This image service contains high resolution satellite imagery for selected regions throughout the Yukon. Imagery is 1m pixel resolution, or better. Imagery was supplied by the Government of Yukon, and the Canadian Department of National Defense. All the imagery in this service is licensed. If you have any questions about Yukon government satellite imagery, please contact Geomatics.Help@gov.yk.can. This service is managed by Geomatics Yukon.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

Originally produced by the Farm Security Administration, these are georeferenced aerial images from Morton County, North Dakota. Historic print images housed at the Mandan, North Dakota ARS Long-Term Agricultural Research facility were digitized, georeferenced, and processed for use in both professional and consumer level GIS applications, or in photo-editing applications. The original images were produced by the Farm Security Administration to monitor government compliance for farm land agreements. Current applications include assessing land use change over time with regard to erosion, land cover, and natural and man-made structures. Not for use in high precision applications. Resources in this dataset:Resource Title: 1938_AZY_3_89. File Name: 1938_AZY_3_89_0.zipResource Description: Contains IIQ, JPG, OVR, XML, AUX, and TIF files processed in ArcMap / ArcGIS that can be used in ArcGIS applications, or in other photo or geospatial applications. Resource Title: 1938 Mosaic Index. File Name: 1938_mosaic_index_1.zipResource Description: This is the index key for the 1938 Mandan aerial images from Morton County, ND. To find the geographic location for each uploaded 1938 image, consult this map. File titles are arranged as follows: Year_Area_Roll_Frame. The mosaic map displays Roll_Frame coordinates to correspond to these images. Contains TIF, OVR, JPG, AUX, IIQ, and XML files. Resource Title: 1938_AZY_5_113. File Name: 1938_AZY_5_113_2.zipResource Description: Contains IIQ, JPG, OVR, XML, AUX, and TIF files processed in ArcMap / ArcGIS.

The imagery posted on this site is of the Gulf Coast of Louisiana, Mississippi and Alabama after Hurricane Katrina made landfall. The regions photographed range from Grand Isle, Louisiana to Gulf Shores, Alabama. The aerial photograph missions were conducted by the NOAA Remote Sensing Division the day after Katrina made landfall, August 30 and concluded September 9. The images were acquired fro...

Aerial imagery captured in 2016 at 5cm resolution is available for download via this pdf Imagery Download Page. The area extent has been divided into 15 sections. Each section is less than 1Gb in size and consists of approximately 50-60 imagery tiles in MrSID format. Simply click on the section of interest to begin the download from ArcGIS Online. The download will require some time to complete and will vary depending on the size of the download and the download speed available.

UK coverage at 50cm to 1m resolution for various dates from the 1930s onwards including UK-wide post war surveys from 1946 to 1952, city, county and district wide databases with a variety of film and print archives also available. Small areas of France were also covered. Sources include R.A.F., U.S.A.F. and Luftwaffe. The data were acquired by the Landmap project from The GeoInformation Group's (TGG) Cities Revealed project. Created from original film where possible, sourced from several archives, this database represents the very best of RAF, Luftwaffe and USAF aerial photography flown during 1939 to 1952. Images were then mosaiced together to produce regional coverage for various areas. Close inspection of some of the images will show where the images were created using printed images as printed annotations are visible. While in other cases prints are visible on the margins of the original film shown. The Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC. When using these data please also add the following copyright statement: Cities Revealed © The GeoInformation Group yyyy

This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).

In this repo, only data from the S2 tile T30UVU are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924

    Dataset name
    S2 tiles
    ROI size
    Temporal extent


    Train

T30TXT,T30TYQ,T30TYS,T30UVU,

T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP

    1024*1024
    2018-2020


    Val
    T30TYR,T30UWU,T31TEK,T31UER
    256*256
    2016-2019

Polygon layer of 1993 aerial photo models; derived from imagery project program which determined model boundaries based on 9” x 9” contact print photo centers of adjacent aerial photo frames. Spring 1993 aerial photography was acquired by Atlantic Aerial (Magnolia River) of Huntsville, Alabama under contract to LOJIC. Photo scale 1 inch = 660 feet.

This repository contains a suite of digital elevation models (DEMs), derived from aerial or satellite imagery, covering glacier and proglacial areas on Mount Rainier between 1960 and 2017. Data are available for the Emmons, Winthrop, Nisqually, and South Tahoma Glaciers and their associated proglacial areas. These data were used in Anderson and Shean (2021) to calculate DEMs of Difference (DoDs) and assess topographic change in these proglacial settings. Aerial lidar datasets used in that analysis are available through the Washington Department of Natural Resources lidar repository (https://lidarportal.dnr.wa.gov/). The DEMs stored here have been coregistered to the 2008 Mount Rainier aerial lidar dataset. Differencing of sequential DEMs will exactly reproduce DoDs used in Anderson and Shean (2021); shapefiles defining exact areas of analysis used to generate final change volumes are also available in a separate child item of this repository. DEMs are separated by glacier/basin and year, indicated in the file name. In several instances, the Winthrop and Emmons study areas contained continuous overlapping photos and were processed together. Each zip file includes a DEM in TIF format with associated supporting files. Zip files for DEMs generated using Agisoft Photoscan (all except the 2017 DEMs) also include a processing report that summarizes imagery and ground control inputs as well as processing parameters used in their generation. Imagery source information: The majority of the aerial imagery used to derive these DEMs was collected by the U.S. Geological Survey (USGS) and later scanned and publicly archived by Nolan et al. (2017). Images are available at doi:10.18739/A21R9G. Imagery from 1951 and 1961 are available through the USGS EarthExplorer repository (https://earthexplorer.usgs.gov/). Imagery of the Emmons and Nisqually from 2005 were collected by the National Park Service and scanned from negatives for this project. Imagery of South Tahoma Glacier from 1960 were collected by the U.S. Geological Survey; print images held at Mount Rainier National Park were scanned for this project.

greyscale non-ortho-rectified vertical aerial photography covering Milwaukee County Wisconsin; captured April 1980; resolution of approx. 11 inches per pixel; source 9 inch x 9 inch 1:20,000 prints were scanned at 1,800 pixels per inch and georeferenced via third-order polynomial transformation using ortho-rectified imagery as reference

Landgate has historical aerial imagery covering a large portion of Western Australia. Aerial imagery has been captured from 1948 to the present day. This dataset provides historical aerial photography boundaries and metadata associated with each project. Note: Some projects have not yet been catalogued. For more information please visit Landgate's Photography prints and enlargements page. © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

description: This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Aerial photographs were already available for the FIIS and it was decided to use these rather than conduct a new photo acquisition project. The Army Corps of Engineers lent a set of color-infrared (CIR) imagery taken in July 1997. This photoset was scanned at a resolution of 600 dpi to be used as a reference for vegetation mapping work (Figure 3a). Because we were not able to maintain possession of the CIR photoset, we obtained print copies of an additional photoset for use in the FIIS project. This set of photos was obtained from Aerographics, Inc1 the same vendor who supplied the CIR photoset to the Army Corps of Engineers. This set was captured in true-color in April of 1997 for Fire Island, and in 1996 for the islands in the Great South Bay and the William Floyd Estate at a scale of 1:1,200. Two copies of each photo were acquired. One was sent to the FIIS headquarters in Patchogue and the other was kept at CMI. The true-color photographs were used to delineate and interpret vegetation polygons at Fire Island. Aerographics scanned a subset of these same photographs at 600 dpi to serve as a backdrop for head-up digitizing (Figure 3b). Only about half of the photoset was scanned, as there was considerable overlap area within the photos. These photos were georeferenced by collecting 10-20 control points from available USGS digital orthoquarterquads (DOQQs) for the area. Photos were georeferenced to a spatial accuracy of 5 m on the ground, determined from the root mean square error term provided by the software during georeferencing.; abstract: This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Aerial photographs were already available for the FIIS and it was decided to use these rather than conduct a new photo acquisition project. The Army Corps of Engineers lent a set of color-infrared (CIR) imagery taken in July 1997. This photoset was scanned at a resolution of 600 dpi to be used as a reference for vegetation mapping work (Figure 3a). Because we were not able to maintain possession of the CIR photoset, we obtained print copies of an additional photoset for use in the FIIS project. This set of photos was obtained from Aerographics, Inc1 the same vendor who supplied the CIR photoset to the Army Corps of Engineers. This set was captured in true-color in April of 1997 for Fire Island, and in 1996 for the islands in the Great South Bay and the William Floyd Estate at a scale of 1:1,200. Two copies of each photo were acquired. One was sent to the FIIS headquarters in Patchogue and the other was kept at CMI. The true-color photographs were used to delineate and interpret vegetation polygons at Fire Island. Aerographics scanned a subset of these same photographs at 600 dpi to serve as a backdrop for head-up digitizing (Figure 3b). Only about half of the photoset was scanned, as there was considerable overlap area within the photos. These photos were georeferenced by collecting 10-20 control points from available USGS digital orthoquarterquads (DOQQs) for the area. Photos were georeferenced to a spatial accuracy of 5 m on the ground, determined from the root mean square error term provided by the software during georeferencing.

The Ministry of Natural Resources and Forestry’s Make a Topographic Map is a mapping application that features the best available topographic data and imagery for Ontario. You can: * easily toggle between traditional map backgrounds and high-resolution imagery * choose to overlay the topographic information with the imagery * turn satellite imagery on or off * customize your map by adding your own text * print your custom map Data features include: * roads * trails * lakes * rivers * wooded areas * wetlands * provincial parks * municipal, township and other administrative boundaries You don’t need special software or licenses to use this application. Technical information Using cached imagery and topographic data, the application provides a fast, seamless display at pre-defined scales. The caches are updated annually.

Fast flood extent monitoring with SAR change detection using Google Earth Engine This dataset develops a tool for near real-time flood monitoring through a novel combining of multi-temporal and multi-source remote sensing data. We use a SAR change detection and thresholding method, and apply sensitivity analytics and thresholding calibration, using SAR-based and optical-based indices in a format that is streamlined, reproducible, and geographically agile. We leverage the massive repository of satellite imagery and planetary-scale geospatial analysis tools of GEE to devise a flood inundation extent model that is both scalable and replicable. The flood extents from the 2021 Hurricane Ida and the 2017 Hurricane Harvey were selected to test the approach. The methodology provides a fast, automatable, and geographically reliable tool for assisting decision-makers and emergency planners using near real-time multi-temporal satellite SAR data sets. GEE code was developed by Ebrahim Hamidi and reviewed by Brad G. Peter; Figures were created by Brad G. Peter. This tool accompanies a publication Hamidi et al., 2023: E. Hamidi, B. G. Peter, D. F. Muñoz, H. Moftakhari and H. Moradkhani, "Fast Flood Extent Monitoring with SAR Change Detection Using Google Earth Engine," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3240097. GEE input datasets: Methodology flowchart: Sensitivity Analysis: GEE code (muti-source and multi-temporal flood monitoring): https://code.earthengine.google.com/7f4942ab0c73503e88287ad7e9187150 The threshold sensitivity analysis is automated in the below GEE code: https://code.earthengine.google.com/a3fbfe338c69232a75cbcd0eb6bc0c8e The above scripts can be run independently. The threshold automation code identifies the optimal threshold values for use in the flood monitoring procedure. GEE code for Hurricane Harvey, east of Houston Java script: // Study Area Boundaries var bounds = /* color: #d63000 */ee.Geometry.Polygon( [[[-94.5214452285728, 30.165244882083663], [-94.5214452285728, 29.56024879238989], [-93.36650748443218, 29.56024879238989], [-93.36650748443218, 30.165244882083663]]], null, false); // [before_start,before_end,after_start,after_end,k_ndfi,k_ri,k_diff,mndwi_threshold] var params = ['2017-06-01','2017-06-15','2017-08-01','2017-09-10',1.0,0.25,0.8,0.4] // SAR Input Data var before_start = params[0] var before_end = params[1] var after_start = params[2] var after_end = params[3] var polarization = "VH" var pass_direction = "ASCENDING" // k Coeficient Values for NDFI, RI and DII SAR Indices (Flooded Pixel Thresholding; Equation 4) var k_ndfi = params[4] var k_ri = params[5] var k_diff = params[6] // MNDWI flooded pixels Threshold Criteria var mndwi_threshold = params[7] // Datasets ----------------------------------- var dem = ee.Image("USGS/3DEP/10m").select('elevation') var slope = ee.Terrain.slope(dem) var swater = ee.Image('JRC/GSW1_0/GlobalSurfaceWater').select('seasonality') var collection = ee.ImageCollection('COPERNICUS/S1_GRD') .filter(ee.Filter.eq('instrumentMode', 'IW')) .filter(ee.Filter.listContains('transmitterReceiverPolarisation', polarization)) .filter(ee.Filter.eq('orbitProperties_pass', pass_direction)) .filter(ee.Filter.eq('resolution_meters', 10)) .filterBounds(bounds) .select(polarization) var before = collection.filterDate(before_start, before_end) var after = collection.filterDate(after_start, after_end) print("before", before) print("after", after) // Generating Reference and Flood Multi-temporal SAR Data ------------------------ // Mean Before and Min After ------------------------ var mean_before = before.mean().clip(bounds) var min_after = after.min().clip(bounds) var max_after = after.max().clip(bounds) var mean_after = after.mean().clip(bounds) Map.addLayer(mean_before, {min: -29.264204107025904, max: -8.938093778644141, palette: []}, "mean_before",0) Map.addLayer(min_after, {min: -29.29334290990966, max: -11.928313976797138, palette: []}, "min_after",1) // Flood identification ------------------------ // NDFI ------------------------ var ndfi = mean_before.abs().subtract(min_after.abs()) .divide(mean_before.abs().add(min_after.abs())) var ndfi_filtered = ndfi.focal_mean({radius: 50, kernelType: 'circle', units: 'meters'}) // NDFI Normalization ----------------------- var ndfi_min = ndfi_filtered.reduceRegion({ reducer: ee.Reducer.min(), geometry: bounds, scale: 10, maxPixels: 1e13 }) var ndfi_max = ndfi_filtered.reduceRegion({ reducer: ee.Reducer.max(), geometry: bounds, scale: 10, maxPixels: 1e13 }) var ndfi_rang = ee.Number(ndfi_max.get('VH')).subtract(ee.Number(ndfi_min.get('VH'))) var ndfi_subtctMin = ndfi_filtered.subtract(ee.Number(ndfi_min.get('VH'))) var ndfi_norm = ndfi_subtctMin.divide(ndfi_rang) Map.addLayer(ndfi_norm, {min: 0.3862747346632676, max: ... Visit https://dataone.org/datasets/sha256%3A5a49b694a219afd20f5b3b730302b6d76b7acb1cc888f47d63648df8acd4d97e for complete metadata about this dataset.

EarthView dataset

  Overview

The EarthView Dataset is a comprehensive collection of multispectral earth imagery. The dataset is divided into four distinct subsets sourced from Satellogic, Sentinel-1, Sentinel-2, and NEON imagers, each providing unique data. The dataset is also available in AWS Open Data registry. And you can play and navigate Satellogic's dataset in this Colab notebook.

  Dataset Viewer

Check the EarthView Dataset Viewerand it's code for… See the full description on the dataset page: https://huggingface.co/datasets/satellogic/EarthView.

Orthophotography of the urban areas within Upper Hutt, flown in January 2021.

Imagery was captured for the ‘Upper Hutt City Council’ by AAM NZ Limited, 6 Ossian St, NAPIER, New Zealand.

Data comprises:

• Imagery Area: 38.7072 km2 • 1070 x ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:500 tile layout

• Tile layout in NZTM projection containing relevant information.

The supplied imagery is in terms of New Zealand Transverse Mercator (NZTM) map projection. Please refer to the supplied tile layout shape file for specific details, naming conventions, etc.

Imagery supplied as 7.5cm pixel resolution (0.2m GSD), 3-band (RGB) uncompressed GeoTIFF.

The final spatial accuracy is ±0.2m @ 90% confidence level.

Index tiles for this dataset are available as layer Upper Hutt 0.075m Urban Aerial Photos Index Tiles (2021)

Web App. View historic aerials in St. Louis County, Missouri from 1937 to 2024.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. The University of Georgia Center for Geospatial Research agreed to analyze existing leaf-on color infrared (CIR) aerial photography to develop photointerpretation keys. Color infrared (CIR) aerial photographs of the park at 1:12,000 scale were acquired by Aero-Metric, Inc. on May 1, 2011. These photos were scanned and converted to digital orthophotographs at 0.3-meter (1 ft) resolution before being delivered to the NPS. Digital orthophotographs and hardcopy photographs in both film transparency and paper print formats were provided to UGA-CGR. A total of 12 photos in two flight lines were required to cover Horseshoe Bend NMP.

Population estimates and 95% credible intervals for each country were derived from hierarchical combination of the best fitting jaguar occurrence and density models based on anthropogenic and environmental variables. Calculations were performed for the area of current jaguar range (Figs 1 and 6).