This data set is an inventory of aerial photographs held at BGS, Murchison House office and consists of a MS Excel spreadsheet containing 11 worksheets. Each worksheet contains information pertaining to the different sub-collections within the collection (9 worksheets of aerial photographs, one for aerial photograph scans, one for satellite imagery). Quality and coverage of metadata varies from worksheet to worksheet, depending on the size of the sub-collection, its pre-existing organisation, and the way in which the sub-collection was brought together (if it was not a complete entity when the inventory was started). Areal extent ranges from Shetland in the N (1200000) to the southern Lake District in the S (480000) and from Barra in the W (65000) to Stockton-on-Tees in the E (450000). By late 2001 all photos (except those being worked on by cuurently by staff) were catalogued in the inventory spreadsheet. By late 2003, the inventory spreadsheet had been updated with newly purchased and newly discovered photos as well as modified to include details of digital holdings and satellite imagery.

Locations found on satellite imagery.

With the launch of Landsat 7, data are no longer copyright protected and these data may be freely distributed. EOS-WEBSTER, in an effort to provide access to earth science data, has designed an interim system to make Landsat data that we have in our database available to other users. In many cases, in-house researchers have acquired these data directly from the USGS EROS Data Center (EDC) for their research projects. They have provided copies of their data to EOS-WEBSTER for distribution to a wide audience. Boreal Russian Landsat data are also being housed.

Therefore, our data holdings come from several different sources and can have a variety of different processing levels associated with them. We have attempted to document, to the best of our ability, the processing steps each Landsat scene has been through. Our data are currently served in two output formats: BSQ and ERDAS Imagine, and three different spectral types (when available): multispectral, panchromatic, and thermal. A header file is provided with each ordered image giving the specifics of the image.

Please refer to the references to learn more about Landsat and the data this satellite acquires. We hope to add more data as it becomes available to EOS-WEBSTER. If you have any Landsat data, which you are willing to share, EOS-WEBSTER would like to provide access to it to a broad audience by adding it to our database. Landsat 7 data and Landsat 5 data older than 10 years can be distributed without copyright restrictions. Please contact our User Services Personnel if you would like to distribute your Landsat data, or other earth science products, via EOS-WEBSTER's FREE data distribution mechanism.

See more detailed information regarding these data and data access privilages at "http://eos-earthdata.sr.unh.edu/" or contact the Data Center Contact above.

GeoEye's OrbView-3 satellite was among the world's first commercial satellites to provide high-resolution imagery from space. OrbView-3 collected one meter panchromatic (black and white) and four meter multispectral (color) imagery at a swath width of 8 km for both sensors. One meter imagery enables more accurate viewing and mapping of houses, automobiles and aircraft, and makes it possible to create precise digital products. Four meter multispectral imagery provides color and near infrared (NIR) information to further characterize cities, rural areas and undeveloped land from space. Imagery from the OrbView-3 satellite complements existing geographic information system (GIS) data for commercial, environmental and national security customers. OrbView-3 orbits 470 km above the Earth in a sun-synchronous polar orbit while collecting imagery of the Earth's surface at one meter resolution in the Panchromatic (black and white) mode, or at four meter resolution in the Multispectral (color) mode with a three day repeat cycle.

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center received 179,981 OrbView-3 image segments from GeoEye with no restrictions. The data were delivered in Basic Enhanced (Level 1B) radiometrically corrected format. The product files include satellite telemetry data, rational functions, post-processed Ground Sample Distance (GPS) at nadir data, and sufficient metadata for rigorous triangulation.

The data in this collection were acquired between September 2003 and March 2007, both multispectral (MS) and panchromatic (Pan) sensor.

Origin: USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL): https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.phpData Access: https://nassgeodata.gmu.edu/CropScape/The Crop Frequency Layers identify crop specific planting frequency and are based on land cover information derived from every year of available CDL data beginning with the 2008 CDL, the first year of full Continental U.S. coverage. The Cultivated Layer and Crop Frequency Data Layers with accompanying metadata detailing the methodology are available for download at /Research_and_Science/Cropland/Release/.From the CDL Metadata:How has the methodology used to create the CDL changed over the program's history?The classification process used to create older CDLs (prior to 2006) was based on a maximum likelihood classifier approach using in-house software. The pre-2006 CDL's relied primarily on satellite imagery from the Landsat TM/ETM satellites which had a 16-day revisit. The in-house software limited the use of only two scenes per classification area. The only source of ground truth was the NASS June Area Survey (JAS). The JAS data is collected by field enumerators so it is quite accurate but is limited in coverage due to the cost and time constraints of such a massive annual field survey. It was also very labor intensive to digitize and label all of the collected JAS field data for use in the classification process. Non-agricultural land cover was based on image analyst interpretations.Starting in 2006, NASS began utilizing a new satellite sensor, new commercial off-the-shelf software, more extensive training/validation data. The in-house software was phased out in favor of a commercial software suite, which includes Erdas Imagine, ESRI ArcGIS, and Rulequest See5. This improved processing efficiency and, more importantly, allowed for unlimited satellite imagery and ancillary dataset inputs. The new source of agricultural training and validation data became the USDA Farm Service Agency (FSA) Common Land Unit (CLU) Program data which was much more extensive in coverage than the JAS and was in a GIS-ready format. NASS also began using the most current USGS National Land Cover Dataset (NLCD) dataset to train over the non-agricultural domain. The new classification method uses a decision tree classifier.NASS continues to strive for CDL processing improvements, including our handling of the FSA CLU pre-processing and the searching out and inclusion of additional agricultural training and validation data from other State, Federal, and private industry sources. New satellite sensors are incorporated as they become available. Currently, the CDL Program uses the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 A and B sensors. Imagery is downloaded daily throughout the growing season with the objective of obtaining at least one cloud-free usable image every two weeks throughout the growing season.Please refer to (FAQ Section 4, Question 4) on this FAQs webpage to learn more about how the handling of grass and pasture related categories has evolved over the history of the CDL Program.Extensive metadata records are available by state and year at the following webpage: (/Research_and_Science/Cropland/metadata/meta.php).

GeoJunxion uses a combination of methods to make this service very fast and efficient. The map service comes with on-demand tile rendering, often with smart-tiling, and custom styling. With smart tiling, all populated areas are pre-rendered to provide super-fast response to map requests.

KEY FEATURES

• 3 databases: GeoJunxion Maps, OSM Maps, Aerial/Satellite Imagery. • 4 custom map styles: GeoJunxion MapStyle, OSM Generic/Default, OSM Bright, OSM Bright with house numbers • Map tiles are delivered following the Slippy Maps convention.

TYPICAL USE CASES

The OSM Map Tile Server will help to display business locations on a map within a company website, it will also show moving objects on a map within a track & trace application. And furthermore it will also Provide an overview to a company’s assets on a map, as well as include geospatial analysis results within a GIS solution

BENEFITS

OSM Map Tile Server enables you to view online maps within websites or alternatively to view those maps hosted on premise through GIS software

DELIVERY FORMATS API

COVERAGE GeoJunxion, OSM: World Aerial/Satellite Imagery: The Netherlands, Flanders (Belgium)

The GeoJunxion Tile Server is the easiest way to receive map tiles to use within your own organization, application and with your preferred map viewer. The GeoJunxion Tile Server installation is Quick & Easy.

Security: On your own server or in the cloud Smart: Intelligent Map Tiling Quick & Easy: Seamless set-up of map tiles Legal: GeoJuxnion as an European contract party Helpdesk: Support from GeoJunxion with SLA LBS: Additional APIs available

On your own server or in the cloud: With the GeoJunxion Tile Server you can host your own map tiles in your own secure environment. You control your own data and connections. Alternatively, GeoJunxion can host the map tiles in the cloud for you.

OSM for Professional use: GeoJunxion offers enhanced services on top of OpenStreetMap for Professional use. The GeoJunxion Tile Server is part of the OSM for Professionals product portfolio: GeoJunxion will your contract party GeoJunxion can offer support on OSM services based on an agreed SLAControlled QA/QC reports on OpenStreetMap

Slippy Map

The provided map tiles can be used in a modern slippy map web map application which let you zoom and pan around. With a slippy map, basically, the map slips around when you drag the mouse. More info regarding this kind of map, can be found here: https://wiki.openstreetmap.org/wiki/Slippy_Map. Slippy Map - OpenStreetMap Wiki

Classification of ground-truthed locations: 85 locations were visited after census because the satellite image-sourced locations showed a potentially missed house.

Solar power plants of ten megawatts or more are shown atop a map of solar energy potential (ten megawatts can power about 10,000 houses). Many plants are in planning or construction phases; tap the buttons above to view them.Solar potential indicates the average annual amount of available energy. In much of the Southwest, solar panels covering a football field can potentially power more than 1,000 homes. This is roughly double the amount solar energy available in the Northeast.Solar power costs are high relative to fossil fuels because the sun's energy is diffuse and it varies with the seasons and weather conditions. Despite the relatively low solar potential on the eastern seaboard, a cluster of plants in the Northeast serves cities with high demand.Zoom into the map for satellite views. Some facilities are not visible due to recent construction and varying dates of imagery.Sources: Solar Energy Industries Association, Institute for Energy Research, National Renewable Energy Laboratory.This map was created with the Text and Legend template. See here for details.

The Digital Bedrock Geologic-GIS Map of Yucca House National Monument and Vicinity, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (yuho_bedrock_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (yuho_bedrock_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (yuho_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (yuho_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (yuho_bedrock_geology_metadata_faq.pdf). Please read the yuho_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: National Park Service. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (yuho_bedrock_geology_metadata.txt or yuho_bedrock_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Digital Geologic-GIS Map of Appomattox Court House National Historical Park and Vicinity, Virginia is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (apco_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (apco_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (apco_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (apco_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (apco_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (apco_geology_metadata_faq.pdf). Please read the apco_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Virginia Department of Mines, Minerals and Energy, Division of Mineral Resources. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (apco_geology_metadata.txt or apco_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:500,000 and United States National Map Accuracy Standards features are within (horizontally) 254 meters or 833.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Locations found in the HDSS census.

The various post-fire data products available on the Burn Severity Portal are produced using satellite imagery. The timing of the satellite imagery used, relative to the fire event, typically depends on the vegetation type and structure where the fire occurred. Each mapping program produces a suite of data products based on user intended user needs. You can find additional details in each of the available areas.

The Digital Geologic-GIS Map of a Portion of Yucca House National Monument, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (yuho_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (yuho_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (yuho_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (yuho_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (yuho_geology_metadata_faq.pdf). Please read the yuho_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (yuho_geology_metadata.txt or yuho_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:12,000 and United States National Map Accuracy Standards features are within (horizontally) 10.2 meters or 33.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The data labeling market is experiencing robust growth, projected to reach $3.84 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 28.13% from 2025 to 2033. This expansion is fueled by the increasing demand for high-quality training data across various sectors, including healthcare, automotive, and finance, which heavily rely on machine learning and artificial intelligence (AI). The surge in AI adoption, particularly in areas like autonomous vehicles, medical image analysis, and fraud detection, necessitates vast quantities of accurately labeled data. The market is segmented by sourcing type (in-house vs. outsourced), data type (text, image, audio), labeling method (manual, automatic, semi-supervised), and end-user industry. Outsourcing is expected to dominate the sourcing segment due to cost-effectiveness and access to specialized expertise. Similarly, image data labeling is likely to hold a significant share, given the visual nature of many AI applications. The shift towards automation and semi-supervised techniques aims to improve efficiency and reduce labeling costs, though manual labeling will remain crucial for tasks requiring high accuracy and nuanced understanding. Geographical distribution shows strong potential across North America and Europe, with Asia-Pacific emerging as a key growth region driven by increasing technological advancements and digital transformation. Competition in the data labeling market is intense, with a mix of established players like Amazon Mechanical Turk and Appen, alongside emerging specialized companies. The market's future trajectory will likely be shaped by advancements in automation technologies, the development of more efficient labeling techniques, and the increasing need for specialized data labeling services catering to niche applications. Companies are focusing on improving the accuracy and speed of data labeling through innovations in AI-powered tools and techniques. Furthermore, the rise of synthetic data generation offers a promising avenue for supplementing real-world data, potentially addressing data scarcity challenges and reducing labeling costs in certain applications. This will, however, require careful attention to ensure that the synthetic data generated is representative of real-world data to maintain model accuracy. This comprehensive report provides an in-depth analysis of the global data labeling market, offering invaluable insights for businesses, investors, and researchers. The study period covers 2019-2033, with 2025 as the base and estimated year, and a forecast period of 2025-2033. We delve into market size, segmentation, growth drivers, challenges, and emerging trends, examining the impact of technological advancements and regulatory changes on this rapidly evolving sector. The market is projected to reach multi-billion dollar valuations by 2033, fueled by the increasing demand for high-quality data to train sophisticated machine learning models. Recent developments include: September 2024: The National Geospatial-Intelligence Agency (NGA) is poised to invest heavily in artificial intelligence, earmarking up to USD 700 million for data labeling services over the next five years. This initiative aims to enhance NGA's machine-learning capabilities, particularly in analyzing satellite imagery and other geospatial data. The agency has opted for a multi-vendor indefinite-delivery/indefinite-quantity (IDIQ) contract, emphasizing the importance of annotating raw data be it images or videos—to render it understandable for machine learning models. For instance, when dealing with satellite imagery, the focus could be on labeling distinct entities such as buildings, roads, or patches of vegetation.October 2023: Refuel.ai unveiled a new platform, Refuel Cloud, and a specialized large language model (LLM) for data labeling. Refuel Cloud harnesses advanced LLMs, including its proprietary model, to automate data cleaning, labeling, and enrichment at scale, catering to diverse industry use cases. Recognizing that clean data underpins modern AI and data-centric software, Refuel Cloud addresses the historical challenge of human labor bottlenecks in data production. With Refuel Cloud, enterprises can swiftly generate the expansive, precise datasets they require in mere minutes, a task that traditionally spanned weeks.. Key drivers for this market are: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Potential restraints include: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Notable trends are: Healthcare is Expected to Witness Remarkable Growth.

This map illustrates satellite-detected areas of IDP shelters in the UN House compound in Juba, Central Equatoria, South Sudan, as seen by WorldView-3 satellite on 25 September 2015. Satellite imagery analysis indicates that the Protection of Civilians (PoCs) areas occupy 89 hectares, and as of 25 September 2015 they contained a total of 8,214 shelters and 239 infrastructure and support buildings. Also, as seen in inset 2 and 3 of PoC 2 from 22 August 2015 and 25 September 2015 all shelters have been removed and relocated as part of reorganization efforts in the area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets, namely: 1) The WSF2015 v1 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively); https://doi.org/10.1038/s41597-020-00580-5 2) The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries; https://arxiv.org/pdf/1712.05839.pdf 3) The novel WSF2019 v1 derived at 10m spatial resolution by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (of which ~ 1.2M and ~1.8M scenes have been processed, respectively); https://doi.org/10.1553/giscience2021_01_s33 The WSF2015 v1 demonstrated to be highly accurate, outperforming all similar existing global layers; however, the use of Landsat imagery prevented a proper detection of very small structures, mostly due to their reduced scale. Based on an extensive qualitative assessment, wherever available the HRSL layer shows instead a systematic underestimation of larger settlements, whereas it proves particularly effective in identifying smaller clusters of buildings down to single houses, thanks to the employment of 2016 VHR imagery. The WSF2015v v2 has been then generated by: i) merging the WSF2015 v1 and HRSL (after resampling to 10m resolution and disregarding the population density information attached); and ii) masking the outcome by means of the WSF2019 product, which exhibits even higher detail and accuracy, also thanks to the use of Sentinel-2 data and the proper employment of state-of-the-art ancillary datasets (which allowed, for instance, to effectively mask out all roads globally from motorways to residential).

This map illustrates satellite-detected areas of IDP shelters in the UN House compound in Juba, Central Equatoria, South Sudan. UNITAR-UNOSAT analysis of WorldView-1 satellite imagery acquired 27 June 2016 revealed a total of 8,477 shelters as well as 231 infrastructure and support buildings within the compound. This represents an increase of approximately 3.2 percent in shelters and a decrease of roughly 3.3 percent in infrastructure and support buildings since the previous UNITAR-UNOSAT analysis of 25 September 2015 satellite imagery. While no structures were detected within PoC2, containers were visible in this area on 27 June 2016, as seen in inset 2. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

The Unpublished Digital Surficial Geologic-GIS Map of Tuzigoot National Monument, Arizona is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (clar_surficial_geology.gdb), a 10.1 ArcMap (.MXD) map document (clar_surficial_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (moca_tuzi_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (moca_tuzi_geology_gis_readme.pdf). Please read the moca_tuzi_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Arizona Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (clar_surficial_geology_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/tuzi/clar_surficial_geology_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 12N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Tuzigoot National Monument.

This dataset has 1-day (daily) averages of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create a gridded rainfall time series for trend analysis and seasonal drought monitoring. Since 1999, USGS and CHC scientists (supported by funding from USAID, NASA, and NOAA) have developed techniques for producing rainfall maps, especially in areas where surface data is sparse. Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. See https://www.nature.com/articles/sdata201566 . See the FAQ at https://wiki.chc.ucsb.edu/CHIRPS_FAQ .

UNOSAT code: CE20231007PSE This map illustrates a satellite imagery-based comprehensive assessment of damage and destruction to structures within the area of interest in the Gaza Strip, Occupied Palestinian Territory, based on images collected on 6 July 2024 when compared to images collected on 1 May 2023, 10 May 2023, 18 September 2023, 15 October 2023, 7 November 2023, 26 November 2023, 6-7 January 2024, 29 February 2024, 31 March - 1 April 2024, and 3 May 2024. According to satellite imagery analysis, UNOSAT identified 46,223 destroyed structures, 18,478 severely damaged structures, 55,954 moderately damaged structures, and 35,754 possibly damaged structures for a total of 156,409 structures. These correspond to around 63% of the total structures in the Gaza Strip and a total of 215,137 estimated damaged housing units. The governorates of North Gaza and Rafah have experienced the highest rise in damage compared to the 3 May 2024 analysis, with around 2,300 new structures damaged in North Gaza and around 15,030 in Rafah. Within Rafah, Rafah City municipality had the highest number of newly damaged structures, totalling almost 10,100. This is a preliminary analysis and has not yet been validated in the field.