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6 results for “orthoimagery”
High-resolution orthoimagery of the Altamaha River estuary in November 2017 and October 2018
We acquired two high-resolution (0.15-m per pixel) color digital images of the salinity gradient of the Altamaha River estuary in November 2017 (1 month after Hurricane Irma) and October 2018. Flights ranged from the mouth of the estuary all the way up to the tidal fresh forest habitat. The 1:1200 scale orthoimagery is comprised of 4-bands (Red, Green, Blue, Near Infrarad (RGBNIR)) collected with a Ground Sample Distance (GSD) of 0.5 foot using a Leica ADS100 airborne digital sensor. Individual tiles were georectified using ground control points and merged into a domain-wide mosaic.
Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.
<h2>Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.</h2><h4>Daniel Buscombe, Marda Science LLC</h4><p>There are two datasets in this data release:</p><p>1. <strong>Model training dataset</strong>. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments. </p><p>2. <strong>Model output dataset</strong>. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments. </p><p>All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (<i>Ritchie et al., 2018</i>). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following <i>Over et al., 2021</i>) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR). </p><p>The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):</p><p>1. vegetation / other (0)</p><p>2. water (1)</p><p>3. sediment (2)</p><p>4. large wood (3)</p><h3>1. Model training dataset.</h3><p>Imagery was labeled using a combination of the open-source software Doodler (<i>Buscombe et al., 2021</i>; <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler</a>) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (<i>Buscombe and Goldstein, 2022</i>). </p><p>Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.</p><p>Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.</p><h4>Files:</h4><p>1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs</p><p>2. Model_<i>training_</i> images1of4.zip, 1.5 GB, imagery tiffs</p><p>3. Model_<i>training_</i> images2of4.zip, 1.5 GB, imagery tiffs</p><p>4. Model_<i>training_</i> images3of4.zip, 1.7 GB, imagery tiffs</p><p>5. Model_<i>training_</i> images4of4.zip, 1.6 GB, imagery tiffs</p><h3>2. Model output dataset.</h3><p>Imagery was labeled using a deep-learning based semantic segmentation model (<i>Buscombe, 2023</i>) trained specifically for the task using the Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) modeling suite. We use the software package Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) to fine-tune a Segformer (<i>Xie et al., 2021</i>) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of <i>Xie et al. (2021)</i>, itself fine-tuned on ADE20k dataset (<i>Zhou et al., 2019</i>) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.</p><p>The spatial extent of the imagery in the MR is [455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678] (NAD83(2011) / UTM zone 10N). Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is [457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395] (NAD83(2011) / UTM zone 10N). Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.</p><p>Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts. </p><h4>Files:</h4><p>1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)</p><p>2. Elwha<i>MR_ imagery_ part1_ of</i>_<i> </i>2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha<i>MR_ imagery_ part2_ of</i>_<i> </i>2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)</p><p>4. ElwhaL<i>R_ imagery_ part1_ of</i>_<i> </i>2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>5. ElwhaL<i>R_ imagery_ part2_ of</i>_<i> </i>2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)<br> </p><p>This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (<a href="https://github.com/dbuscombe-usgs">https://github.com/dbuscombe-usgs</a>) and Evan Goldstein (<a href="https://github.com/ebgoldstein">https://github.com/ebgoldstein</a>). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (<a href="https://github.com/2320sharon">https://github.com/2320sharon</a>) and Jaycee Favela for contributing labels. </p><h3>References</h3><p>• Buscombe, D. (2023). <strong>Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0)</strong> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8172858">https://doi.org/10.5281/zenodo.8172858</a></p><p>• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021).<strong> Human-in-the-loop segmentation of Earth surface imagery</strong>. Earth and Space Science, 9, e2021EA002085. <a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a></p><p>• Buscombe, D., & Goldstein, E. B. (2022). <strong>A reproducible and reusable pipeline for segmentation of geoscientific imagery.</strong> Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p><p>• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, <strong>Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation</strong>: U.S. Geological Survey Open-File Report 2021–1039, 46 p., <a href="https://doi.org/10.3133/ofr20211039">https://doi.org/10.3133/ofr20211039</a>.</p><p>• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, <strong>Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals</strong>: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/F7PG1QWC">https://doi.org/10.5066/F7PG1QWC</a>.</p><p>• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. <strong>SegFormer: Simple and efficient design for semantic segmentation with transformers</strong>. Advances in Neural Information Processing Systems, 34, pp.12077-12090.</p><p>• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. <strong>Semantic understanding of scenes through the ade20k dataset</strong>. International Journal of Computer Vision, 127, pp.302-321.</p><p><br> </p>
Digital orthoimagery for fractional vegetation cover at site Alpha from Morgan et al Kuiseb River study
<p>Contains high-resolution digital orthoimagery (RGB and NIR) and derived products for site Alpha, from the following manuscript:</p> <p>Morgan et al. (in prep), Spatiotemporal analysis of vegetation cover change in a large ephemeral river: multi-sensor fusion of unmanned aerial vehicle (UAV) and Landsat imagery.</p>
National Agriculture Imagery Program (NAIP) Orthoimagery from 2005, Niwot Ridge LTER Project Area, Colorado
This data set contains imagery from the National AgriculturalImagery Program (NAIP). NAIP acquires digital ortho imageryduring the agricultural growing seasons in the continental U.S..A primary goal of the NAIP program is to enable availabilty ofortho imagery within a year of acquisition. NAIP provides twomain products: 1 meter ground sample distance (GSD) orthoimagery rectified to a horizontal accuracy of within +/- 5meters of reference digital ortho quarter quads (DOQQS) fromthe National Digital Ortho Program (NDOP); and, 2 meter GSDortho imagery rectified to within +/- 10 meters of referenceDOQQs. The tiling format of NAIP imagery is based on a 3.75'x 3.75' quarter quadrangle with a 360 meter buffer on all foursides. NAIP quarter quads are rectified to the UTM coordinatesystem NAD83. NAIP imagery can obtain as much as 10% cloudcover per tile. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
Landsat Orthoimagery Mosaic from 1999, Niwot Ridge LTER Project Area, Colorado
An orthoimage is remotely-sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The Landsat Mosaic orthoimagery database contains Landsat Thematic Mapper imagery for the conterminous United States. The more than 700 Landsat scenes have been resampled to a 1-arc-second (approximately 30-meter) sample interval in a geographic coordinate system using the North American Horizontal Datum of 1983. Three bands have been selected from the eight spectral bands available for each frame. These are bands 4 (near-infrared), 3 (red), and 2 (green), typically displayed as red, green, and blue, respectively. The image is a full-resolution (spectral and spatial), 24-bit color-infrared composite that simulates color infrared film as a "false color composite". NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
USGS High Resolution Orthoimagery
High resolution orthorectified images combine the image characteristics of an aerial photograph with the geometric qualities of a map. An orthoimage is a uniform-scale image where corrections have been made for feature displacement such as building tilt and for scale variations caused by terrain relief, sensor geometry, and camera tilt. A mathematical equation based on ground control points, sensor calibration information, and a digital elevation model is applied to each pixel to rectify the image to obtain the geometric qualities of a map. A digital orthoimage may be created from several photographs mosaicked to form the final image. The source imagery may be black-and-white, natural color, or color infrared with a pixel resolution of 1-meter or finer. With orthoimagery, the resolution refers to the distance on the ground represented by each pixel.
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