK performed some post-processing which included joining some polygons which were part of the same built-up expansion event (e.g. a long road). It is important to note that the map is a result of AI and has errors in it. Therefore, users are encouraged to read the sections on data quality and usage information below. Users can refer to Venter et al. (2024) for details on the scientific best practice which the NRK journalists followed to ensure that their reported area estimates in the article were not biased. In summary, the map is wrong 18% of the time. Users should expect to find that on average 1 in 5 square meter is incorrectly identified as built-up expansion. There are also many instances of built-up expansion which will be missed in the map such as forestry road development, building of small cabins etc.

2. File Details

   • Format: Shapefile (.shp, .shx, .dbf, .prj)

   • Size: 13.27 MB

3. Geospatial Information

   • Coordinate System: EPSG:32632, UTM zone 32N

   • Spatial Resolution: 10m

   • Geographical Coverage: Norway mainland (excludes Svalbard)

   • Temporal Coverage: 2017 to 2022

4. Data Content

   • Attributes Included:

     • id: unique identity number for each polygon

     • undersøkt: whether the polygon has been investigated manually using visual interpretation of orthophotos. “ja” = “yes” and “nei” = “no”

     • undersøkt_source: whether the data was collected by the NRK team or the crowdsourcing effort

     • kategori_1: the type of built-up expansion labelled by the NRK team - see Google Translate for translations

     • year: the year in which the built-up expansion occurred as defined by the crowdsourcing volunteers

     • ai_feil: whether the AI model method correctly (“riktig”) or incorrectly (“feil”) identified natural habitat conversion to built-up surface. Values where undersøkt == “nei” are labelled as “ikke_verifisert”

5. Data Quality

   • Accuracy: As described above, the false positive rate of the map was 18% based on 500 locations used for map validation and accuracy assessment. We did not quantify a false negative rate and balanced accuracy estimates because this would have required a denser sample for manual verification. Therefore, it is likely that there are many instances of built-up expansion that our map does not capture. After the formal accuracy assessment using the 500 stratified random points, NRK verified additional polygons (total of 3875) in the dataset during their investigative journalism workflow. Although these were not collected in a systematic manner, then can still be useful for some downstream tasks such as exploring what causes the AI model to misidentify built-up expansion.

   • Validation Methods: A design-based approach was used to quantify map accuracy and estimate uncertainty around the resulting area estimate reported in the NRK article. The details of this method are reported in Venter et al. (2024). This approach quantifies the error in the AI-derived map, and corrects for this using a stratified area estimator. Therefore, the total built-up expansion of 208 km<2> reported in the NRK article has been bias-corrected. We also quantified 95% confidence intervals around this are estimate of 9.8 km<2>. It is important to note that the validation approach was conducted on individual Sentinel-2 pixels of 10x10m and not at the polygon level. Therefore, we did not quantify the error in the precision of the polygon shape in terms of capturing the full extent of a given built-up expansion event.

6. Usage Information

   • Use Limitations: Considering the map error described above, users should proceed with caution when analysing the map to derive area statistics or overlays with other maps. As described in Venter et al. (2024), simply adding the areas of the polygons (or “pixel counting” with maps formatted as images) without accounting for the error in the map will lead to incorrect area statistics. We recommend that users validate the map for their municipality or study area before proceeding with analysis. It is likely that the margin of error is highly variable between municipalities. For example, although we have not quantified it, we noticed many AI mistakes in mountainous regions due to snow and ice interference and therefore high-altitude municipalities might have more errors than low-altitude ones.

   Norwegian description:

7. Datasettinformasjon

   • Tittel: Kart over nedbygging over Norge 2017-2022

   • Forfatter(e): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)

   • Kontaktinformasjon: zander.venter@nina.no

   • Dato for datagenerering: 06.01.2024

   • Versjon: 1

   • Beskrivelse: Dette er datasettet som brukes i NRK-artikkelen publisert 06.01.2024. Dataene inneholder polygoner som skisserer potensiell nedbygging mellom 2017 og 2022 over Norge. Nedbyggingsområdene ble identifisert ved hjelp av en kombinasjon av Sentinel-2 satellittbilder, et fullstendig konvolusjonelt nevralt nettverk (en type KI-modell) fra Google kalt Dynamic World og NINAs tidsserie-analyse av dette. Metoden for å lage kartet vil bli publisert av NINA på et senere tidspunkt. Det originale kartet ble laget av NINA, men NRK utførte en del etterbehandling som inkluderte sammenføyning av noen polygoner som var en del av den samme oppbygde utvidelseshendelsen (f.eks. en lang vei). Det er viktig å merke seg at kartet er produsert ved hjelp av kunstig intelligens og inneholder feil. Derfor oppfordres brukere til å lese avsnittene om datakvalitet og bruksinformasjon nedenfor. Brukere kan referere til Venter et al. (2024) for detaljer om den vitenskapelige beste praksisen som NRK-journalistene fulgte for å sikre at deres rapporterte arealstatistikk i artikkelen er korrekt. Oppsummert er 18 % av arealet i kartet feil. Brukere bør forvente å finne at i gjennomsnitt 1 av 5 kvadratmeter er feilaktig identifisert som nedbygging. Det er også mange tilfeller av nedbygging som som ikke vil vises i kartet, som skogsveiutbygging, bygging av småhytter mm.

8. Fildetaljer

   • Format: Shapefil (.shp, .shx, .dbf, .prj)

   • Størrelse: 13,27 MB

9. Geospatial informasjon

   • Koordinatsystem: EPSG:32632, UTM-sone 32N

   • Rolig oppløsning: 10m

   • Geografisk dekning: Norges fastland (ekskluderer Svalbard)

   • Tidlig dekning: 2017 til 2022

10. Datainnhold

    • Attributter inkludert:

      • id: unikt identitetsnummer for hver polygon

      • undersøkt: om polygonet er undersøkt manuelt ved bruk av visuell tolkning av ortofoto.

      • undersøkt_source: om dataene er samlet inn av NRK-teamet eller crowdsourcing-innsatsen

      • kategori_1: typen nedbygging merket av NRK-teamet

      • year: året hvor nedbygging skjedde som definert av crowdsourcing

      • ai_feil: om AI-modellmetoden var “riktig” eller “feil”. Verdier der undersøkt == «nei» er merket som «ikke_verifisert»

11. Datakvalitet

    • Nøyaktighet: Som beskrevet ovenfor var andelen falske positive punkter i kartet 18 % basert på 500 steder (prøveflater) brukt for kartvalidering og nøyaktighetsvurdering. Vi kvantifiserte ikke andelen falske negative punkter og balanserte nøyaktighetsestimater, fordi dette ville ha krevd en tettere stikkprøvedensitet for manuell verifisering. Derfor er det sannsynlig at det er mange tilfeller av nedbygging som kartet vårt ikke fanger opp. Etter den formelle nøyaktighetsvurderingen ved bruk av 500 stratifiserte tilfeldige prøveflater, verifiserte NRK ytterligere polygoner (totalt 3875) i datasettet i løpet av deres journalistiske undersøkelser. Selv om disse ikke ble samlet inn på en systematisk måte, kan de fortsatt være nyttige for noen oppfølgingsanalyser som å utforske hva som får AI-modellen til å feilidentifisere nedbygging.

    • Valideringsmetoder: En designbasert tilnærming («design-based area estimation» på engelsk) ble brukt for å kvantifisere kartnøyaktighet og estimere usikkerhet rundt det resulterende arealestimatet rapportert i NRK-artikkelen. Detaljene ved denne metoden er forklart i Venter et al. (2024). Denne tilnærmingen kvantifiserer feilen i det KI-avledede kartet, og korrigerer for dette ved å bruke en stratifisert arealestimator. Derfor er den totale bebygde utvidelsen på 208 km<2> som er rapportert i NRK-artikkelen, skjevhetskorrigert. Vi kvantifiserte også

If you use the dataset/source code/pre-trained models in your research, please cite our work. The preprint is available at this link.

  🛰️ CSDS: AI-Based Construction Site Detection and Segmentation Tool for Satellite Images

The Construction Site Detection and Segmentation(CSDS) is a large-scale dataset of construction site satellite imagery with detailed polygon annotations.It contains both the raw source data (images and XML annotations) and preprocessed training-ready splits in… See the full description on the dataset page: https://huggingface.co/datasets/issai/CSDS_dataset.

These are distinguished from community gardens in that they are generally not intended for the public to use the space for their own growing activities, and in that many have a commercial focus. These were drawn by the Office of Planning based on ESRI satellite basemap imagery compared against the Urban Agriculture points layer. Note that, because many locations are small (or indoors) and could not be located through this satellite view, and because acreage as calculated by these polygons differs, sometimes significantly, from producers' self-reported acreage (indicating the presence of other, less visible growing space, or out-of-date satellite imagery), this layer should not be considered complete and should be used for internal purposes only.

Groundwater is the water that soaks into the ground from rain and can be stored beneath the ground. Groundwater floods occur when the water stored beneath the ground rises above the land surface. The Historic Groundwater Flood Map shows the observed peak flood extents caused by groundwater in Ireland. This map was made using satellite images (Copernicus Programme Sentinel-1), field data, aerial photos, as well as flood records from the past. Most of the data was collected during the flood events of winter 2015 / 2016, as in most areas this data showed the largest floods on record.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. Vector data portray the world using points, lines, and polygons (area). The floods are shown as polygons. Each polygon has info about the type of flood, the data source, and the area of the flood.The flood extents were calculated using data and techniques with various precision levels, and as such, it may not show the true historic peak flood extents.The Winter 2015/2016 Surface Water Flooding map shows fluvial (rivers) and pluvial (rain) floods, excluding urban areas, during the winter 2015/2016 flood event, and was developed as a by-product of the historic groundwater flood map.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info about the type of flood, the data source, and the area of the flood.The flood extents were made using remote sensing images (Copernicus Programme Sentinel-1), which covered any site in Ireland every 4-6 days. As such, it may not show the true peak flood extents.The Synthetic Aperture Radar (SAR) Seasonal Flood Maps shows observed peak flood extents which took place between Autumn 2015 and Summer 2021. The maps were made using Synthetic Aperture Radar (SAR) images from the Copernicus Programme Sentinel-1 satellites. SAR systems emit radar pulses and record the return signal at the satellite. Flat surfaces such as water return a low signal. Based on this low signal, SAR imagery can be classified into non-flooded and flooded (i.e. flat) pixels.Flood extents were created using Python 2.7 algorithms developed by Geological Survey Ireland. They were refined using a series of post processing filters. Please read the lineage for more information.The flood maps shows flood extents which have been observed to occur. A lack of flooding in any part of the map only implies that a flood was not observed. It does not imply that a flood cannot occur in that location at present or in the future.This flood extent are to the scale 1:20,000. This means they should be viewed at that scale. When printed at that scale 1cm on the maps relates to a distance of 200m.They are vector datasets. Vector data portray the world using points, lines, and polygons (areas). The flood extents are shown as polygons. Each polygon has information on the confidence of the flood extent (high, medium or low), a flood id and a unique id.The Groundwater Flooding High Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 10%, which correspond with a return period of every 10 years. The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.The Groundwater Flooding Medium Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 1%, which correspond with a return period of every 100 years. The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.The Groundwater Flooding Low Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 0.1%, which correspond with a return period of every 1000 years.The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. Vector data portray the world using points, lines, and polygons (area). The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.

We created HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), a deep learning-based, high-performance computing-enabled mapping pipeline to automatically detect buildings and roads from high-resolution Maxar satellite imagery in Arctic communities. The code is made available at https://github.com/PermafrostDiscoveryGateway/HABITAT. The pipeline is based on a ResNet50-UNet++ semantic segmentation architecture trained on a training dataset comprised of building and road footprint polygons manually digitized from Maxar satellite imagery across the circumpolar Arctic (including Alaska, Russia, and Canada). From imagery of 285 Alaskan communities acquired between 2018-2023, we detected approximately 250,000 buildings and storage tanks (comprising a 41.76 million square meter footprint) and 15 million meters of road. Building (including storage tanks) footprint polygons and road centerlines were strictly mapped within the boundaries of Alaskan communities (both incorporated places and census designated places). After the deep learning model detected building and road footprints, post-processing was performed to smooth out building footprints, extract centerlines from road footprints, and remove falsely-detected infrastructure. In particular, a buffer is created around developed land cover identified by the 2016 Alaska National Land Cover Database map, and model predictions that fall outside of the buffer are assumed to be confused with non-infrastructure land cover.

The Satellite Image Catalogue provides information on satellite imagery used in the Australian Antarctic Program. It includes satellite imagery from IKONOS, Landsat, Quickbird, and SPOT satellites and Russian cameras such as KATE-200, KFA-1000 and MK-4 (from the Resurs-F1 and Resurs-F2 platforms). Not all images that are currently held by the Australian Antarctic Data Centre are available for viewing in the Satellite Image Catalogue, but will be added whenever possible. As additional images are acquired, they will also be added.

The images cover the area of the Australian Antarctic Territory.

Previews of the images are included where possible.

The complete list of satellites/sensors for which the catalogue holds data are as follows:

Earth-Observing 1 Hyperion IKONOS IKONOS Landsat ETM+ TM MSS NOAA AVHRR Quickbird Quickbird RADARSAT SAR Resurs MK-4 KFA-1000 KATE-200 SPOT HRG HRV Terra ASTER MODIS RADARSAT
AVHRR

The GIS database contains the data of aufeis (naleds) in the Indigirka River basin (Russia) from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects). Historical data collection is created based on the Cadastre of aufeis (naleds) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 896 aufeis with a total area 2063.6 km² within the studied basin. Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2017. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season (e.g. between May, 15 and June, 18), to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 1213, with their total area about 1287 km². The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

This vector dataset contains information about individual building footprints covering all countries of the European Union (EU27). This is the result of conflating the building footprint polygons available in three datasets, and in the following order of priority: OpenStreetMap, Microsoft GlobalML Building Footprints and European Settlement Map.

Results indicate how DBSM R2023 compares robustly agains cadastral data from Estonia, used as reference area.

The comparison with GHS-BUILT-S, reveals a relative overestimation of the latter, factored by 0.68 at the EU scale for a sound match. While this dataset only contains the polygon of the building footprint, the aim is to continue to add relevant attributes from the point of view of energy efficiency and energy consumption in building in future versions.

This layer contains a change analysis from 1973 to 2001 based on analysis of satellite imagery. A NALC image from 1973 with 60-m resolution was classified using unsupervised classification into 100 classes. These classes were subsequently recoded into 5 classes (Woody, Herbaceous, Bare, Marsh and Water) based on comparisions with maps and aerial photos. The same procedure was followed for a 2001 ETM+ image that had been resampled to 15-m resolution. The recoded layers were converted to vector shapefiles and intersected to create this data layer. Subsequently, codes were added to recode the polygons into and to 3 classes (upland, marsh, water) and the area and perimeter of each polygon was calculated. Layer was later renamed (in 2013) from "vbi1970_2001c5_Intersect_N83" to "VBI_LUC_1973_2001_NAD83" to avoid temporal confusion and remove ESRI auto-naming appendage. FGDC Metadata: Identification Information: Citation: Citation information: Originators: John H. Porter Title: Change data layer for the Virginia Coast Reserve, 1973-2001 - VCR05133 *File or table name: vbi1970_2001c5_Intersect_N83 Publication date: 12/22/2005 *Geospatial data presentation form: vector digital data *Online linkage: \MAP1\d\jhp7e\vbi1970_2001c5_Intersect_N83.shp Description: Abstract: This layer contains a change analysis from 1973 to 2001 based on analysis of satellite imagery. A NALC image from 1973 with 60-m resolution was classified using unsupervised classification into 100 classes. These classes were subsequently recoded into 5 classes (Woody, Herbaceous, Bare, Marsh and Water) based on comparisions with maps and aerial photos. The same procedure was followed for a 2001 ETM+ image that had been resampled to 15-m resolution. The recoded layers were converted to vector shapefiles and intersected to create this data layer. Subsequently, codes were added to recode the polygons into and to 3 classes (upland, marsh, water) and the area and perimeter of each polygon was calculated. Purpose: To detect changes on the coast of Virginia. *Language of dataset: en Time period of content: Time period information: Multiple dates/times: Single date/time: Calendar date: 08/12/1973 Single date/time: Calendar date: 08/27/2001 Currentness reference: ground condition Status: Progress: Complete Maintenance and update frequency: None planned Spatial domain: Bounding coordinates: *West bounding coordinate: -76.112114 *East bounding coordinate: -75.135130 *North bounding coordinate: 38.237583 *South bounding coordinate: 37.046598 Local bounding coordinates: *Left bounding coordinate: 402666.874551 *Right bounding coordinate: 487984.802095 *Top bounding coordinate: 4232184.738430 *Bottom bounding coordinate: 4100601.786647 Minimum altitude: -30 Maximum altitude: 30 Altitude units: m Keywords: Theme: Theme keywords: Change analysis Theme keyword thesaurus: None Place: Place keywords: Delmarva Peninsula Place keyword thesaurus: None Access constraints: VCR/LTER Data License required Use constraints: Bona fide scientific research. This is not a legal document Point of contact: Contact information: Contact person primary: Contact person: John Porter Contact organization: Virginia Coast Reserve Long-Term Ecological Research, University of Virginia Contact address: Address type: mailing and physical address Address: 291 McCormick Road Address: PO Box 400123 City: Charlottesville State or province: VA Postal code: 22904-4123 Country: USA Contact voice telephone: 434-924-8999 Contact facsimile telephone: 434-982-2137 Contact electronic mail address: jhp7e@virginia.edu Data set credit: John H. Porter, Virginia Coast Reserve Long-Term Ecological Research, University of Viriginia, Charlottesville, VA 22904 USA *Native dataset format: Shapefile *Native data set environment: Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.0.0.535 Cross reference: Citation information: Title: VCR05113 - Change analysis of the Virginia Coast 1973-2001 Back to Top -------------------------------------------------------------------------------- Data Quality Information: Positional accuracy: Horizontal positional accuracy: Horizontal positional accuracy report: 60-m pixels were used for the 1973 image. Quantitative horizontal positional accuracy assessment: Horizontal positional accuracy value: 60 Horizontal positional accuracy explanation: 60-m pixels were used for the 1973 image. Lineage: Process step: Process description: Dataset copied. Back to Top -------------------------------------------------------------------------------- Spatial Data Organization Information: *Direct spatial reference method: Vector Point and vector object information: SDTS terms description: *Name: vbi1970_2001c5_Intersect_N83 *SDTS point and vector object type: G-polygon *P