AndreaBozzo/ceres-open-data-index huggingface dataset:
Geotagged Low-Altitude Aerial Imagery From Unmanned Aerial Systems Flights Over Town Neck Beach, in Sandwich, Massachusetts, on January 9, 2017

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on seven days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Tags: aerial-and-satellite-photography, aerial-imagery, aerial-photography, cmgp, coastal-and-marine-geology-program, dji-phantom-3-quadcopter, fc300x, field-activity-number-2017-005-fa, geoscientificinformation, geotagged-photos, image-collections, jpeg, location, low-altitude-aerial-imagery, massachusetts, oceans, photographs, photos, remote-sensing, sandwich, shoreline, shoreline-photographs, spring-hill-beach, town-neck-beach, u-s-geological-survey, uas, unmanned-aerial-system, usa, usgs, usgs-5a871abee4b00f54eb3a2d3a, whcmsc, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
Regional scale magnetotelluric data from the Rio Grande Rift and southern Rocky Mountains, Station rgr313

This dataset consists of 102 magnetotelluric (MT) stations collected in 2012-2014 in the Rio Grande Rift and southern Rocky Mountains. The U.S. Geological Survey acquired these data to improve regional conductivity models of the western United States. This work is in support of studies of the effect of lithospheric modification on electrical resistivity structure and tectonic evolution of the western United States.

Tags: apparent-resistivity, electromagnetic-surveying, geology-geophysics-and-geochemistry-science-center, geophysics, geoscientificinformation, gggsc, impedance, magnetic-field-earth, magnetotelluric, mineral-resources-program, mrp, mt, new-mexico, phase, regional-conductivity-models, rio-grande-rift, rocky-mountains, time-series, tipper, usgs-5ada0ed6e4b0e2c2dd28d367
Organization: Department of the Interior
GIS shapefile: Hillsborough County, Florida irrigated agricultural land-use for the 2018 growing season

This data set consists of a detailed digital map of the areal extent of fields and a summary of the  irrigated acreage for the 2018 growing season developed for Hillsborough County, Florida. Selected attribute data that include crop type, irrigation system, and primary water source were collected for each irrigated field.

Tags: boundaries, crop-type, environment, farming, florida-department-of-agriculture-and-consumer-service, groundwater, irrigation-type, landuse, landuse-classification-codes, orthoimagery, south-florida-water-management-district, southwest-florida-water-management-district, surface-water, usgs-5ae1ca77e4b0e2c2dd302892
Organization: Department of the Interior
Hydrology Rating Curves on the Lower Mississippi River Data

Data needed to generate hydrolic rating curves of areas along Lower Mississippi River.

Tags: arkansas, arkansas-city, biota, dams, discharge, helena, hickman, hydrology, kentucky, levees, lower-mississippi-river, mississippi, mississippi-river, missouri, rating-curves, st-louis, stage, usgs-5a943756e4b069906068f8b2, vicksburg
Organization: Department of the Interior
Accuracy of Rapid Crop Cover Map of Conterminous United States for 2016

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 58.03 percent, this approach shows strong potential for generating crop type maps of current year in September.

Tags: conus, crop-cover-mapping, modelling, near-real-time, usgs-5a8701cae4b00f54eb3a2b1b
Organization: Department of the Interior
Accuracy of Rapid Crop Cover Map of Conterminous United States for 2014

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 59.62 percent, this approach shows strong potential for generating crop type maps of current year in September.

Tags: conus, crop-cover-mapping, modelling, near-real-time, usgs-5a87018fe4b00f54eb3a2b14
Organization: Department of the Interior
Accuracy of Rapid Crop Cover Map of Conterminous United States for 2012

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 67.06 percent, this approach shows strong potential for generating crop type maps of current year in September.

Tags: conus, crop-cover-mapping, modelling, near-real-time, usgs-5a87012ee4b00f54eb3a2b0e
Organization: Department of the Interior
Accuracy of Rapid Crop Cover Map of Conterminous United States for 2011

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 66.31 percent, this approach shows strong potential for generating crop type maps of current year in September.

Tags: conus, crop-cover-mapping, modelling, near-real-time, usgs-5a870105e4b00f54eb3a2b0a
Organization: Department of the Interior
Beach Profile Data Collected from Madeira Beach, Florida (October 5, 2022)

This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides beach profile data collected at Madeira Beach, Florida. Data were collected on foot by a person equipped with a Global Positioning System (GPS) antenna affixed to a backpack outfitted for surveying location and elevation data (XYZ) along pre-determined transects. The horizontal position data are given in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 17 North (17N), referenced to the North American Datum of 1983 (NAD 83); the elevation data are referenced to the North American Vertical Datum of 1988 (NAVD 88), GEOID12B.

Tags: altimetry, bathymetry-and-elevation, beach, cmhrp, coastal-and-marine-hazards-and-resources-program, distributions, earth-science-gt-land-surface-gt-topography-gt-terrain-elevation, earth-science-gt-oceans-gt-coastal-processes-gt-beaches, elevation, elevation-data, florida, geology, geomorphology, geoscientificinformation, global-positioning-system-gps-observations, gps-measurement, gulf-of-mexico, madeira-beach, marine-geology, oceans, providers-gt-government-agencies-u-s-federal-agencies-gt-doi-gt-usgs-gt-doi-usgs-cmg-gt-coastal, spcmsc, st-petersburg-coastal-and-marine-science-center, topographic-mapping, topography, u-s-geological-survey, usgs, usgs-5a90e6cd-ac3d-4c02-a2d7-b9ead26161b3
Organization: Department of the Interior
Accuracy of Rapid Crop Cover Map of Conterminous United States for 2009

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 64.28 percent, this approach shows strong potential for generating crop type maps of current year in September.

Tags: conus, crop-cover-mapping, modelling, near-real-time, usgs-5a8700afe4b00f54eb3a2b06
Organization: Department of the Interior
Accuracy of Rapid Crop Cover Map of Conterminous United States for 2008

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively.With overall mapping accuracy for the map reaching 69.88 percent, this approach shows strong potential for generating crop type maps of current year in September.

Tags: conus, crop-cover-mapping, modelling, near-real-time, usgs-5a87005fe4b00f54eb3a2b02
Organization: Department of the Interior
Regional scale magnetotelluric data from the Rio Grande Rift and southern Rocky Mountains, Station rgr315

This dataset consists of 102 magnetotelluric (MT) stations collected in 2012-2014 in the Rio Grande Rift and southern Rocky Mountains. The U.S. Geological Survey acquired these data to improve regional conductivity models of the western United States. This work is in support of studies of the effect of lithospheric modification on electrical resistivity structure and tectonic evolution of the western United States.

Tags: apparent-resistivity, electromagnetic-surveying, geology-geophysics-and-geochemistry-science-center, geophysics, geoscientificinformation, gggsc, impedance, magnetic-field-earth, magnetotelluric, mineral-resources-program, mrp, mt, new-mexico, phase, regional-conductivity-models, rio-grande-rift, rocky-mountains, time-series, tipper, usgs-5ada0ed7e4b0e2c2dd28d36c
Organization: Department of the Interior
Up-looking acoustic Doppler current profiler data in the Chicago Sanitary and Ship Canal near Lemont, Illinois, January 2014 to January 2018

A critical component of the Lake Michigan Diversion Accounting (LMDA) program, which oversees the diversion of Great Lakes water by the State of Illinois, is the U.S. Geological Survey (USGS) streamflow gaging station on the Chicago Sanitary and Ship Canal (CSSC) near Lemont, Illinois (05536890). The long-term application of an up-looking acoustic Doppler current profiler (ADCP) at this gaging station allows the flows at this site to be examined from a new perspective; one that is not possible with the horizontally-oriented instruments typically used at the site. This data release includes continuous 1-minute and 15-minute time-averaged velocity data from an up-looking ADCP deployed on the bed of the CSSC approximately 170 feet upstream of the streamgage (Latitude: 41.691151 degrees North, Longitude: -87.963081 degrees West; World Geodetic System 1984). The 1,200 kHz Teledyne RDI Rio Grande ADCP was mounted in a custom-built aluminum mount and fixed (bolted) to the bedrock bottom of the canal by divers on June 18, 2013. A direct connection between the gage house and the instrument was possible using a 300 foot (ft) armored power and communication cable. The trawl-resistant mount was designed to shed debris and places the ADCP transducers 2.0 ft above the bed of the canal. The bed elevation at the deployment location at the time of deployment was measured to be 550.61 ft above the North American Vertical Datum of 1988 (NAVD88). The ADCP was configured with thirty 0.98 ft bins and a 0.82 ft blanking distance. The center of the first bin was located 4.3 ft above the channel bed. The instrument was configured to use water mode 12 with 100 subpings per 1-minute ensemble and a time between subpings of 0.55 seconds. Therefore, each 1-minute ensemble represents an average over approximately 57 seconds of each minute. This configuration produces 1-minute time-averaged velocity profiles with an estimated standard deviation of horizontal velocity of approximately 0.04 feet per second. The 1-minute time-averaged data are used by the Campbell Scientific CR1000 data logger to compute the 15-minute time-averaged data. Time stamps (in Central Standard Time) are recorded at the end of the 1-minute and 15-minute averaging intervals. Data from the instrument including streamwise, transverse, and vertical velocity components, return signal strength indicator (RSSI), correlation, pitch, roll, water temperature at the transducer head, and battery voltage are included in the datasets.

Tags: acoustic-doppler-current-profiling, chicago-sanitary-and-ship-canal, dupage-county, gravity-current, illinois, lemont, surge, united-states, unsteady-flow, usgs-5a86f64ee4b00f54eb3a2aa2
Organization: Department of the Interior