The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer.

The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities.

Basic Scene Product Components and Format Product Components
Image File (GeoTIFF format) Metadata File (XML format) Rational Polynomial Coefficients (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Accuracy <10 m RMSE

The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components
Image File (GeoTIFF format) Metadata File (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 3-band natural colour (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Projection UTM WGS84 Accuracy <10 m RMSE

PlanetScope Ortho Scene product is available in the following:

PlanetScope Visual Ortho Scene product is orthorectified and colour-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite. PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance.

As per ESA policy, very high-resolution imagery of conflict areas cannot be provided.

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

Date of Images:Dates used for landslide mapping: April 20, 2024; April 21, 2024; May 6, 2024; May 7, 2024; May 8, 2024Dates of Planet Imagery: 4/20/2023, 5/6/2024, 5/7/2024Summary:The NASA GSFC landslides team manually mapped landslide initiation points after the April 29, 2024 heavy rainfall in Rio Grande do Sul, Brasil. The landslide initiation points were derived from PlanetScope imagery. This is only a portion of the region where landslides occurred, and areas covered with clouds were not included. More landslides will be mapped in coming days.NOTE: This is a rapid response product. These landslides were manually mapped using different dates of PlanetScope imagery, but all are presumed to have been triggered by the 4/29/2024 heavy rainfall event. As clouds cleared, more landslides were identified.This PlanetScope imagery captured by Planet Labs Inc. in May 2024 shows landslides following the heavy rainfall in Brasil. True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB s produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred.Suggested Use:True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred.Satellite/Sensor:PlanetScopeResolution:3 metersCredits:NASA Disasters Program, NASA GSFC, Includes copyrighted material of Planet Labs PBC. All rights reserved.Esri REST Endpoint:Landslide Points: https://maps.disasters.nasa.gov/arcgis/home/item.html?id=a442900ab36549ffb50cd722307ffa43Planet Imagery: https://maps.disasters.nasa.gov/arcgis/home/item.html?id=c434d94758344e368d10a7dfa39beeb5Data Download:Landslide points: https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/brasil_flood_202405/landslides/

These are global maps of dwarf planet (1) Ceres described in "Resolved spectrophotometric properties of the Ceres surface from Dawn Framing Camera images" by Schröder et al., Icarus 288 (2017) 201-225 (arXiv). Global color maps created from these data files are shown in Fig. 7 of that paper.

These files contain global maps of the photometrically corrected reflectance of the Ceres surface, with one map for each of the 7 narrow-band filter of the Dawn framing camera (FC2). Photometrically corrected reflectance means observed reflectance divided by model reflectance, and the global average of the photometrically corrected reflectance is unity by definition. The model is the Akimov photometric model with parameters valid for global average Ceres and an upper limit of 80° for the photometric angles. The model reflectance was calculated with surface topography derived from a global shape model. Global maps were created as the median of projected individual images of the photometrically corrected reflectance. The IMG and FITS files contain the data in floating point format. The PNG files are provided for visual reference.

We are developing a set of NASA Extensions to the Google Maps API—and soon to other frameworks such as OpenLayers as well—that will make these platforms more useful to NASA scientists and our colleagues elsewhere.

Date of Images:5/5/2024Date of Next Image:N/ASummary:Scientists at NASA's Marshall Space Flight Center created these water extents on May 5, 20224 using PlanetScope imagery. These images can be used to see where open water is visible at the time of the satellite overpass. This product shows all water detected and differentiates between normal water areas and some flooded areas. This product was classified using WorldCover. It's important to note that all flooded areas may not be captured do to the sensors limitations of not being able to "see" through vegetation and buildings. To determine where additional flooding may have occurred, combine this layer with other data sets.Suggested Use:This product shows water that is detected by the sensor with different colors indicating different land cover/land use classifications from WorldCover that appear to have water and are potentially flooded.Blue (1): Known WaterRed (2): Flooded DevelopedGreen (3): Flooded VegetationOrange (4): Flooded Cropland/GrasslandGray (5): Clouds/Cloud Shadow(0): No DataSatellite/Sensor:PlanetScopeResolution:3 metersCredits:NASA Disasters Program, Includes copyrighted material of Planet Labs PBC. All rights reserved.Esri REST Endpoint:See URL section on the right side of page.WMS Endpoint: https://maps.disasters.nasa.gov/ags04/services/texas_flood_202405/planet_waterextents/MapServer/WMSServer

ADMMR map collection: New Planet Iron Mine Index Map; 1 in. to 500 feet; 40 x 30 in.

Final resampling generator models produced from the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) (https://github.com/xinntao/ESRGAN). ESRGAN was trained at two different resampling factors, 4x and 10x, using a training data set of global Planet CubeSat satellite images. These generators can be used to resample Planet CubeSat satellite images from 30m and 12m to 3m resolution. Descriptions and results of training can be found at https://wandb.ai/elezine/pixelsmasher. In press at Canadian Journal of Remote Sensing: Super-resolution surface water mapping on the Canadian Shield using Planet CubeSat images and a Generative Adversarial Network, Ekaterina M. D. Lezine, Ethan D. Kyzivat, and Laurence C. Smith (2021).

This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m² for 2017; 13 m² for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.

All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in Table 1. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.

Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey

For an interactive map demo of these datasets via Google Earth Engine Apps, visit: https://ekyzivat.users.earthengine.app/view/super-resolution-demo

Table 1: File naming scheme based on region, with some regions requiring two-scene mosaics.

Region	Landsat ID	Mosaic name
Yukon Flats Basin	LC08_L2SP_068014_20170708_20200903_02_T1	LC08_20170708_yflats_cog.tif
“	LC08_L2SP_068013_20170708_20201015_02_T1	“
Old Crow Flats	LC08_L2SP_067012_20170903_20200903_02_T1	-
Mackenzie River Delta	LC08_L2SP_064011_20170728_20200903_02_T1	LC08_20170728_inuvik_cog.tif
“	LC08_L2SP_064012_20170728_20200903_02_T1	“
Canadian Shield Margin	LC08_L2SP_050015_20170811_20200903_02_T1	LC08_20170811_cshield-margin_cog.tif
“	LC08_L2SP_048016_20170829_20200903_02_T1	“
Canadian Shield near Baker Creek	LC08_L2SP_046016_20170831_20200903_02_T1	-
Canadian Shield near Daring Lake	LC08_L2SP_045015_20170723_20201015_02_T1	-
Peace-Athabasca Delta	LC08_L2SP_043019_20170810_20200903_02_T1	-
Prairie Potholes North 1	LC08_L2SP_041021_20170812_20200903_02_T1	LC08_20170812_potholes-north1_cog.tif
“	LC08_L2SP_041022_20170812_20200903_02_T1	“
Prairie Potholes North 2	LC08_L2SP_038023_20170823_20200903_02_T1	-
Prairie Potholes South	LC08_L2SP_031027_20170907_20200903_02_T1	-
Fairbanks	LT05_L2SP_070014_19850831_20200918_02_T1	-

References:

Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. Remote Sensing, 11(18), 2163. https://doi.org/10.3390/rs11182163

Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1707

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. Environ. Res. Lett, 16, 35010. https://doi.org/10.1088/1748-9326/abc848

Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1859

A polygon layer of regions of multiple planetary bodies beyond Earth that are covered by proposed or published planetary geologic maps published by the USGS.

NOTE: This services contains a large amount of data and may be slow to load. For best results and faster loading, zoom into an area of interest.Date of Images:10/5/2022Date of Next Image:None ExpectedSummary:This PlanetScope imagery captured by Planet Labs Inc. on October 5, 2022 shows the impacts from Hurricane Ian across Florida.The true Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred.The color infrared image is created using the near-infrared, red, and green channels from the Planet instrument allowing for the ability to see areas impacted from the hurricane. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue.Suggested Use:True Color:True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred.Color Infrared:A false color composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred.Satellite/Sensor:PlanetScopeResolution:3 metersCredits:NASA Disasters Program, Includes copyrighted material of Planet Labs PBC. All rights reserved.Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/hurricane_ian_2022/planet_20221005/MapServer/WMSServer

The purpose of the lunar maps is to provide an up-to-date and comprehensive depiction on lunar nomenclature approved by the International Astronomical Union (IAU).

2016 ESA Living Planet Symposium Abstract Submission - Mapping the Morphology of the Intertidal Zone using the time-series of Landsat data

Appendices include the original survey, response data, and collated results related to the Open File Report. Geoscience maps, regardless of target body, are spatial and temporal representations of materials and processes recorded on planetary surfaces (Varnes, 1973; Spencer, 2000). The information and context provided by these maps promote basic and applied research within and across various geoscience disciplines. They also provide an important basis for programmatic and policy decisions (for example, H.R. 2763 – 102nd Congress, National Geologic Mapping Act of 1992). Since 1961, planetary (that is, all solid surface bodies in the Solar System beyond Earth) geoscience maps have been used in nearly every facet of planetary exploration, from landing site characterization for human (for example, Grolier, 1970) and robotic (for example, Anderson and Bell, 2010) missions to mineralogical analyses of water-alteration on Mars (for example, Loizeau and others, 2007). Modern planetary geo ...

In response to the growing concerns about mangrove deforestation, recent studies have used various remote sensing technology like satellite imagery to measure the mangrove extent. In this work, we investigated the mangrove distribution in Northwestern Madagascar by using fine spatial imagery with a pixel size as small as 3m and compared it with the result of the traditional method based on relatively coarser Landsat data. Mangroves are an essential biodiverse ecosystem found along tropical and subtropical intertidal beaches, providing critical goods and services to coastal communities, and supporting diverse organisms. However, anthropogenic activities have caused the loss of mangroves in Madagascar, necessitating a new mapping approach utilizing the fine spatial resolution map from Planet data to create a map with advanced detail. The quantitative result central to this work is the new multi-date map of the Tsimipaika- Ampasindava-Ambaro Bays (TAB) from 2020 to 2022, which provides advanced detail and direct comparison with the shift in local mangrove species. The classification maps are based on Random Forest and Maximum Likelihood algorithms, and all of them have an overall accuracy of over 85%. The dynamics of mangrove forests from 2020 to 2022 are quantified, with a 12.6% loss in closed-canopy mangroves, and a 24.1% loss in open-canopy mangroves I am overestimated. Limitations regarding the classification model are also found in this study, including the overestimation of open canopy mangroves caused by the shadow and the seamline in the base map. This result shows the potential of using fine-resolution satellite imagery in supervised land cover classification, and the corresponding challenges raised by the smaller pixel size.

ProtoInnovations, LLC and Carnegie Mellon University have formed a partnership to commercially develop localization and mapping technologies for planetary rovers. Our first aim is to provide a reliable means of localization that is independent of infrastructure, such as GPS, and compatible with requirements of missions to planetary surfaces. Simultaneously solving for the precise location of the rover as it moves while building an accurate map of the environment is an optimization problem involving internal sensing, sensing of the surrounding environment, probabilistic optimization methods, efficient data structures, and a robust implementation. Our second aim is to merge simultaneous localization and mapping (SLAM) technologies with our existing Reliable Autonomous Surface Mobility (RASM) architecture for rover navigation. Our unique partnership brings together state-of-the-art technologies for SLAM with experience in delivering and supporting both autonomous systems and mobility platforms for NASA.

Our proposed project will create a SLAM framework that is capable of accurately localizing a rover throughout long, multi-kilometer traverses of barren terrain. Our approach is compatible with limited communication and computing resources expected for missions to planetary surfaces. Our technology is based on innovative representations of evidence grids, particle-filter algorithms that operate on range data rather than explicit features, and strategies for segmenting large evidence grids into manageable pieces.

In this project we will evaluate the maturity of these algorithms, developed for research programs at Carnegie Mellon, and incorporate them into our RASM architecture, thus providing portable and reliable localization for a variety of vehicle platforms and sensors. Mission constraints will vary broadly, so our SLAM components will be able to merge readings from various suites of sensors that may be found on planetary rovers.

This is the data catalog for the paper:

Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits

The catalog can be automatically retrieved from here using the Python package p4tools

Dust in debris disks is produced by colliding or evaporating planetesimals, the remnant of the planet formation process. Warm dust disks, known by their emission at <24mic, are rare (4% of FGK mainsequence stars), and specially interesting because they trace material in the region likely to host terrestrial planets, where the dust has very short dynamicallifetimes. Dust in this region comes from very recent asteroidal collisions, migrating Kuiper Belt planetesimals, or migrating dust.NASAs Kepler mission has just released a list of 1235 candidate transiting planets, and in parallel, the WideField Infrared Survey Explorer (WISE) has just completed a sensitive allsky mapping in the 3.4, 4.6, 12, and 22 micron bands. By crossidentifying the WISE sources with Kepler candidates as well as with other transiting planetary systems we have identified 21 transiting planet hosts with previously unknown warm debris disks. We propose Herschel/PACS 100 and 160 micron photometry of this sample, to determine whether the warm dust in these systems represents stochastic outbursts of local dust production, or simply the Wien side of emission from a cold outer dust belt. These data will allow us to put constraints in the dust temperature and infrared luminosity of these systems, allowing them to be understood in the context of other debris disks and disk evolution theory. This program represents a unique opportunity to exploit the synergy between three great space facilities: Herschel, Kepler, and WISE. The transiting planet sample hosts will remain among the most studied group of stars for the years to come, and our knowledge of their planetary architecture will remain incomplete if we do not understand the characteristics of their debris disks. truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

Virtual Planet (VP) technologies, such as Google Earth, provide an innovative and dynamic measn for interacting with geographic information in new ways. This presentation explores how socio-economic indicators, derived from Canada's 2001 Census, can be viewed at multiple scales and with different styles of 3-D representation - from national aggregates dow to local community-level statistics.