This resource contain the training materials from a workshop held at the 2nd Annual Developers Conference at the University of Utah. It delves into the integration of ground-based observations with remote sensing datasets. The workshop facilitated hands-on experience in employing cloud-based technologies such as Google Earth Engine, Compute Engine, and Cloud Storage for data dissemination. Participants learned to create automated systems for data upload, processing, and dissemination, featuring the Stevens River Ice Monitoring System. This approach enhances collaboration and efficiency in environmental studies by streamlining data handling workflows.

This project aims to use remote sensing data from the Landsata database from Google Earth Engine to evaluate the spatial extent changes in the Bear Lake located between the US states of Utah and Idaho. This work is part of a term project submitted to Dr Alfonso Torres-Rua as a requirment to pass the Remote Sensing of Land Surfaces class (CEE6003). More information about the course is provided below. This project uses the geemap Python package (https://github.com/giswqs/geemap) for dealing with the google earth engine datasets. The content of this notebook can be used to:

learn how to retrive the Landsat 8 remote sensed data. The same functions and methodology can also be used to get the data of other Landsat satallites and other satallites such as Sentinel-2, Sentinel-3 and many others. However, slight changes might be required when dealing with other satallites then Landsat. Learn how to create time lapse images that visulaize changes in some parameters over time. Learn how to use supervised classification to track the changes in the spatial extent of water bodies such as Bear Lake that is located between the US states of Utah and Idaho. Learn how to use different functions and tools that are part of the geemap Python package. More information about the geemap Pyhton package can be found at https://github.com/giswqs/geemap and https://github.com/diviningwater/RS_of_Land_Surfaces_laboratory Course information:

Name: Remote Sensing of Land Surfaces class (CEE6003) Instructor: Alfonso Torres-Rua (alfonso.torres@usu.edu) School: Utah State University Semester: Spring semester 2023

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation Note: (1) Information on growing and non-growing seasons has been derived from the remote sensing product: https://lpdaac.usgs.gov/dataset_discovery/measures/measures_products_table/vipphen_ndvi_v004(2) Google provides full global coverage by images, in contrast to Bing. However, in many areas, these are Landsat-based images (from 1984 up to now). For more objective comparison with Bing imagery, we have excluded those areas from the analysis. Supplement to: Lesiv, Myroslava; See, Linda; Laso-Bayas, Juan-Carlos; Sturn, Tobias; Schepaschenko, Dmitry; Karner, Mathias; Moorthy, Inian; McCallum, Ian; Fritz, Steffen (2018): Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data. Land, 7(4), 118

The AIMS Google Earth Catalogue contains lists of KML/KMZ files, created by AIMS staff, that can be loaded into Google Earth and some other 3D programs. Maps may be used as is, or customized in Google Earth for your specific purposes.Files in the cataloque have been created for a variety of purposes such as providing high resolution imagery of islands and reefs and mapping study sites. Staff are encouraged to add their own files to the catalogue. The application contains instructions to how to add and document files to share internally. If you are familiar with RSS Feeds, Syndication or News Feeds, you might be interested in adding the RSS URL to your feed reader in your web browser or email client.
The AIMS Google Earth Catalogue is an initiative of the AIMS Data Centre to provide a facility for sharing KML/KMZ files between AIMS staff.

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the NSF/NCAR C-130 flight track in real-time during the ARISTO2015 project.

The Cropland Data Layer (CDL) is a crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. The CDL is created by the USDA, National Agricultural Statistics Service (NASS), Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section. For detailed FAQ please visit CropScape and Cropland Data Layers - FAQs. To explore details about the classification accuracies and utility of the data, see state-level omission and commission errors by crop type and year. The asset date is aligned with the calendar year of harvest. For most crops the planted and harvest year are the same. Some exceptions: winter wheat is unique, as it is planted in the prior year. A hay crop like alfalfa could have been planted years prior. For winter wheat the data also have a class called "Double Crop Winter Wheat/Soybeans". Some mid-latitude areas of the US have conditions such that a second crop (usually soybeans) can be planted immediately after the harvest of winter wheat and itself still be harvested within the same year. So for mapping winter wheat areas use both classes (use both values 24 and 26). While the CDL date is aligned with year of harvest, the map itself is more representative of what was planted. In other words, a small percentage of fields on a given year will not be harvested. Some non-agricultural categories are duplicate due to two very different epochs in methodology. The non-ag codes 63-65 and 81-88 are holdovers from the older methodology and will only appear in CDLs from 2007 and earlier. The non-ag codes from 111-195 are from the current methodology which uses the USGS NLCD as non-ag training and will only appear in CDLs 2007 and newer. 2007 was a transition year so there may be both sets of categories in the 2007 national product but will not appear within the same state. Note: The 2024 CDL only has the data band. The cultivated and confidence bands are yet to be released by the provider.

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

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Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the GV flight track in real-time during the HEFT10 project

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the GV flight track in real-time during the MPEX project.

This dataset contains Keyhole Markup Language (KML) files. KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the Gulfstream-V High-performance Instrumented Airborne Platform for Environmental Research (NSF/NCAR GV HIAPER) flight track in real-time during the CONvective TRansport of Active Species in the Tropics (CONTRAST) project.

It is derived from the Global Surface Water Explorer which maps terrestrial surface water globally using historical Landsat satellite imagery. (Pekel, J. et al., Nature 540, 418-422 (2016). (doi:10.1038/nature20584)). The data files contain zipped archives of shapefiles and CSV (comma separated values) files.

Shapefiles are one for each season (dry, wet and permanent) and scale (river basin and reach) of our analysis, and contain annual trends in surface water area. To open and explore them in a GIS software (eg. QGIS), un-ZIP them and include them as vector datasets.

CSV files are one for each scale (river basin and reach (transect)) of our analysis, and contain time series of surface water areas from 1991 to 2020. To open and explore them, for analysis or to explore in a table editing software, un-ZIP them and read them in.

Refer to 00_README.txt for details on feature and table attributes in the files.

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the NSF/NCAR HIAPER GV flight track in real-time during the ASPIRE project.

Geospatial Services Land management within the US Forest Service and on the 900,000+ acre Monongahela National Forest (NF) is driven by a wide mix of resource and societal demands that prove a challenge in fulfilling the Forest Service’s mission of “Caring for the Land and Serving the People.” Programmatically, the 2006 Land and Resource Management Plan guide natural resource management activities on lands administered by the Monongahela National Forest. The Forest Plan describes management direction and practices, resource protection methods and monitoring, desired resource conditions, and the availability and suitability of lands for resource management. Technology enables staff to address these land management issues and Forest Plan direction by using a science-based approach to facilitate effective decisions. Monongahela NF geospatial services, using enabling-technologies, incorporate key tools such as Environmental Systems Research Institute’s ArcGIS desktop suite and Trimble’s global positioning system (GPS) units to meet program and Forest needs. Geospatial Datasets The Forest has a broad set of geospatial datasets that capture geographic features across the eastern West Virginia landscape. Many of these datasets are available to the public through our download site. Selected geospatial data that encompass the Monongahela National Forest are available for download from this page. A link to the FGDC-compliant metadata is provided for each dataset. All data are in zipped format (or available from the specified source), in one of two spatial data formats, and in the following coordinate system: Coordinate System: Universal Transverse Mercator Zone: 17 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Map files – All map files are in pdf format. These maps illustrate the correlated geospatial data. All maps are under 1 MB unless otherwise noted. Metadata file – This FGDC-compliant metadata file contains information pertaining to the specific geospatial dataset. Shapefile – This downloadable zipped file is in ESRI’s shapefile format. KML file – This downloadable zipped file is in Google Earth’s KML format. Resources in this dataset:Resource Title: Monongahela National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/mnf/landmanagement/gis/?cid=stelprdb5108081 Selected geospatial data that encompass the Monongahela National Forest are available for download from this page.

The SEN12 Global Urban Mapping (SEN12_GUM) dataset consists of Sentinel-1 SAR (VV + VH band) and Sentinel-2 MSI (10 spectral bands) satellite images acquired over the same area for 96 training and validation sites and an additional 60 test sites covering unique geographies across the globe. The satellite imagery was acquired as part of the European Space Agency's Earth observation program Copernicus and was preprocessed in Google Earth Engine. Built-up area labels for the 30 training and validation sites located in the United States, Canada, and Australia were obtained from Microsoft's open-access building footprints. The other 66 training sites located outside of the United States, Canada, and Australia are unlabeled but can be used for semi-supervised learning. Labels obtained from the SpaceNet7 dataset are provided for all 60 test sites.

The Digital Geologic-GIS Map of Roosevelt-Vanderbilt National Historic Sites and Vicinity, New York 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 (rova_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 (rova_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 (rova_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 (rova_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (rova_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 (rova_geology_metadata_faq.pdf). Please read the rova_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: New York Geological Survey and New York State Department of Transportation. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (rova_geology_metadata.txt or rova_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 1:24,000 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, 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).

TerraMetrics, Inc., proposes an SBIR Phase I R/R&D program to investigate and develop a key web services architecture that provides data processing, storage and delivery capabilities and enables successful deployment, display and visual interaction of diverse, massive, multi-dimensional science datasets within popular web-based geospatial platforms like Google Earth, Google Maps, NASA's World Wind and others. The proposed innovation exploits the use of a wired and wireless, network-friendly, wavelet-compressed data format and server architecture that extracts and delivers appropriately-sized blocks of multi-resolution geospatial data to client applications on demand and in real time. The resulting format and architecture intelligently delivers client-required data from a server, or multiple distributed servers, to a wide range of networked client applications that can build a composite, user-interactive 3D visualization of fused, disparate, geospatial datasets. The proposed innovation provides a highly scalable approach to data storage and management while offering geospatial data services to client science applications and a wide range of client and connection types from broadband-connected desktop computers to wireless cell phones. TerraMetrics offers to research the feasibility of the proposed innovation and demonstrate it within the context of a live, server-supported, Google Earth-compatible client application with high-density, multi-dimensional NASA science data.

Find alternative fueling stations near an address or ZIP code or along a route in the United States. Enter a state to see a station count. ## Data Collection Methods ## The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities coordinators, an Add a Station form on the Alternative Fuels Data Center (AFDC) website, and through collaborating with infrastructure equipment and fuel providers. NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies and inclusion criteria may result in slight differences between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to maintain the data. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, and propane industry groups to establish best practices for identifying new stations in the most-timely manner possible and to develop a more rigorous network for the future. ## Station Update Schedule ## Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and dispensing the fuel specified. Based on an established data collection schedule, the database is updated once a month with the exception of electric vehicle supply equipment (EVSE) data, which are updated twice a month. Stations that are no longer operational or no longer provide alternative fuel are removed from the database on a monthly basis or as they are identified. ## Mapping and Counting Methods ## Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or charging outlets at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool like Google Earth, Google Maps, or Google StreetView. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application. ## Notes about Specific Station Types ## ### Private Stations ### Stations with an Access of "Private - Fleet customers only" may allow other entities to fuel through a business-to-business arrangement. For more information, fleet customers should refer to the information listed in the details section for that station to contact the station directly. ### Biodiesel Stations ### The Alternative Fueling Station Locator only includes stations offering biodiesel blends of 20% (B20) and above. ### Electric Vehicle Supply Equipment (EVSE) ### An electric charging station, or EVSE, appears as one point on the map, regardless of the number of charging outlets at that location. The number and type of charging outlets available are displayed as additional details when the station location is selected. Each point on the map is counted as one station in the station count. To see a total count of EVSE for all outlets available, go to the Alternative Fueling Station Counts by State table. Residential EVSE locations are not included in the Alternative Fueling Station Locator. ## Liquefied Petroleum Gas (Propane) Stations ### Because many propane stations serve customers other than drivers and fleets, NREL collaborated with the industry to effectively represent the differences. Each propane station is designated as a 'primary' or 'secondary' service type. Both types are able to fuel vehicles. However, locations with a 'primary' designation offer vehicle services and fuel priced specifically for use in vehicles. The details page for each station lists its service designation.

Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020) -

This dataset provides high-resolution, nationwide land use/land cover (LULC) and terrestrial carbon stock maps of Pakistan for four epochs: 1990, 2000, 2010, and 2020. Developed using multi-sensor satellite imagery and advanced classification techniques in Google Earth Engine (GEE), the dataset presents a comprehensive analysis of land cover changes driven by urbanization and their impacts on carbon storage capacity over 30 years.

The LULC data includes nine distinct classes, covering key land cover types such as forest cover, agriculture, rangeland, wetlands, barren lands, water bodies, built-up areas, and snow/ice. Classification was performed using a hybrid machine learning approach, and the accuracy of the land cover maps was validated using a stratified random sampling approach.

The carbon stock maps were derived using the InVEST model, which estimated carbon storage in four major carbon pools (above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter) based on the LULC maps. The results showed a significant decline in carbon storage due to rapid urban expansion, particularly in major cities like Karachi and Lahore, where substantial forest and agricultural lands were converted into urban areas. The study estimates that Pakistan lost approximately -5% of its carbon storage capacity over this period, with urban areas growing by over ~1040%.

This dataset is a valuable resource for researchers, policymakers, and environmental managers, providing crucial insights into the long-term impacts of urbanization on land cover and carbon sequestration. It is expected to support future land management strategies, urban planning, and climate change mitigation efforts. The high temporal and spatial resolution of the dataset makes it ideal for monitoring land cover dynamics and assessing ecosystem services over time.

This dataset is aslo available as Google Earth Engine application. For more details check:

> Github Project repository: https://github.com/waleedgeo/lulc_pk
> Paper DOI: https://doi.org/10.1016/j.eiar.2023.107396
> Paper PDF: https://waleedgeo.com/papers/waleed2024_paklulc.pdf

If you find this work useful, please consider citing it as

Waleed, M., Sajjad, M., & Shazil, M. S. (2024). Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020). Environmental Impact Assessment Review, 105, 107396.

Contributors:
Mirza Waleed (email) (Linkedin)
Muhammad Sajjad (email) (Linkedin)
Muhammad Shareef Shazil

To check other work, please check:
My Webpage & Google Scholar

Context

KMZ file for the global list of ports. Very useful if you want to map logistics, get distances between certain ports, find the optimal port/node for shipping goods. File is great for visualization using Google Earth Pro or My Maps.

Content

Found the dataset from intensely searching for datasets I'm interested to map for my Geospatial Visualization and Geospatial Information Systems Class. Unfortunately, I couldn't find the source anymore. Found this around September 2021.

KMZ zip file with: -port id's -latitude and longitude -website

Acknowledgements

Thank you to everyone who will contribute to improve this dataset. I realize that finding files to work with for some specific programs for Geospatial Information Science can be hard, so I'm paying it forward to help beginners out there. If you find the data source you can let me know so we can credit them properly! 😄

👍 give this dataset a like, to help other people find it!

Inspiration & Things you can do with it:

🗺️ Visit all the ports using tours in Google Earth Pro 🧭 Locate ports and find ports nearby 🚢 Find Optimal paths 📍Every port has an ID and a website to find it 🌎 Create your map and visualize it 🔍 Create a categorized shipping map

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the GV flight track in real-time during thePACDEX project.